├── .DS_Store ├── MergeBench.png ├── merging ├── requirements.txt ├── merging_methods │ ├── __init__.py │ ├── merger.py │ ├── task_arithmetic.py │ ├── TIES.py │ ├── DARE.py │ ├── utils.py │ ├── consensus.py │ ├── localize_and_stitch.py │ ├── fisher_utils.py │ ├── regmean_utils.py │ ├── regmean.py │ ├── ties_merging_utils.py │ ├── fisher.py │ ├── localize_utils.py │ └── regmean_plusplus.py ├── README.md ├── main.py ├── prepare_args.py └── taskloader.py ├── merged_models ├── Llama-3.1-8B_merged │ └── RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1 │ │ ├── code_eval.json │ │ ├── safety_eval.json │ │ └── lm_eval.json └── Llama-3.2-3B_merged │ └── RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1 │ ├── code_eval.json │ ├── safety_eval.json │ └── lm_eval.json ├── scripts ├── evaluate.sh └── merge.sh └── README.md /.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/uiuctml/MergeBench/HEAD/.DS_Store -------------------------------------------------------------------------------- /MergeBench.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/uiuctml/MergeBench/HEAD/MergeBench.png -------------------------------------------------------------------------------- /merging/requirements.txt: -------------------------------------------------------------------------------- 1 | torch==2.1.2 2 | transformers 3 | datasets 4 | trl 5 | protobuf==3.20.3 6 | flash-attn==2.5.7 7 | tqdm 8 | deepspeed==0.14.5 -------------------------------------------------------------------------------- /merging/merging_methods/__init__.py: -------------------------------------------------------------------------------- 1 | from merging_methods.merger import Merger 2 | from merging_methods.task_arithmetic import TaskArithmetic 3 | from merging_methods.TIES import TIES 4 | from merging_methods.DARE import DARE 5 | from merging_methods.localize_and_stitch import LocalizeAndStitch 6 | from merging_methods.consensus import Consensus 7 | from merging_methods.regmean import RegMean 8 | from merging_methods.regmean_plusplus import RegMeanPlusPlus -------------------------------------------------------------------------------- /merging/merging_methods/merger.py: -------------------------------------------------------------------------------- 1 | import torch.nn as nn 2 | from transformers import AutoModelForCausalLM, AutoTokenizer 3 | 4 | class Merger(nn.Module): 5 | def __init__(self, base_model, ft_models, save_path): 6 | super().__init__() 7 | 8 | self.base_model_name = base_model 9 | self.base_model = AutoModelForCausalLM.from_pretrained(self.base_model_name, torch_dtype="bfloat16") 10 | self.tokenizer = AutoTokenizer.from_pretrained(self.base_model_name) 11 | self.ft_ckpts = [AutoModelForCausalLM.from_pretrained(ft_model, torch_dtype="bfloat16") for ft_model in ft_models] 12 | self.save_path = save_path 13 | 14 | def merge(self, **kwargs): 15 | pass 16 | -------------------------------------------------------------------------------- /merging/merging_methods/task_arithmetic.py: -------------------------------------------------------------------------------- 1 | from merging_methods.utils import * 2 | from merging_methods.merger import Merger 3 | 4 | class TaskArithmetic(Merger): 5 | def __init__(self, base_model, ft_models, save_path): 6 | super().__init__(base_model, ft_models, save_path) 7 | 8 | def merge(self, **kwargs): 9 | scaling_coef = kwargs['scaling_coef'] 10 | task_vectors = [get_task_vector(ft_model, self.base_model) for ft_model in self.ft_ckpts] 11 | merged_tv = scaling_coef * sum(task_vectors) 12 | merged_model = vector_to_state_dict(merged_tv, self.base_model) 13 | 14 | merged_model.save_pretrained(self.save_path) 15 | self.tokenizer.save_pretrained(self.save_path) 16 | -------------------------------------------------------------------------------- /merging/merging_methods/TIES.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from merging_methods.utils import * 3 | from merging_methods.ties_merging_utils import * 4 | from merging_methods.merger import Merger 5 | import time 6 | 7 | class TIES(Merger): 8 | def __init__(self, base_model, ft_models, save_path): 9 | super().__init__(base_model, ft_models, save_path) 10 | 11 | def merge(self, **kwargs): 12 | scaling_coef = kwargs['scaling_coef'] 13 | task_vectors = [get_task_vector(ft_model, self.base_model) for ft_model in self.ft_ckpts] 14 | 15 | start = time.time() 16 | 17 | merged_tv = scaling_coef * ties_merging(torch.stack(task_vectors), reset_thresh=kwargs['K'], merge_func=kwargs['merge_func']) 18 | merged_model = vector_to_state_dict(merged_tv, self.base_model) 19 | print("Time taken for ties: ", time.time() - start) 20 | 21 | merged_model.save_pretrained(self.save_path) 22 | self.tokenizer.save_pretrained(self.save_path) 23 | -------------------------------------------------------------------------------- /merging/README.md: -------------------------------------------------------------------------------- 1 | # Implementation of model merging methods 2 | 3 | ## Merging 4 | Each merging algorithms contain specific parameters and configurations, and all of which are prepared in `prepare_args.py`. Please refer to their original papers for more detailed definitions. We provide examples for running each merging algorithms in `scripts/merge.sh`. 5 | 6 | To install the packages required for merging, run the following commands: 7 | ``` 8 | conda create -n merging 9 | conda activate merging 10 | pip install -r requirements.txt 11 | ``` 12 | 13 | ## Adding new merging methods 14 | 1. Create a new python file under the `merging_methods` directory, e.g., `task_arithmetic.py`. 15 | 2. Within the file, define the class name for the merging algorithm `TaskArithmetic`, which inherits from the abstract method `Merger` in `merger.py`. 16 | 3. Remember to add the class in `__init__.py`, e.g., 17 | ``` 18 | from merging_methods.task_arithmetic import TaskArithmetic 19 | ``` 20 | 4. Add any method dependent hyperparameters in `prepare_args.py`, which will be passed into the class via `kwargs`. The argument parsing in `main.py` only handles generic arguments. 21 | 5. Overwrite the `merge` function to implement the details of merging, which ends with saving the merged model and tokenizer. 22 | -------------------------------------------------------------------------------- /merging/merging_methods/DARE.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from merging_methods.utils import * 3 | from merging_methods.merger import Merger 4 | import time 5 | 6 | class DARE(Merger): 7 | def __init__(self, base_model, ft_models, save_path): 8 | super().__init__(base_model, ft_models, save_path) 9 | 10 | 11 | def random_drop_and_rescale(self, task_vector, p=0.8): 12 | if not 0 <= p < 1: 13 | raise ValueError("p must be in the range [0, 1).") 14 | 15 | # Generate a binary mask: 1 with probability (1-p) and 0 with probability p. 16 | mask = torch.bernoulli(torch.full(task_vector.shape, 1 - p, device=task_vector.device)) 17 | 18 | # Apply the mask and rescale the kept values by 1/(1-p) 19 | return task_vector * mask / (1 - p) 20 | 21 | def merge(self, **kwargs): 22 | p = kwargs['p'] 23 | coeff = kwargs['scaling_coef'] 24 | 25 | task_vectors = [get_task_vector(ft_model, self.base_model) for ft_model in self.ft_ckpts] 26 | start = time.time() 27 | task_vectors = [self.random_drop_and_rescale(task_vector, p) for task_vector in task_vectors] 28 | merged_tv = sum(task_vectors) * coeff 29 | print("Time taken for random drop and rescale: ", time.time() - start) 30 | merged_model = vector_to_state_dict(merged_tv, self.base_model) 31 | 32 | merged_model.save_pretrained(self.save_path) 33 | self.tokenizer.save_pretrained(self.save_path) 34 | -------------------------------------------------------------------------------- /merging/merging_methods/utils.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | def flatten_ckpt_into_vec(ckpt): 4 | vec = [] 5 | for param in ckpt.values(): 6 | vec.append(param.flatten()) 7 | return torch.cat(vec) 8 | 9 | def select_trainable_params(model): 10 | params = {} 11 | 12 | for n, p in model.named_parameters(): 13 | if 'embed' not in n and 'Embedding' not in n: 14 | params[n] = p 15 | 16 | return params 17 | 18 | def get_task_vector(ft_model, base_model): 19 | ft_model.to('cpu') 20 | base_model.to('cpu') 21 | 22 | ft_params = select_trainable_params(ft_model) 23 | base_params = select_trainable_params(base_model) 24 | 25 | ft_vec = flatten_ckpt_into_vec(ft_params) 26 | base_vec = flatten_ckpt_into_vec(base_params) 27 | 28 | return ft_vec - base_vec 29 | 30 | def vector_to_state_dict(vec, pretrained_model, return_dict=False): 31 | i = 0 32 | vec.to('cpu') 33 | pretrained_model.to('cpu') 34 | for k, v in pretrained_model.state_dict().items(): 35 | if 'embed' not in k.lower() and 'lm_head' not in k: 36 | if torch.nonzero(v).size(0) == 0: 37 | continue 38 | vec[i:i+v.numel()].reshape(v.shape).to(pretrained_model.device) 39 | pretrained_model.state_dict()[k] += vec[i:i+v.numel()].reshape(v.shape) 40 | i += v.numel() 41 | 42 | if return_dict: 43 | return pretrained_model.state_dict() 44 | else: 45 | return pretrained_model 46 | -------------------------------------------------------------------------------- /merging/main.py: -------------------------------------------------------------------------------- 1 | from prepare_args import prepare_args, create_parser 2 | import importlib 3 | 4 | def get_ft_ckpts(base_model): 5 | model_name = base_model.split('/')[-1] 6 | task_names = ['instruction', 'math', 'coding', 'safety', 'multilingual'] 7 | return [f'MergeBench/{model_name}_{task_name}' for task_name in task_names] 8 | 9 | def parse_args(): 10 | parser = create_parser() 11 | 12 | parser.add_argument('--base-model', default='meta-llama/Llama-3.2-3B', type=str) 13 | parser.add_argument('--algo', default='TaskArithmetic', type=str, choices=['TaskArithmetic', 'TIES', 'DARE', 'LocalizeAndStitch', 'Consensus', 'RegMean', 'RegMeanPlusPlus', 'Fisher']) 14 | parser.add_argument('--save-path', default='./merged_models/', type=str) 15 | 16 | return parser.parse_args() 17 | 18 | def main(args): 19 | kwargs = prepare_args(args) 20 | merger_module = importlib.import_module("merging_methods") 21 | ft_ckpts = get_ft_ckpts(args.base_model) 22 | 23 | kwargs_str = "_".join(f"{key}_{value}" for key, value in kwargs.items() if key not in ['fisher_only','merge_only','save_group','task_names','keep_checkpoints']) 24 | if args.save_group: 25 | task_group = args.save_group 26 | elif args.task_names: 27 | task_group = args.task_names 28 | else: 29 | task_group = None 30 | 31 | save_path = args.save_path + args.base_model.split('/')[1] + '_merged/' + args.algo 32 | if task_group: 33 | save_path += '_task_names_' + task_group 34 | if kwargs_str != '': 35 | save_path += '_' + kwargs_str 36 | 37 | print('merged model save to:',save_path) 38 | merger = getattr(merger_module, args.algo)(args.base_model, ft_ckpts, save_path) 39 | print(args) 40 | print(kwargs) 41 | merger.merge(**kwargs) 42 | 43 | if __name__ == "__main__": 44 | args = parse_args() 45 | main(args) -------------------------------------------------------------------------------- /merged_models/Llama-3.1-8B_merged/RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1/code_eval.json: -------------------------------------------------------------------------------- 1 | { 2 | "humanevalplus": { 3 | "pass@1": 0.47317073170731705, 4 | "pass@10": 0.6524390243902439 5 | }, 6 | "mbppplus": { 7 | "pass@1": 0.5732804232804233, 8 | "pass@10": 0.656084656084656 9 | }, 10 | "config": { 11 | "prefix": "", 12 | "do_sample": true, 13 | "temperature": 0.2, 14 | "top_k": 0, 15 | "top_p": 0.95, 16 | "n_samples": 10, 17 | "eos": "<|endoftext|>", 18 | "seed": 0, 19 | "model": "merged_models/Llama-3.1-8B_merged/RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1", 20 | "modeltype": "causal", 21 | "peft_model": null, 22 | "revision": null, 23 | "use_auth_token": true, 24 | "trust_remote_code": false, 25 | "tasks": "humanevalplus,mbppplus", 26 | "instruction_tokens": null, 27 | "batch_size": 10, 28 | "max_length_generation": 512, 29 | "precision": "bf16", 30 | "load_in_8bit": false, 31 | "load_in_4bit": false, 32 | "left_padding": false, 33 | "limit": null, 34 | "limit_start": 0, 35 | "save_every_k_tasks": -1, 36 | "postprocess": true, 37 | "allow_code_execution": true, 38 | "generation_only": false, 39 | "load_generations_path": null, 40 | "load_data_path": null, 41 | "metric_output_path": "merged_models/Llama-3.1-8B_merged/RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1/results/code_eval.json", 42 | "save_generations": false, 43 | "load_generations_intermediate_paths": null, 44 | "save_generations_path": "generations.json", 45 | "save_references": false, 46 | "save_references_path": "references.json", 47 | "prompt": "prompt", 48 | "max_memory_per_gpu": null, 49 | "check_references": false 50 | } 51 | } -------------------------------------------------------------------------------- /merged_models/Llama-3.2-3B_merged/RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1/code_eval.json: -------------------------------------------------------------------------------- 1 | { 2 | "humanevalplus": { 3 | "pass@1": 0.31036585365853653, 4 | "pass@10": 0.4634146341463415 5 | }, 6 | "mbppplus": { 7 | "pass@1": 0.4304232804232804, 8 | "pass@10": 0.5582010582010583 9 | }, 10 | "config": { 11 | "prefix": "", 12 | "do_sample": true, 13 | "temperature": 0.2, 14 | "top_k": 0, 15 | "top_p": 0.95, 16 | "n_samples": 10, 17 | "eos": "<|endoftext|>", 18 | "seed": 0, 19 | "model": "merged_models/Llama-3.2-3B_merged/RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1", 20 | "modeltype": "causal", 21 | "peft_model": null, 22 | "revision": null, 23 | "use_auth_token": true, 24 | "trust_remote_code": false, 25 | "tasks": "humanevalplus,mbppplus", 26 | "instruction_tokens": null, 27 | "batch_size": 10, 28 | "max_length_generation": 512, 29 | "precision": "bf16", 30 | "load_in_8bit": false, 31 | "load_in_4bit": false, 32 | "left_padding": false, 33 | "limit": null, 34 | "limit_start": 0, 35 | "save_every_k_tasks": -1, 36 | "postprocess": true, 37 | "allow_code_execution": true, 38 | "generation_only": false, 39 | "load_generations_path": null, 40 | "load_data_path": null, 41 | "metric_output_path": "merged_models/Llama-3.2-3B_merged/RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1/results/code_eval.json", 42 | "save_generations": false, 43 | "load_generations_intermediate_paths": null, 44 | "save_generations_path": "generations.json", 45 | "save_references": false, 46 | "save_references_path": "references.json", 47 | "prompt": "prompt", 48 | "max_memory_per_gpu": null, 49 | "check_references": false 50 | } 51 | } -------------------------------------------------------------------------------- /scripts/evaluate.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | MODEL=$1 4 | GPU_ID=$2 5 | OUTPUT_PATH=$3 6 | 7 | echo $MODEL 8 | echo $GPU_ID 9 | echo $OUTPUT_PATH 10 | 11 | export CUDA_VISIBLE_DEVICES=$GPU_ID 12 | 13 | source $(conda info --base)/etc/profile.d/conda.sh 14 | mkdir -p $OUTPUT_PATH 15 | 16 | conda activate lmeval 17 | 18 | lm_eval --model hf \ 19 | --model_args pretrained=$MODEL \ 20 | --tasks gsm8k_cot \ 21 | --device cuda:$GPU_ID \ 22 | --batch_size 16 \ 23 | --output_path $OUTPUT_PATH 24 | 25 | lm_eval --model hf \ 26 | --model_args pretrained=$MODEL \ 27 | --tasks m_mmlu_fr,arc_fr,hellaswag_fr,m_mmlu_es,arc_es,hellaswag_es,m_mmlu_de,arc_de,hellaswag_de,m_mmlu_ru,arc_ru,hellaswag_ru \ 28 | --device cuda:$GPU_ID \ 29 | --batch_size 8 \ 30 | --output_path $OUTPUT_PATH 31 | 32 | lm_eval --model hf \ 33 | --model_args pretrained=$MODEL \ 34 | --tasks ifeval \ 35 | --device cuda:$GPU_ID \ 36 | --batch_size 8 \ 37 | --output_path $OUTPUT_PATH 38 | 39 | 40 | conda deactivate 41 | conda activate bigcode 42 | cd bigcode-evaluation-harness 43 | 44 | accelerate launch main.py \ 45 | --model $MODEL \ 46 | --max_length_generation 512 \ 47 | --precision bf16 \ 48 | --tasks humanevalplus,mbppplus \ 49 | --temperature 0.2 \ 50 | --n_samples 10 \ 51 | --batch_size 10 \ 52 | --allow_code_execution \ 53 | --metric_output_path $OUTPUT_PATH/code_eval.json \ 54 | --use_auth_token 55 | 56 | cd .. 57 | conda deactivate 58 | conda activate safety-eval 59 | cd safety-eval-fork 60 | 61 | export OPENAI_API_KEY='' 62 | 63 | python evaluation/eval.py generators \ 64 | --model_name_or_path $MODEL \ 65 | --use_vllm \ 66 | --model_input_template_path_or_name llama3 \ 67 | --tasks wildguardtest,harmbench,xstest,do_anything_now \ 68 | --report_output_path $OUTPUT_PATH/safety_eval.json \ 69 | --save_individual_results_path $OUTPUT_PATH/safety_generation.json \ 70 | --batch_size 8 71 | -------------------------------------------------------------------------------- /scripts/merge.sh: -------------------------------------------------------------------------------- 1 | # Model soup 2 | python ./merging/main.py --algo TaskArithmetic --scaling-coef 0.2 --base-model meta-llama/Llama-3.2-3B 3 | 4 | # Task arithmetic 5 | python ./merging/main.py --algo TaskArithmetic --scaling-coef 0.4 --base-model meta-llama/Llama-3.2-3B 6 | 7 | # Fisher Merging 8 | ALL_TASKS=DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF 9 | 10 | for TASK in DartMath WildguardMix MagiCoder Aya Tulu3IF; do 11 | deepspeed --master_port=61001 --include=localhost:0,1,2,3 ./merging/main.py \ 12 | --algo Fisher \ 13 | --base-model meta-llama/Llama-3.2-3B \ 14 | --task_names $TASK \ 15 | --save_group $ALL_TASKS \ 16 | --fisher_only \ 17 | --model_coeff 1 18 | done 19 | 20 | python ./merging/main.py \ 21 | --algo Fisher \ 22 | --base-model meta-llama/Llama-3.2-3B \ 23 | --task_names $ALL_TASKS \ 24 | --save_group $ALL_TASKS \ 25 | --merge_only \ 26 | --keep_checkpoints \ 27 | --model_coeff 1 28 | 29 | # RegMean 30 | python ./merging/main.py --algo RegMean --base-model meta-llama/Llama-3.2-3B --task_names DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF --reduction 0.5 31 | 32 | # RegMeanPlusPlus 33 | python ./merging/main.py --algo RegMeanPlusPlus --base-model meta-llama/Llama-3.2-3B --task_names DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF --reduction 0.1 34 | 35 | # TIES Merging 36 | python ./merging/main.py --algo TIES --base-model meta-llama/Llama-3.2-3B --K 0.3 --scaling-coef 0.4 37 | 38 | # DARE 39 | python ./merging/main.py --algo RegMean --base-model meta-llama/Llama-3.2-3B --p 0.9 --scaling-coef 0.4 40 | 41 | # Consensus TA 42 | python ./merging/main.py --algo RegMean --base-model meta-llama/Llama-3.2-3B --scaling-coef 0.4 43 | 44 | # Dataless Localize-and-Stitch 45 | python ./merging/main.py --algo LocalizeAndStitch --base-model meta-llama/Llama-3.2-3B --sparsity 0.1 --dataless 46 | 47 | # Localize-and-Stitch 48 | python ./merging/main.py --algo LocalizeAndStitch --base-model meta-llama/Llama-3.2-3B --lr 1e8 --sparsity 0.1 --n_epochs 1 49 | -------------------------------------------------------------------------------- /merging/merging_methods/consensus.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from merging_methods.utils import * 3 | from merging_methods.merger import Merger 4 | 5 | 6 | class Consensus(Merger): 7 | def __init__(self, base_model, ft_models, save_path): 8 | super().__init__(base_model, ft_models, save_path)\ 9 | 10 | def tune_lamda(self, mtl_tv, tv, i): 11 | for lamda in [0.2,0.6]: 12 | print(f'Tuning lamda: {lamda} for model {i}') 13 | tall_mask = (torch.abs(tv) > torch.abs(mtl_tv - tv) * lamda) 14 | 15 | masked_model = vector_to_state_dict(tv * tall_mask, self.base_model) 16 | 17 | save_dir = './tmp/' + self.base_model_name.split('/')[1] + '/' + f'Consensus_{i}_lamda_' + str(lamda) 18 | masked_model.save_pretrained(save_dir) 19 | self.tokenizer.save_pretrained(save_dir) 20 | 21 | def merge(self, **kwargs): 22 | k = kwargs['k'] 23 | scaling_coef = kwargs['scaling_coef'] 24 | 25 | task_vectors = [get_task_vector(ft_model, self.base_model) for ft_model in self.ft_ckpts] 26 | mtl_tv = sum(task_vectors) 27 | 28 | tall_masks = [] 29 | # replace this with results from the tune_lamda function 30 | lamdas = [0.2, 0.2, 0.2, 0.2, 0.2] 31 | for i in range(len(task_vectors)): 32 | tv = task_vectors[i] 33 | tall_mask = (torch.abs(tv) > torch.abs(mtl_tv - tv) * lamdas[i]) 34 | tall_masks.append(tall_mask) 35 | 36 | consensus_mask = torch.zeros_like(tall_masks[0], dtype=torch.int16) 37 | for mask in tall_masks: 38 | consensus_mask += mask.to(torch.int16) 39 | consensus_mask = consensus_mask >= k 40 | 41 | merged_tv = mtl_tv * consensus_mask * scaling_coef 42 | 43 | merged_model = vector_to_state_dict(merged_tv, self.base_model) 44 | 45 | merged_model.save_pretrained(self.save_path) 46 | self.tokenizer.save_pretrained(self.save_path) -------------------------------------------------------------------------------- /merging/merging_methods/localize_and_stitch.py: -------------------------------------------------------------------------------- 1 | from merging_methods.utils import * 2 | from merging_methods.merger import Merger 3 | from merging_methods.localize_utils import * 4 | from transformers import AutoModelForCausalLM 5 | from datasets import load_dataset 6 | 7 | 8 | class LocalizeAndStitch(Merger): 9 | def __init__(self, base_model, ft_models, save_path): 10 | super().__init__(base_model, ft_models, save_path) 11 | 12 | self.task_names = ['instruction', 'math', 'coding', 'safety', 'multilingual'] 13 | 14 | def extract_format_keys(self, task): 15 | dataset = load_dataset(f'MergeBench/{task}_val', split='train') 16 | 17 | if task == 'safety': 18 | format_keys = {"instruction_key": "prompt", "output_key": "response"} 19 | elif task == 'multilingual': 20 | format_keys = {"instruction_key": "inputs", "output_key": "targets"} 21 | elif task == 'math': 22 | format_keys = {"instruction_key": "query", "output_key": "response"} 23 | elif task == 'instruction': 24 | format_keys = {"instruction_key": "instruction", "output_key": "output"} 25 | elif task == 'coding': 26 | format_keys = {"output_key": "response"} 27 | 28 | return dataset, format_keys 29 | 30 | def merge(self, **kwargs): 31 | graft_args = {} 32 | dataless = kwargs['dataless'] 33 | graft_args['sparsity'] = kwargs['sparsity'] 34 | graft_args['sigmoid_bias'] = kwargs['sigmoid_bias'] 35 | if not dataless: 36 | graft_args['lr'] = kwargs['learning_rate'] 37 | graft_args['num_train_epochs'] = kwargs['num_train_epochs'] 38 | graft_args['l1_strength'] = kwargs['l1_strength'] 39 | 40 | # Localize 41 | masks = [] 42 | for i in range(len(self.ft_ckpts)): 43 | current_task = self.task_names[i] 44 | print(f'Localizing {current_task} model') 45 | ft_model = self.ft_ckpts[i] 46 | trainable_params = select_trainable_params(ft_model) 47 | 48 | localizer = Localizer(trainable_params, self.base_model, ft_model, graft_args, self.base_model_name) 49 | 50 | if not dataless: 51 | print(f'Training mask {current_task} model') 52 | dataset, format_keys = self.extract_format_keys(self.task_names[i]) 53 | 54 | localizer.train_mask(dataset, format_keys) 55 | 56 | mask, _ = localizer.interpolate_model(round_=True, return_mask=True, train=False) 57 | masks.append(mask) 58 | 59 | # Stitch 60 | final_model = AutoModelForCausalLM.from_pretrained(self.base_model_name) 61 | stitcher = Stitcher(trainable_params, final_model, self.base_model, self.ft_ckpts, masks) 62 | merged_model = stitcher.interpolate_models() 63 | 64 | merged_model.save_pretrained(self.save_path) 65 | self.tokenizer.save_pretrained(self.save_path) -------------------------------------------------------------------------------- /merging/prepare_args.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | 3 | def create_parser(): 4 | _parser = argparse.ArgumentParser(description='Configuration for MergeBench') 5 | 6 | # DDP 7 | _parser.add_argument("--local_rank", type=int, default=0) 8 | 9 | # Task arithmetic 10 | _parser.add_argument('--scaling-coef', default=1, type=float) 11 | 12 | # TIES 13 | _parser.add_argument('--K', default=0.2, type=float) 14 | _parser.add_argument('--merge_func', default="sum", type=str) 15 | 16 | # DARE 17 | _parser.add_argument('--p', default=0.8, type=float) 18 | 19 | # LocalizeAndStitch 20 | _parser.add_argument('--sigmoid_bias', default=3, type=float) 21 | _parser.add_argument('--sparsity', default=0.1, type=float) 22 | _parser.add_argument('--lr', default=1e8, type=float) 23 | _parser.add_argument('--n_epochs', default=1, type=int) 24 | _parser.add_argument('--l1_strength', default=0.0, type=float) 25 | _parser.add_argument('--dataless', action='store_true') 26 | 27 | # Consensus 28 | _parser.add_argument('--k', default=2, type=int) 29 | _parser.add_argument('--lamda', default=0.5, type=float) 30 | 31 | # RegMean and RegMeanPlusPlus 32 | _parser.add_argument('--task_names', type=str) 33 | _parser.add_argument('--reduction', type=float) 34 | 35 | # Fisher 36 | _parser.add_argument("--fisher_only", action="store_true", help='fisher-stage1') 37 | _parser.add_argument("--merge_only", action="store_true", help='fisher-stage2') 38 | _parser.add_argument("--save_group", type=str, default=None) 39 | _parser.add_argument("--model_coeff_value", type=float, default=0.3) 40 | _parser.add_argument("--keep_checkpoints", action="store_true", help='whether delete intermediate files') 41 | 42 | 43 | return _parser 44 | 45 | def prepare_args(params): 46 | kwargs = {} 47 | if params.algo == 'TaskArithmetic': 48 | kwargs['scaling_coef'] = params.scaling_coef 49 | elif params.algo == 'TIES': 50 | kwargs['scaling_coef'] = params.scaling_coef 51 | kwargs['merge_func'] = params.merge_func 52 | kwargs['K'] = params.K 53 | elif params.algo == 'DARE': 54 | kwargs['scaling_coef'] = params.scaling_coef 55 | kwargs['p'] = params.p 56 | elif params.algo == 'LocalizeAndStitch': 57 | kwargs['sparsity'] = params.sparsity 58 | kwargs['dataless'] = params.dataless 59 | kwargs['sigmoid_bias'] = params.sigmoid_bias 60 | if not params.dataless: 61 | kwargs['learning_rate'] = params.lr 62 | kwargs['num_train_epochs'] = params.n_epochs 63 | kwargs['l1_strength'] = params.l1_strength 64 | elif params.algo == 'Consensus': 65 | kwargs['scaling_coef'] = params.scaling_coef 66 | kwargs['k'] = params.k 67 | kwargs['lamda'] = params.lamda 68 | elif params.algo == 'RegMean' or params.algo == 'RegMeanPlusPlus': 69 | kwargs['task_names'] = params.task_names 70 | kwargs['reduction'] = params.reduction 71 | elif params.algo == 'Fisher': 72 | kwargs['task_names'] = params.task_names 73 | kwargs['merge_only'] = params.merge_only 74 | kwargs['fisher_only'] = params.fisher_only 75 | kwargs['save_group'] = params.save_group 76 | kwargs['model_coeff_value'] = params.model_coeff_value 77 | kwargs['keep_checkpoints'] = params.keep_checkpoints 78 | else: 79 | raise ValueError('No support merging method {}'.format(params.algo)) 80 | 81 | return kwargs -------------------------------------------------------------------------------- /merged_models/Llama-3.1-8B_merged/RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1/safety_eval.json: -------------------------------------------------------------------------------- 1 | { 2 | "wildguardtest": { 3 | "micro harm (lower)": 0.4753004005340454, 4 | "prompt_type harm (lower)": { 5 | "private_information_individual": 0.19753086419753085, 6 | "others": 0.45918367346938777, 7 | "cyberattack": 0.7083333333333334, 8 | "sexual_content": 0.44680851063829785, 9 | "social_stereotypes_and_unfair_discrimination": 0.34210526315789475, 10 | "toxic_language_hate_speech": 0.7804878048780488, 11 | "violence_and_physical_harm": 0.7, 12 | "defamation_encouraging_unethical_or_unsafe_actions": 0.717391304347826, 13 | "fraud_assisting_illegal_activities": 0.55, 14 | "mental_health_over-reliance_crisis": 0.4666666666666667, 15 | "causing_material_harm_by_disseminating_misinformation": 0.4444444444444444, 16 | "disseminating_false_or_misleading_information_encouraging_disinformation_campaigns": 0.7674418604651163, 17 | "copyright_violations": 0.3, 18 | "sensitive_information_organization_government": 0.10204081632653061 19 | }, 20 | "adversarial harm (lower)": 0.47181008902077154, 21 | "vanilla harm (lower)": 0.47815533980582525 22 | }, 23 | "harmbench": { 24 | "micro ASR (lower)": 0.65625, 25 | "FunctionalCategory ASR (lower)": { 26 | "standard": 0.7735849056603774, 27 | "contextual": 0.7160493827160493, 28 | "copyright": 0.3625 29 | }, 30 | "SemanticCategory ASR (lower)": { 31 | "misinformation_disinformation": 0.5740740740740741, 32 | "chemical_biological": 0.9047619047619048, 33 | "illegal": 0.7924528301886793, 34 | "harmful": 0.6111111111111112, 35 | "copyright": 0.3625, 36 | "cybercrime_intrusion": 0.9423076923076923, 37 | "harassment_bullying": 0.47619047619047616 38 | } 39 | }, 40 | "xstest": { 41 | "inverted_rta_all_safe": 0.884, 42 | "inverted_rta_all_contrast": 0.775, 43 | "inverted_rta_homonyms": 0.92, 44 | "inverted_rta_contrast_homonyms": 0.8, 45 | "inverted_rta_figurative_language": 0.8, 46 | "inverted_rta_contrast_figurative_language": 0.96, 47 | "inverted_rta_safe_targets": 0.84, 48 | "inverted_rta_contrast_safe_targets": 0.8, 49 | "inverted_rta_safe_contexts": 0.92, 50 | "inverted_rta_contrast_safe_contexts": 0.92, 51 | "inverted_rta_definitions": 0.92, 52 | "inverted_rta_contrast_definitions": 0.84, 53 | "inverted_rta_nons_group_real_discr": 0.8, 54 | "inverted_rta_real_group_nons_discr": 0.84, 55 | "inverted_rta_contrast_discr": 0.6799999999999999, 56 | "inverted_rta_historical_events": 0.96, 57 | "inverted_rta_contrast_historical_events": 0.6, 58 | "inverted_rta_privacy_public": 0.92, 59 | "inverted_rta_privacy_fictional": 0.92, 60 | "inverted_rta_contrast_privacy": 0.6, 61 | "refusal_prf": { 62 | "precision": 0.6081081081081081, 63 | "recall": 0.225, 64 | "f1": 0.3284671532846716 65 | }, 66 | "overall_accuracy": 0.5911111111111111 67 | }, 68 | "do_anything_now": { 69 | "macro ASR": 0.6066666666666667, 70 | "jailbreak_chat ASR": 0.6206896551724138, 71 | "LLM Promptwriting ASR": 0.5, 72 | "BreakGPT ASR": 0.375, 73 | "ChatGPTJailbreak ASR": 0.5, 74 | "ChatGPT ASR": 0.8, 75 | "AI Prompt Sharing ASR": 0.3333333333333333 76 | } 77 | } -------------------------------------------------------------------------------- /merged_models/Llama-3.2-3B_merged/RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1/safety_eval.json: -------------------------------------------------------------------------------- 1 | { 2 | "wildguardtest": { 3 | "micro harm (lower)": 0.650200267022697, 4 | "prompt_type harm (lower)": { 5 | "private_information_individual": 0.5432098765432098, 6 | "others": 0.6428571428571429, 7 | "cyberattack": 0.6875, 8 | "sexual_content": 0.6595744680851063, 9 | "social_stereotypes_and_unfair_discrimination": 0.5263157894736842, 10 | "toxic_language_hate_speech": 0.8292682926829268, 11 | "violence_and_physical_harm": 0.875, 12 | "defamation_encouraging_unethical_or_unsafe_actions": 0.8260869565217391, 13 | "fraud_assisting_illegal_activities": 0.5333333333333333, 14 | "mental_health_over-reliance_crisis": 0.6, 15 | "causing_material_harm_by_disseminating_misinformation": 0.7333333333333333, 16 | "disseminating_false_or_misleading_information_encouraging_disinformation_campaigns": 0.8372093023255814, 17 | "copyright_violations": 0.5666666666666667, 18 | "sensitive_information_organization_government": 0.4897959183673469 19 | }, 20 | "adversarial harm (lower)": 0.5964391691394659, 21 | "vanilla harm (lower)": 0.6941747572815534 22 | }, 23 | "harmbench": { 24 | "micro ASR (lower)": 0.646875, 25 | "FunctionalCategory ASR (lower)": { 26 | "standard": 0.8113207547169812, 27 | "contextual": 0.7037037037037037, 28 | "copyright": 0.2625 29 | }, 30 | "SemanticCategory ASR (lower)": { 31 | "misinformation_disinformation": 0.7222222222222222, 32 | "chemical_biological": 0.8809523809523809, 33 | "illegal": 0.8113207547169812, 34 | "harmful": 0.6111111111111112, 35 | "copyright": 0.2625, 36 | "cybercrime_intrusion": 0.8461538461538461, 37 | "harassment_bullying": 0.5714285714285714 38 | } 39 | }, 40 | "xstest": { 41 | "inverted_rta_all_safe": 0.376, 42 | "inverted_rta_all_contrast": 0.565, 43 | "inverted_rta_homonyms": 0.43999999999999995, 44 | "inverted_rta_contrast_homonyms": 0.6, 45 | "inverted_rta_figurative_language": 0.43999999999999995, 46 | "inverted_rta_contrast_figurative_language": 0.6799999999999999, 47 | "inverted_rta_safe_targets": 0.24, 48 | "inverted_rta_contrast_safe_targets": 0.72, 49 | "inverted_rta_safe_contexts": 0.52, 50 | "inverted_rta_contrast_safe_contexts": 0.56, 51 | "inverted_rta_definitions": 0.64, 52 | "inverted_rta_contrast_definitions": 0.52, 53 | "inverted_rta_nons_group_real_discr": 0.28, 54 | "inverted_rta_real_group_nons_discr": 0.31999999999999995, 55 | "inverted_rta_contrast_discr": 0.36, 56 | "inverted_rta_historical_events": 0.4, 57 | "inverted_rta_contrast_historical_events": 0.64, 58 | "inverted_rta_privacy_public": 0.28, 59 | "inverted_rta_privacy_fictional": 0.19999999999999996, 60 | "inverted_rta_contrast_privacy": 0.43999999999999995, 61 | "refusal_prf": { 62 | "precision": 0.35802469135802467, 63 | "recall": 0.435, 64 | "f1": 0.3927765237020316 65 | }, 66 | "overall_accuracy": 0.4022222222222222 67 | }, 68 | "do_anything_now": { 69 | "macro ASR": 0.5633333333333334, 70 | "jailbreak_chat ASR": 0.5593869731800766, 71 | "LLM Promptwriting ASR": 0.5, 72 | "BreakGPT ASR": 0.4375, 73 | "ChatGPTJailbreak ASR": 0.5, 74 | "ChatGPT ASR": 1.0, 75 | "AI Prompt Sharing ASR": 0.3333333333333333 76 | } 77 | } -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # MergeBench: A Benchmark for Merging Domain-Specialized LLMs 2 | 3 | This is the official repo of MergeBench in the paper ["MergeBench: A Benchmark for Merging Domain-Specialized LLMs"](https://arxiv.org/abs/2505.10833) at NeurIPS 2025 Datasets and Benchmarks Track. 4 | 5 | 6 | ![alt text](MergeBench.png "MergeBench") 7 | 8 | ## Abstract 9 | Model merging provides a scalable alternative to multi-task training by combining specialized finetuned models through parameter arithmetic, enabling efficient deployment without the need for joint training or access to all task data. While recent methods have shown promise, existing evaluations are limited in both model scale and task diversity, leaving open questions about their applicability to large, domain-specialized LLMs. To tackle the challenges, we introduce MergeBench, a comprehensive evaluation suite designed to assess model merging at scale. MergeBench builds on state-of-the-art open-source language models, including Llama and Gemma families at 2B to 9B scales, and covers five key domains: instruction following, mathematics, multilingual understanding, coding and safety. We standardize finetuning and evaluation protocols, and assess eight representative merging methods across multi-task performance, forgetting and runtime efficiency. Based on extensive experiments, we provide practical guidelines for algorithm selection and share insights showing that model merging tends to perform better on stronger base models, with techniques such as merging coefficient tuning and sparsification improving knowledge retention. However, several challenges remain, including the computational cost on large models, the gap for in-domain performance compared to multi-task models, and the underexplored role of model merging in standard LLM training pipelines. We hope MergeBench provides a foundation for future research to advance the understanding and practical application of model merging. 10 | 11 | ## Merging Algorithms 12 | All of the constituent model checkpoints are available at https://huggingface.co/MergeBench. We provide further details in the readme file of the `merging` folder. 13 | 14 | ## Evaluation 15 | We utilize three existing evaluation packages, and we recommend creating separate environments for each evaluation. 16 | ### lm-eval 17 | ``` 18 | conda create -n lmeval python=3.10.9 19 | conda activate lmeval 20 | 21 | git clone --depth 1 https://github.com/EleutherAI/lm-evaluation-harness 22 | cd lm-evaluation-harness 23 | pip install -e . 24 | 25 | pip3 install torch==2.1.2 torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121 26 | 27 | pip install langdetect 28 | pip install immutabledict 29 | ``` 30 | 31 | ### bigcode-eval 32 | ``` 33 | conda create -n bigcode python=3.10.9 34 | conda activate bigcode 35 | 36 | git clone https://github.com/bigcode-project/bigcode-evaluation-harness.git 37 | cd bigcode-evaluation-harness 38 | 39 | pip install -e . 40 | pip3 install torch==2.1.2 torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121 41 | pip install numpy==1.24.1 42 | ``` 43 | 44 | ### safety-eval 45 | To install the evaluation 46 | ``` 47 | git clone https://github.com/nouhadziri/safety-eval-fork 48 | conda create -n safety-eval python=3.10 && conda activate safety-eval 49 | pip install -e . 50 | pip install -r requirements.txt 51 | pip install vllm==0.4.2 52 | ``` 53 | Running the evaluation necessitates a value for openai API key as some tasks in the benchmark suite requires openai API. However, for the ones we test on, it is not required, and you can put the placeholder as follows 54 | ``` 55 | export OPENAI_API_KEY='' 56 | ``` 57 | 58 | To perform the full evaluation on all five task categories on the base `Llama-3.2-3B` model with GPU 0 and save the results in the folder `results/llama-3.2-3b`, run the following command: 59 | ``` 60 | bash scripts/evaluate.sh meta-llama/Llama-3.2-3B 0 results/llama-3.2-3b 61 | ``` 62 | 63 | ## Citation 64 | ``` 65 | @inproceedings{ 66 | he2025mergebench, 67 | title={MergeBench: A Benchmark for Merging Domain-Specialized {LLM}s}, 68 | author={Yifei He and Siqi Zeng and Yuzheng Hu and Rui Yang and Tong Zhang and Han Zhao}, 69 | booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems Datasets and Benchmarks Track}, 70 | year={2025}, 71 | url={https://openreview.net/forum?id=rw50iUoyLu} 72 | } 73 | ``` -------------------------------------------------------------------------------- /merging/merging_methods/fisher_utils.py: -------------------------------------------------------------------------------- 1 | import os 2 | import gc 3 | 4 | import torch 5 | from torch.nn import functional as F 6 | from trl import SFTTrainer 7 | 8 | 9 | 10 | class FisherTrainer(SFTTrainer): 11 | 12 | def __init__( 13 | self, 14 | fisher_variant="hard", 15 | **kwargs 16 | ): 17 | super().__init__(**kwargs) 18 | self.fisher_variant = fisher_variant 19 | 20 | def compute_loss(self, model, inputs, num_items_in_batch, return_outputs=False): 21 | outputs = model( 22 | input_ids=inputs["input_ids"], 23 | attention_mask=inputs["attention_mask"], 24 | return_dict=True 25 | ) 26 | 27 | logits = outputs.logits[:, -1, :] # (batch_size, vocab_size), check last token 28 | 29 | if self.fisher_variant == "hard": 30 | log_probs = F.log_softmax(logits, dim=-1) 31 | _, target_labels = logits.max(dim=-1) 32 | loss = F.nll_loss(log_probs, target_labels) 33 | 34 | elif self.fisher_variant == "soft": 35 | probs = torch.softmax(logits, dim=-1).detach() 36 | log_probs = torch.log_softmax(logits, dim=-1) 37 | 38 | vocab_size = probs.size(-1) 39 | nll_losses = [] 40 | for label_id in range(vocab_size): 41 | targets = torch.full( 42 | (probs.size(0),), label_id, 43 | dtype=torch.long, device=probs.device 44 | ) 45 | nll_loss_per_label = F.nll_loss( 46 | log_probs, targets, reduction="none" 47 | ) 48 | nll_losses.append(nll_loss_per_label) 49 | 50 | nll_losses = torch.stack(nll_losses, dim=-1) 51 | weighted_nll_losses = probs * nll_losses 52 | loss = weighted_nll_losses.sum(dim=-1).mean() 53 | 54 | else: 55 | loss = outputs.loss 56 | return (loss, outputs) if return_outputs else loss 57 | 58 | 59 | def save_tensor_dict(tensor_dict, path): 60 | os.makedirs(path, exist_ok=True) 61 | for key, tensor in tensor_dict.items(): 62 | filename = os.path.join(path, key + ".pt") 63 | torch.save(tensor, filename) 64 | return 65 | 66 | 67 | def cleanup_task_loader(task_loader): 68 | """ 69 | Safely clean up task_loader and its nested trainer to reduce CPU and GPU memory usage. 70 | """ 71 | trainer = getattr(task_loader, 'trainer', None) 72 | 73 | if trainer is not None: 74 | for attr in [ 75 | 'model', 'processing_class', 'train_dataset', 'eval_dataset', 76 | 'callback_handler', 'args', 'data_collator', 77 | 'train_dataloader', 'eval_dataloader', 78 | 'optimizer', 'lr_scheduler', 79 | ]: 80 | if hasattr(trainer, attr): 81 | try: 82 | setattr(trainer, attr, None) 83 | except Exception as e: 84 | print(f"Warning: couldn't clear trainer.{attr}: {e}") 85 | try: 86 | del trainer 87 | except Exception as e: 88 | print(f"Warning: couldn't delete trainer: {e}") 89 | 90 | for attr in ['training_dataset', 'training_args']: 91 | if hasattr(task_loader, attr): 92 | try: 93 | setattr(task_loader, attr, None) 94 | except Exception as e: 95 | print(f"Warning: couldn't clear task_loader.{attr}: {e}") 96 | 97 | try: 98 | del task_loader 99 | except Exception as e: 100 | print(f"Warning: couldn't delete task_loader: {e}") 101 | 102 | gc.collect() 103 | torch.cuda.empty_cache() 104 | 105 | 106 | def get_expected_fisher_keys(model): 107 | 108 | return { 109 | name for name, param in model.named_parameters() 110 | if param.requires_grad and "lm_head" not in name 111 | } 112 | 113 | def is_tensor_dict_complete(path, keys): 114 | if not os.path.exists(path): 115 | print(f'{path} doesn\'t exist') 116 | return False 117 | for k in keys: 118 | file_path = os.path.join(path, k + ".pt") 119 | if not os.path.exists(file_path): 120 | print(f'{file_path} doesn\'t exist') 121 | return False 122 | try: 123 | _ = torch.load(file_path, map_location="cpu") 124 | except Exception: 125 | print(f'{file_path} is corrupted') 126 | return False # File exists but is corrupted or unreadable 127 | return True -------------------------------------------------------------------------------- /merging/merging_methods/regmean_utils.py: -------------------------------------------------------------------------------- 1 | 2 | import re 3 | import os 4 | from tqdm import tqdm 5 | import torch 6 | from torch import nn 7 | import gc 8 | 9 | # https://github.com/bloomberg/dataless-model-merging/blob/main/regmean_demo.ipynb 10 | 11 | def filter_modules_by_regex(base_module, include_patterns, include_type): 12 | modules = {} 13 | for name, module in base_module.named_modules(): 14 | valid_name = not include_patterns or any( 15 | [re.match(patt, name) for patt in include_patterns] 16 | ) 17 | valid_type = not include_type or any( 18 | [isinstance(module, md_cls) for md_cls in include_type] 19 | ) 20 | if valid_type and valid_name: 21 | modules[name] = module 22 | return modules 23 | 24 | 25 | def send_inputs_to_device(inputs, device): 26 | for k, v in inputs.items(): 27 | if isinstance(v, torch.Tensor): inputs[k] = v.to(device) # for 'hidden_states', 'attention_mask', 'position_ids', 'cache_position', 'past_key_values' 28 | elif isinstance(v, tuple): inputs[k] = tuple([vv.to(device) for vv in v]) # for 'position_embeddings' 29 | 30 | return inputs 31 | 32 | 33 | def compute_grams(trainer, finetuned_model, train_dataloader): 34 | covs = {} 35 | xn = {} 36 | 37 | def get_grams(name): 38 | def hook(module, input, output): 39 | """ 40 | Note: adhere to signature of hook functions 41 | """ 42 | x = input[0].detach() # $[b,t,h] 43 | x = x.view(-1, x.size(-1)) 44 | xtx = torch.matmul(x.transpose(0, 1), x) # [h,h] 45 | if name not in covs: 46 | covs[name] = xtx / x.size(0) 47 | xn[name] = x.size(0) 48 | else: 49 | covs[name] = (covs[name] * xn[name] + xtx) / (x.size(0) + xn[name]) 50 | xn[name] += x.size(0) 51 | 52 | return hook 53 | 54 | device = "cpu" 55 | if trainer is not None: 56 | device = trainer.args.device 57 | elif torch.cuda.is_available(): 58 | device = "cuda:0" 59 | model = finetuned_model.to(device) 60 | linear_modules = filter_modules_by_regex( 61 | model, None, [nn.Linear] 62 | ) 63 | handles = [] 64 | for name, module in linear_modules.items(): 65 | handle = module.register_forward_hook(get_grams(name)) 66 | handles.append(handle) 67 | 68 | total = len(train_dataloader) 69 | for inputs in tqdm( 70 | train_dataloader, total=total, desc="Computing gram matrix", 71 | disable = type(train_dataloader) == list 72 | ): 73 | if type(train_dataloader) == list: 74 | # For RegMeanPlusPlus 75 | inputs = send_inputs_to_device(inputs, device) 76 | _ = model(**inputs) 77 | inputs = send_inputs_to_device(inputs, "cpu") 78 | else: 79 | # For RegMean 80 | inputs = trainer._prepare_inputs(inputs) 81 | _ = model(**inputs) 82 | 83 | for handle in handles: 84 | handle.remove() 85 | 86 | return covs 87 | 88 | def reduce_non_diag(cov_mat, a): 89 | diag_weight = torch.diag(torch.ones(cov_mat.size(0), dtype=cov_mat.dtype) - a).to(cov_mat.device) 90 | non_diag_weight = torch.zeros_like(diag_weight).fill_(a) 91 | weight = diag_weight + non_diag_weight 92 | return cov_mat * weight 93 | 94 | def save_tensor_dict(tensor_dict, path): 95 | os.makedirs(path, exist_ok=True) 96 | for key, tensor in tensor_dict.items(): 97 | torch.save(tensor, os.path.join(path, key + ".pt")) 98 | 99 | def cleanup_task_loader(task_loader): 100 | """ 101 | Safely clean up task_loader and its nested trainer to reduce CPU memory usage. 102 | """ 103 | trainer = getattr(task_loader, 'trainer', None) 104 | 105 | if trainer is not None: 106 | for attr in [ 107 | 'model', 'processing_class', 'train_dataset', 'eval_dataset', 108 | 'callback_handler', 'args', 'data_collator', 109 | 'train_dataloader', 'eval_dataloader', 110 | 'optimizer', 'lr_scheduler', 111 | ]: 112 | if hasattr(trainer, attr): 113 | try: 114 | setattr(trainer, attr, None) 115 | except Exception as e: 116 | print(f"Warning: couldn't clear trainer.{attr}: {e}") 117 | del trainer 118 | 119 | for attr in ['training_dataset', 'training_args']: 120 | if hasattr(task_loader, attr): 121 | try: 122 | setattr(task_loader, attr, None) 123 | except Exception as e: 124 | print(f"Warning: couldn't clear task_loader.{attr}: {e}") 125 | del task_loader 126 | 127 | gc.collect() -------------------------------------------------------------------------------- /merging/merging_methods/regmean.py: -------------------------------------------------------------------------------- 1 | import os 2 | import gc 3 | import shutil 4 | 5 | from tqdm import tqdm 6 | 7 | import torch 8 | 9 | from merging_methods.utils import * 10 | from merging_methods.merger import Merger 11 | 12 | import sys 13 | sys.path.append('/MergeBench/merging') 14 | from .regmean_utils import compute_grams, save_tensor_dict, reduce_non_diag, cleanup_task_loader 15 | from taskloader import * 16 | 17 | # https://github.com/bloomberg/dataless-model-merging/blob/main/regmean_demo.ipynb 18 | 19 | class RegMean(Merger): 20 | def __init__(self, base_model, ft_models, save_path): 21 | super().__init__(base_model, ft_models, save_path) 22 | 23 | def merge(self, **kwargs): 24 | reduction = kwargs["reduction"] 25 | 26 | exam_datasets = kwargs["task_names"].split("-") 27 | 28 | save_dir = self.save_path 29 | gram_dir = os.path.join(save_dir, "regmean") 30 | param_dir = os.path.join(save_dir, "params") 31 | 32 | all_param_names = set() 33 | model_params = self.base_model.state_dict() 34 | all_param_names.update(model_params.keys()) 35 | 36 | gram_dirs = [os.path.join(gram_dir, dataset_name) for dataset_name in exam_datasets] 37 | param_dirs = [os.path.join(param_dir, dataset_name) for dataset_name in exam_datasets] 38 | 39 | for idx, dataset_name in enumerate(exam_datasets): 40 | finetuned_model = self.ft_ckpts[idx] 41 | task_loader = TaskLoader(dataset_name, self.base_model, self.tokenizer, sample_size=1000) 42 | trainer = task_loader.trainer 43 | dataloader = trainer.get_train_dataloader() 44 | with torch.no_grad(): 45 | grams = compute_grams(trainer, finetuned_model, dataloader) 46 | save_tensor_dict(grams, os.path.join(gram_dir, dataset_name)) # contains most (linear) params grams 47 | save_tensor_dict(finetuned_model.state_dict(), os.path.join(param_dir, dataset_name)) # contains all params 48 | 49 | finetuned_model.to("cpu") 50 | cleanup_task_loader(task_loader) 51 | del finetuned_model, grams #, trainer, dataloader, task_loader 52 | torch.cuda.empty_cache() 53 | gc.collect() 54 | self.ft_ckpts = [] 55 | gc.collect() 56 | 57 | 58 | with torch.no_grad(): 59 | gram_module_names = {f[:-3] for f in os.listdir(gram_dirs[0]) if f.endswith(".pt")} 60 | avg_params = {} 61 | for name in tqdm(all_param_names, desc='Merging'): 62 | h_avged = False 63 | if name.endswith('.weight') and not name.startswith('lm_head'): 64 | module_name = name[:-len('.weight')] 65 | if module_name in gram_module_names: 66 | sum_gram, grams = None, None 67 | for model_id in range(len(gram_dirs)): 68 | param_grams = torch.load(os.path.join(gram_dirs[model_id], module_name + ".pt"), map_location='cpu').detach() 69 | param_grams = reduce_non_diag(param_grams, a=reduction) # avoid degeneration 70 | param = torch.load(os.path.join(param_dirs[model_id], name + ".pt"), map_location='cpu').detach() 71 | gram_m_w = torch.matmul(param_grams, param.transpose(0, 1)) 72 | if sum_gram is None: 73 | sum_gram = param_grams.clone() 74 | sum_gram_m_ws = gram_m_w.clone() 75 | else: 76 | sum_gram.add_(param_grams) 77 | sum_gram_m_ws.add_(gram_m_w) 78 | del param_grams, param, gram_m_w 79 | gc.collect() 80 | sum_gram_f32 = sum_gram.to(dtype=torch.float32) 81 | cond_number = torch.linalg.cond(sum_gram_f32) 82 | threshold = 1e8 83 | if cond_number > threshold or torch.any(torch.diag(sum_gram_f32) == 0): 84 | sum_gram_inv = torch.linalg.pinv(sum_gram_f32).to(dtype=sum_gram_m_ws.dtype) 85 | else: 86 | sum_gram_inv = torch.inverse(sum_gram_f32).to(dtype=sum_gram_m_ws.dtype) 87 | wt = torch.matmul(sum_gram_inv, sum_gram_m_ws) 88 | avg_params[name] = wt.transpose(0, 1) 89 | h_avged = True 90 | 91 | if not h_avged: # if not averaged with regmean, then do simple avg 92 | filtered_model_params = None 93 | for model_id in range(len(gram_dirs)): 94 | if not name.startswith('model.embed') and not name.startswith('lm_head'): # embed_tokens.weight have incompatible dimensions due to vocab size difference 95 | filtered_model_param = torch.load(os.path.join(param_dirs[model_id], name + ".pt"), map_location='cpu').detach() 96 | if filtered_model_params is None: 97 | filtered_model_params = filtered_model_param.clone() 98 | else: 99 | filtered_model_params.add_(filtered_model_param) 100 | del filtered_model_param 101 | gc.collect() 102 | avg_params[name] = filtered_model_params.div(len(gram_dirs)) 103 | 104 | shutil.rmtree(gram_dir) 105 | shutil.rmtree(param_dir) 106 | 107 | incompatible_params = self.base_model.load_state_dict(avg_params, strict=False) 108 | self.base_model.save_pretrained(save_dir) 109 | self.tokenizer.save_pretrained(save_dir) 110 | -------------------------------------------------------------------------------- /merging/merging_methods/ties_merging_utils.py: -------------------------------------------------------------------------------- 1 | import sys 2 | import os, copy 3 | import torch 4 | import matplotlib.pyplot as plt 5 | import numpy as np 6 | import re 7 | from collections import OrderedDict 8 | import torch.nn.functional as F 9 | # from transformers import AutoTokenizer, AutoModelForSeq2SeqLM 10 | 11 | ## Model conversion utils 12 | def state_dict_to_vector(state_dict, remove_keys=[]): 13 | shared_state_dict = copy.deepcopy(state_dict) 14 | for key in remove_keys: 15 | if key in shared_state_dict: 16 | del shared_state_dict[key] 17 | sorted_shared_state_dict = OrderedDict(sorted(shared_state_dict.items())) 18 | return torch.nn.utils.parameters_to_vector( 19 | [value.reshape(-1) for key, value in sorted_shared_state_dict.items()] 20 | ) 21 | 22 | 23 | def add_ptm_to_tv(tv_dict, ptm_dict): 24 | assert set(tv_dict.keys()) == set( 25 | ptm_dict.keys() 26 | ), "Differing parameter names in models." 27 | final_dict = copy.deepcopy(tv_dict) 28 | for k, v in ptm_dict.items(): 29 | final_dict[k] = tv_dict[k] + v 30 | return final_dict 31 | 32 | 33 | def check_parameterNamesMatch(checkpoints): 34 | parameter_names = set(checkpoints[0].keys()) 35 | 36 | if len(checkpoints) >= 2: 37 | # raise ValueError("Number of models is less than 2.") 38 | for checkpoint in checkpoints[1:]: 39 | current_parameterNames = set(checkpoint.keys()) 40 | if current_parameterNames != parameter_names: 41 | raise ValueError( 42 | "Differing parameter names in models. " 43 | f"The different parameters are {parameter_names.symmetric_difference(current_parameterNames)}" 44 | ) 45 | 46 | def check_state_dicts_equal(state_dict1, state_dict2): 47 | if set(state_dict1.keys()) != set(state_dict2.keys()): 48 | return False 49 | 50 | for key in state_dict1.keys(): 51 | if not torch.equal(state_dict1[key], state_dict2[key]): 52 | return False 53 | 54 | return True 55 | 56 | 57 | 58 | ## TIES MERGING UTILS 59 | 60 | def topk_values_mask(M, K=0.7, return_mask=False): 61 | if K > 1: 62 | K /= 100 63 | 64 | original_shape = M.shape 65 | if M.dim() == 1: 66 | M = M.unsqueeze(0) 67 | 68 | n, d = M.shape 69 | k = int(d * K) 70 | k = d - k # Keep top k elements instead of bottom k elements 71 | 72 | # Find the k-th smallest element by magnitude for each row 73 | kth_values, _ = M.abs().kthvalue(k, dim=1, keepdim=True) 74 | # Create a mask tensor with True for the top k elements in each row 75 | mask = M.abs() >= kth_values 76 | final_mask = mask.squeeze() if original_shape == M.squeeze().shape else mask 77 | 78 | if return_mask: 79 | return M * final_mask, final_mask.float().mean(dim=1), final_mask 80 | return M * final_mask, final_mask.float().mean(dim=1) 81 | 82 | 83 | def resolve_zero_signs(sign_to_mult, method="majority"): 84 | majority_sign = torch.sign(sign_to_mult.sum()) 85 | 86 | if method == "majority": 87 | sign_to_mult[sign_to_mult == 0] = majority_sign 88 | elif method == "minority": 89 | sign_to_mult[sign_to_mult == 0] = -1 * majority_sign 90 | return sign_to_mult 91 | 92 | 93 | def resolve_sign(Tensor): 94 | sign_to_mult = torch.sign(Tensor.sum(dim=0)) 95 | sign_to_mult = resolve_zero_signs(sign_to_mult, "majority") 96 | return sign_to_mult 97 | 98 | 99 | def disjoint_merge(Tensor, merge_func, sign_to_mult): 100 | merge_func = merge_func.split("-")[-1] 101 | 102 | # If sign is provided then we select the corresponding entries and aggregate. 103 | if sign_to_mult is not None: 104 | rows_to_keep = torch.where( 105 | sign_to_mult.unsqueeze(0) > 0, Tensor > 0, Tensor < 0 106 | ) 107 | selected_entries = Tensor * rows_to_keep 108 | # Else we select all non-zero entries and aggregate. 109 | else: 110 | rows_to_keep = Tensor != 0 111 | selected_entries = Tensor * rows_to_keep 112 | 113 | if merge_func == "mean": 114 | non_zero_counts = (selected_entries != 0).sum(dim=0).float() 115 | disjoint_aggs = torch.sum(selected_entries, dim=0) / torch.clamp(non_zero_counts, min=1) 116 | elif merge_func == "sum": 117 | disjoint_aggs = torch.sum(selected_entries, dim=0) 118 | elif merge_func == "max": 119 | disjoint_aggs = selected_entries.abs().max(dim=0)[0] 120 | disjoint_aggs *= sign_to_mult 121 | else: 122 | raise ValueError(f"Merge method {merge_func} is not defined.") 123 | 124 | return disjoint_aggs 125 | 126 | 127 | def ties_merging( 128 | flat_task_checks, 129 | reset_thresh=None, 130 | merge_func="", 131 | ): 132 | all_checks = flat_task_checks.clone() 133 | updated_checks, *_ = topk_values_mask( 134 | all_checks, K=reset_thresh, return_mask=False 135 | ) 136 | print(f"RESOLVING SIGN") 137 | final_signs = resolve_sign(updated_checks) 138 | assert final_signs is not None 139 | 140 | print(f"Disjoint AGGREGATION: {merge_func}") 141 | merged_tv = disjoint_merge(updated_checks, merge_func, final_signs) 142 | 143 | return merged_tv 144 | 145 | def disjoint_merge_split(Tensor, merge_func, sign_to_mult): 146 | merge_func = merge_func.split("-")[-1] 147 | 148 | # If sign is provided then we select the corresponding entries and aggregate. 149 | if sign_to_mult is not None: 150 | rows_to_keep = torch.where( 151 | sign_to_mult.unsqueeze(0) > 0, Tensor > 0, Tensor < 0 152 | ) 153 | selected_entries = Tensor * rows_to_keep 154 | # Else we select all non-zero entries and aggregate. 155 | else: 156 | rows_to_keep = Tensor != 0 157 | selected_entries = Tensor * rows_to_keep 158 | 159 | if merge_func == "sum": 160 | disjoint_aggs = torch.sum(selected_entries, dim=0) 161 | else: 162 | raise ValueError(f"Merge method {merge_func} is not defined.") 163 | 164 | return selected_entries, disjoint_aggs 165 | 166 | 167 | def ties_merging_split( 168 | flat_task_checks, 169 | reset_thresh=None, 170 | merge_func="", 171 | ): 172 | all_checks = flat_task_checks.clone() 173 | updated_checks, *_ = topk_values_mask( 174 | all_checks, K=reset_thresh, return_mask=False 175 | ) 176 | print(f"RESOLVING SIGN") 177 | final_signs = resolve_sign(updated_checks) 178 | assert final_signs is not None 179 | 180 | print(f"Disjoint AGGREGATION: {merge_func}") 181 | selected_entries, merged_tv = disjoint_merge_split(updated_checks, merge_func, final_signs) 182 | 183 | return selected_entries, merged_tv -------------------------------------------------------------------------------- /merging/merging_methods/fisher.py: -------------------------------------------------------------------------------- 1 | import os 2 | import gc 3 | import shutil 4 | 5 | from tqdm import tqdm 6 | 7 | import torch 8 | import torch.distributed as dist 9 | 10 | from accelerate.state import AcceleratorState, GradientState 11 | 12 | from merging_methods.utils import * 13 | from merging_methods.merger import Merger 14 | 15 | import sys 16 | sys.path.append('/MergeBench/merging') 17 | from .fisher_utils import FisherTrainer, save_tensor_dict, cleanup_task_loader, get_expected_fisher_keys, is_tensor_dict_complete 18 | from taskloader import * 19 | 20 | from accelerate.utils.deepspeed import DeepSpeedEngineWrapper 21 | from deepspeed.utils import safe_get_full_grad 22 | import os 23 | import gc 24 | import shutil 25 | 26 | from tqdm import tqdm 27 | 28 | import torch 29 | import torch.distributed as dist 30 | 31 | from accelerate.state import AcceleratorState, GradientState 32 | 33 | from merging_methods.utils import * 34 | from merging_methods.merger import Merger 35 | 36 | import sys 37 | sys.path.append('/home/cindy2000_sh/MergeBench/merging') 38 | from .fisher_utils import FisherTrainer, save_tensor_dict, cleanup_task_loader, get_expected_fisher_keys, is_tensor_dict_complete 39 | from taskloader import * 40 | 41 | from accelerate.utils.deepspeed import DeepSpeedEngineWrapper 42 | from deepspeed.utils import safe_get_full_grad 43 | 44 | # https://github.com/mmatena/model_merging/blob/master/model_merging/fisher.py 45 | 46 | class Fisher(Merger): 47 | def __init__(self, base_model, ft_models, save_path): 48 | super().__init__(base_model, ft_models, save_path) 49 | self.base_model = self.base_model.to('cpu') 50 | self.ft_ckpts = [ft_model.to('cpu') for ft_model in self.ft_ckpts] 51 | 52 | def merge(self, **kwargs): 53 | fisher_only = kwargs["fisher_only"] 54 | merge_only = kwargs["merge_only"] 55 | model_coeff_value = kwargs["model_coeff_value"] 56 | keep_checkpoints = kwargs['keep_checkpoints'] 57 | save_group = kwargs['save_group'] 58 | 59 | exam_datasets = kwargs["task_names"].split("-") 60 | all_tasks = save_group.split("-") 61 | self.ft_ckpts = { 62 | task: model.to('cpu') 63 | for task, model in zip(all_tasks, self.ft_ckpts) 64 | } 65 | 66 | save_dir = self.save_path 67 | fisher_dir = os.path.join(save_dir, "fisher") 68 | param_dir = os.path.join(save_dir, "params") 69 | 70 | all_param_names = set() 71 | model_params = self.base_model.state_dict() 72 | all_param_names.update(model_params.keys()) 73 | 74 | fisher_dirs = [os.path.join(fisher_dir, dataset_name) for dataset_name in exam_datasets] 75 | param_dirs = [os.path.join(param_dir, dataset_name) for dataset_name in exam_datasets] 76 | 77 | if fisher_only: 78 | for idx, dataset_name in enumerate(exam_datasets): 79 | 80 | fisher = {} 81 | n_steps_ref = [0] # Mutable n_steps (so it updates across steps) 82 | 83 | def make_patched_backward(fisher_dict, n_steps_ref): 84 | def patched_backward(self, loss, **kwargs): 85 | self.engine.backward(loss, **kwargs) 86 | with torch.no_grad(): 87 | for name, param in self.engine.module.named_parameters(): 88 | if not param.requires_grad: 89 | continue 90 | grad_ds = safe_get_full_grad(param) 91 | if grad_ds is not None: 92 | grad_cpu = grad_ds.detach().cpu() 93 | grad_sq_cpu = grad_cpu ** 2 94 | if name not in fisher_dict: 95 | fisher_dict[name] = grad_sq_cpu 96 | else: 97 | fisher_dict[name] += grad_sq_cpu 98 | del grad_ds, grad_cpu, grad_sq_cpu 99 | torch.cuda.empty_cache() 100 | n_steps_ref[0] += 1 101 | self.engine.step() 102 | return patched_backward 103 | 104 | 105 | DeepSpeedEngineWrapper.backward = make_patched_backward(fisher, n_steps_ref) 106 | 107 | finetuned_model = self.ft_ckpts[dataset_name] 108 | expected_fisher_keys = get_expected_fisher_keys(finetuned_model) 109 | param_path = param_dirs[idx] 110 | fisher_path = fisher_dirs[idx] 111 | 112 | fisher_complete = is_tensor_dict_complete(fisher_path, expected_fisher_keys) 113 | params_complete = is_tensor_dict_complete(param_path, all_param_names) 114 | 115 | print(exam_datasets,"fisher_complete:",fisher_complete,"params_complete:",params_complete) 116 | 117 | if fisher_complete and params_complete: 118 | print(f"Skipping {dataset_name} — already processed.") 119 | self.ft_ckpts[dataset_name] = finetuned_model.to("cpu") 120 | continue 121 | 122 | elif fisher_complete and not params_complete: 123 | print(f"Processing {dataset_name}") 124 | if dist.get_rank() == 0: 125 | save_tensor_dict(finetuned_model.state_dict(), os.path.join(param_dir, dataset_name)) 126 | continue 127 | 128 | print(f"Processing {dataset_name}") 129 | if not params_complete: 130 | if dist.get_rank() == 0: 131 | save_tensor_dict(finetuned_model.state_dict(), os.path.join(param_dir, dataset_name)) 132 | 133 | finetuned_model = self.ft_ckpts[dataset_name].to("cuda") 134 | 135 | task_loader = TaskLoader(dataset_name, finetuned_model, self.tokenizer, sample_size=1000) 136 | sft_trainer = task_loader.trainer 137 | 138 | sft_args = sft_trainer.args 139 | sft_model = sft_trainer.model 140 | sft_train_dataset = sft_trainer.train_dataset 141 | sft_formatting_func = getattr(sft_trainer, "formatting_func", None) 142 | 143 | sft_model.gradient_checkpointing_enable() 144 | 145 | AcceleratorState._reset_state(True) 146 | GradientState._reset_state() 147 | 148 | fisher_trainer = FisherTrainer( 149 | model=sft_model, 150 | args=sft_args, 151 | train_dataset=sft_train_dataset, 152 | formatting_func=sft_formatting_func, 153 | ) 154 | 155 | fisher_trainer.train() 156 | 157 | for k in fisher: 158 | fisher[k] /= n_steps_ref[0] 159 | 160 | if dist.get_rank() == 0: 161 | save_tensor_dict(fisher, os.path.join(fisher_dir, dataset_name)) 162 | 163 | self.ft_ckpts[dataset_name] = self.ft_ckpts[dataset_name].to("cpu") 164 | cleanup_task_loader(task_loader) 165 | del fisher_trainer 166 | gc.collect() 167 | torch.cuda.empty_cache() 168 | 169 | self.ft_ckpts = [] 170 | torch.cuda.empty_cache() 171 | gc.collect() 172 | 173 | if merge_only: 174 | if not dist.is_initialized() or dist.get_rank() == 0: 175 | # https://github.com/uiuctml/MergeBench/blob/main/merging/clip_merging_code/src/main_fisher.py 176 | model_coeffs = torch.ones(len(exam_datasets)) * model_coeff_value 177 | avg_params = {} 178 | fisher_module_names = {f[:-3] for f in os.listdir(fisher_dirs[0]) if f.endswith(".pt")} 179 | 180 | for n in tqdm(all_param_names, desc='Merging'): 181 | if n in fisher_module_names and not n.startswith('model.embed'): 182 | param_list = [] 183 | fisher_list = [] 184 | 185 | fisher_list = [torch.load(os.path.join(fisher_dirs[model_id], n + ".pt"), map_location="cpu") for model_id in range(len(fisher_dirs))] 186 | param_list = [torch.load(os.path.join(param_dirs[model_id], n + ".pt"), map_location="cpu") for model_id in range(len(fisher_dirs))] 187 | 188 | params = torch.stack(param_list) # [N, *] 189 | fisher = torch.stack(fisher_list) + 1.0e-10 # [N, *] 190 | 191 | coeff = model_coeffs.view(-1, *[1 for _ in range(params.dim() - 1)]).to(params.device) 192 | fisher = fisher.to(params.device) 193 | sum_p = (params * fisher * coeff).sum(0) 194 | denom = (fisher * coeff).sum(0) 195 | avg_p = sum_p / denom 196 | 197 | avg_params[n] = avg_p.cpu() 198 | 199 | del param_list, fisher_list, params, fisher, sum_p, denom, avg_p 200 | torch.cuda.empty_cache() 201 | 202 | # remove intermediate checkpoints 203 | if not keep_checkpoints: 204 | shutil.rmtree(fisher_dir) 205 | shutil.rmtree(param_dir) 206 | 207 | incompatible_params = self.base_model.load_state_dict(avg_params, strict=False) 208 | self.base_model.save_pretrained(save_dir) 209 | self.tokenizer.save_pretrained(save_dir) -------------------------------------------------------------------------------- /merging/merging_methods/localize_utils.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | from tqdm import tqdm 4 | from merging_methods.utils import get_task_vector, vector_to_state_dict 5 | from taskloader import formatting_prompts_func 6 | from trl import SFTTrainer, SFTConfig 7 | from transformers import AutoModelForCausalLM, TrainerCallback 8 | from accelerate import dispatch_model 9 | 10 | class Localizer(): 11 | def __init__(self, trainable_params, pretrained_model, finetuned_model, graft_args, base_model_name): 12 | super().__init__() 13 | 14 | self.params = trainable_params 15 | self.pretrained_model = pretrained_model 16 | self.finetuned_model = finetuned_model 17 | self.graft_args = graft_args 18 | self.base_model_name = base_model_name 19 | 20 | self.pretrained_model.to("cpu") 21 | self.finetuned_model.to("cpu") 22 | self.finetuned_model.eval() 23 | self.pretrained_model.eval() 24 | for param in self.pretrained_model.parameters(): 25 | param.requires_grad = False 26 | for param in self.finetuned_model.parameters(): 27 | param.requires_grad = False 28 | 29 | self.task_vector = get_task_vector(self.finetuned_model, self.pretrained_model) 30 | self.num_params = len(self.task_vector) 31 | 32 | # self.create_binary_masks() 33 | self.mask = self.create_topk_mask() 34 | 35 | 36 | def reset_model(self): 37 | self.model = AutoModelForCausalLM.from_pretrained(self.base_model_name, 38 | torch_dtype="bfloat16", 39 | attn_implementation="flash_attention_2", 40 | device_map='auto') 41 | self.device_map = self.model.hf_device_map 42 | 43 | 44 | def create_topk_mask(self): 45 | 46 | abs_tv = torch.abs(self.task_vector) 47 | k = int(self.graft_args['sparsity'] * abs_tv.numel()) # 1% of the total number of elements 48 | 49 | # Get the k largest values; returns values and their indices 50 | values, indices = torch.topk(abs_tv.view(-1), k) 51 | threshold = values.min() 52 | 53 | mask = torch.zeros_like(self.task_vector, requires_grad=False) 54 | mask[torch.abs(self.task_vector) >= threshold] = self.graft_args['sigmoid_bias'] 55 | # print non-zero count in mask 56 | print('Initial topk sparsity in my mask: ', torch.nonzero(mask).numel() / self.num_params) 57 | 58 | mask[torch.abs(self.task_vector) < threshold] = -self.graft_args['sigmoid_bias'] 59 | # mask[torch.abs(self.task_vector) > threshold] = 1 60 | 61 | return mask 62 | 63 | 64 | def interpolate_model(self, round_, return_mask=False, train=True): 65 | 66 | sigmoid = torch.nn.Sigmoid() 67 | frac = sigmoid(self.mask) 68 | 69 | if round_: 70 | frac = torch.round(frac) 71 | 72 | final_tv = self.task_vector.clone() 73 | final_tv = final_tv * frac 74 | self.model = vector_to_state_dict(final_tv, self.pretrained_model, return_dict=False) 75 | self.pretrained_model = AutoModelForCausalLM.from_pretrained(self.base_model_name, 76 | torch_dtype="bfloat16", 77 | attn_implementation="flash_attention_2") 78 | 79 | if train: 80 | self.model = dispatch_model(self.model, device_map=self.device_map) 81 | 82 | if round_: 83 | proportion = len(torch.nonzero(frac.bool())) / self.num_params 84 | print('Proportion in my mask: ', proportion) 85 | 86 | if return_mask: 87 | return frac, proportion 88 | 89 | 90 | def train_mask(self, dataset, format_keys): 91 | 92 | sigmoid = torch.nn.Sigmoid() 93 | 94 | # Create the interpolated model with the current mask 95 | self.reset_model() 96 | 97 | for i in range(self.graft_args['num_train_epochs']): 98 | print(f"Training epoch {i+1}") 99 | 100 | self.interpolate_model(round_=False) 101 | self.model.train() 102 | for param in self.model.parameters(): 103 | param.requires_grad = True 104 | 105 | training_args = SFTConfig( 106 | per_device_train_batch_size=2, # Minimum batch size 107 | packing=True, 108 | gradient_checkpointing=True, 109 | save_strategy="no", 110 | optim="adamw_torch_fused", 111 | bf16=True, 112 | report_to=None, 113 | do_eval=False, 114 | num_train_epochs=self.graft_args['num_train_epochs'], 115 | output_dir="output", 116 | max_seq_length=3072, 117 | ) 118 | 119 | # Create SFTTrainer 120 | trainer = SFTTrainer( 121 | model=self.model, 122 | args=training_args, 123 | train_dataset=dataset, 124 | formatting_func=lambda examples: formatting_prompts_func( 125 | examples, **format_keys 126 | ), 127 | ) 128 | 129 | # Define a callback to track gradients during training 130 | class GradientTrackingCallback(TrainerCallback): 131 | def __init__(self): 132 | self.accumulated_grads = {} 133 | self.num_backward_calls = 0 134 | 135 | def on_optimizer_step(self, args, state, control, model, **kwargs): 136 | self.num_backward_calls += 1 137 | for name, param in model.named_parameters(): 138 | # if 'embed' not in name.lower() and 'lm_head' not in name.lower(): 139 | if 'embed' not in name.lower(): 140 | if name not in self.accumulated_grads: 141 | self.accumulated_grads[name] = param.grad.to('cpu').detach().clone() 142 | else: 143 | self.accumulated_grads[name] += param.grad.to('cpu').detach().clone() 144 | return control 145 | 146 | def get_total_grads(self): 147 | grad_vector = torch.cat([grad.flatten() for k, grad in self.accumulated_grads.items()]) 148 | return grad_vector 149 | 150 | # Convert accumulated gradients dict to a single tensor 151 | gradient_callback = GradientTrackingCallback() 152 | trainer.add_callback(gradient_callback) 153 | 154 | # Train for one epoch 155 | trainer.train() 156 | 157 | # gradient of the loss with respect to the model 158 | grad = gradient_callback.get_total_grads() 159 | grad = grad * self.task_vector 160 | 161 | # Reset model for next epoch 162 | self.reset_model() 163 | 164 | # Take the gradient step to update the mask 165 | with torch.no_grad(): 166 | # gradient of the model with respect to the mask 167 | derivative = sigmoid(self.mask) * (1 - sigmoid(self.mask)) 168 | reg_term = self.graft_args['l1_strength'] * torch.where(self.mask > 0, derivative, -derivative) 169 | grad.to(self.mask.device) 170 | # print("total_grad: ", (total_grad * derivative).mean()) 171 | print(self.graft_args['lr'] * grad * derivative - reg_term) 172 | self.mask -= self.graft_args['lr'] * grad * derivative - reg_term 173 | print("Gradient step on mask complete") 174 | 175 | cur_mask = self.mask.clone() 176 | cur_mask = torch.round(sigmoid(cur_mask)) 177 | print('Proportion in my mask: ', len(torch.nonzero(cur_mask.bool())) / self.num_params) 178 | 179 | 180 | class Stitcher(nn.Module): 181 | def __init__(self, trainable_params, model, pretrained_model, finetuned_models, masks): 182 | super().__init__() 183 | self.params = trainable_params 184 | self.pretrained_model = pretrained_model 185 | self.finetuned_models = finetuned_models 186 | self.model = model 187 | 188 | self.masks = masks 189 | if len(self.masks) > 1: 190 | self.masks = self.get_average_masks() 191 | self.task_vector = torch.zeros_like(get_task_vector(self.finetuned_models[0], self.pretrained_model)) 192 | 193 | 194 | def get_average_masks(self): 195 | 196 | def reciprocal_with_zero(tensor): 197 | mask = tensor == 0 198 | reciprocal = torch.reciprocal(tensor) 199 | reciprocal = reciprocal.masked_fill(mask, 0) 200 | return reciprocal 201 | 202 | output_masks = [] 203 | for i in range(len(self.masks)): 204 | output_mask = self.masks[i].clone().detach() 205 | for j in range(len(self.masks)): 206 | if i == j: continue 207 | intersect = torch.logical_and(self.masks[i], self.masks[j]) 208 | output_mask = output_mask + intersect 209 | output_mask = reciprocal_with_zero(output_mask) 210 | output_masks.append(output_mask) 211 | 212 | return output_masks 213 | 214 | 215 | def interpolate_models(self): 216 | 217 | for finetuned_model, mask in zip(self.finetuned_models, self.masks): 218 | with torch.no_grad(): 219 | self.task_vector += mask * get_task_vector(finetuned_model, self.pretrained_model) 220 | 221 | self.model = vector_to_state_dict(self.task_vector, self.pretrained_model, return_dict=False) 222 | 223 | return self.model 224 | -------------------------------------------------------------------------------- /merging/merging_methods/regmean_plusplus.py: -------------------------------------------------------------------------------- 1 | import os 2 | import gc 3 | import shutil 4 | from collections import defaultdict 5 | 6 | from tqdm import tqdm 7 | 8 | import torch 9 | from torch import nn 10 | 11 | from merging_methods.utils import * 12 | from merging_methods.merger import Merger 13 | 14 | import sys 15 | sys.path.append('/MergeBench/merging') 16 | from .regmean_utils import * 17 | from taskloader import * 18 | 19 | 20 | ARCHITECTURE_MODULE_MAP = { 21 | "LlamaForCausalLM": { 22 | "transformer_layers_string": "model.layers", 23 | "embedding_layer_string": "model.embed_tokens", 24 | "lm_head_string": "lm_head", 25 | }, 26 | "Gemma2ForCausalLM": { 27 | "transformer_layers_string": "model.layers", 28 | "embedding_layer_string": "model.embed_tokens", 29 | "lm_head_string": "lm_head", 30 | } 31 | } 32 | 33 | 34 | class RegMeanPlusPlus(Merger): 35 | def __init__(self, base_model, ft_models, save_path): 36 | super().__init__(base_model, ft_models, save_path) 37 | self.merged_model = self.base_model 38 | self.merged_model.config.use_cache = False # Don't need to store past key values as we are not generating text 39 | self.number_of_layers = self.merged_model.config.num_hidden_layers 40 | 41 | self.num_finetuned_models = len(self.ft_ckpts) 42 | 43 | module_map = ARCHITECTURE_MODULE_MAP[self.base_model.config.architectures[0]] 44 | self.transformer_layers_string = module_map["transformer_layers_string"] 45 | self.embedding_layer_string = module_map["embedding_layer_string"] 46 | self.lm_head_string = module_map["lm_head_string"] 47 | 48 | self.post_init() 49 | 50 | def post_init(self): 51 | # Init the merged model by weight averaging for weights other than the transformer layers, embedding layers, and LM head 52 | merged_model_param_names = self.merged_model.state_dict().keys() 53 | merged_model_param_names = [name for name in merged_model_param_names if not (name.startswith(self.transformer_layers_string) or name.startswith(self.embedding_layer_string) or name.startswith(self.lm_head_string))] 54 | 55 | for name in tqdm(merged_model_param_names, desc='Init the merged model by weight averaging'): 56 | merged_param = torch.mean(torch.stack([self.ft_ckpts[i].state_dict()[name] for i in range(self.num_finetuned_models)]), dim=0) 57 | self.merged_model.state_dict()[name].copy_(merged_param) 58 | 59 | def get_first_layer_input(self, model, trainer, dataloader): 60 | first_layer_input_batch = [] 61 | 62 | # https://docs.pytorch.org/docs/stable/generated/torch.nn.Module.html 63 | def hook(module, args, kwargs, output): 64 | for k, v in kwargs.items(): 65 | if isinstance(v, torch.Tensor): kwargs[k] = v.detach().cpu() # for 'hidden_states' (if it's explicitly passed with keyword arguments), 'attention_mask', 'position_ids', 'cache_position' 66 | elif isinstance(v, tuple): kwargs[k] = tuple([vv.detach().cpu() for vv in v]) # for 'position_embeddings' 67 | else: kwargs[k] = v # for 'past_key_values' 68 | 69 | if len(args) > 0: 70 | first_layer_input_batch.append({"hidden_states": args[0].detach().cpu(), **kwargs}) 71 | else: 72 | first_layer_input_batch.append(kwargs) 73 | 74 | model.to(trainer.args.device) 75 | model.config.num_hidden_layers = 1 76 | 77 | handle = eval(f"model.{self.transformer_layers_string}[0]").register_forward_hook(hook, with_kwargs=True) 78 | 79 | total = len(dataloader) 80 | for inputs in tqdm(dataloader, total=total, desc="Get (merged) model's 1st layer input"): 81 | inputs = trainer._prepare_inputs(inputs) 82 | _ = model(**inputs) 83 | 84 | handle.remove() 85 | 86 | model.to("cpu") 87 | model.config.num_hidden_layers = self.number_of_layers 88 | 89 | return first_layer_input_batch 90 | 91 | def forward_layer(self, layer, task_input): 92 | device = "cuda:0" if torch.cuda.is_available() else "cpu" 93 | layer.to(device) 94 | 95 | for batch_idx, inputs in enumerate(task_input): 96 | inputs = send_inputs_to_device(inputs, device) 97 | hidden_states = layer(**inputs) 98 | if type(hidden_states) == tuple: 99 | hidden_states = hidden_states[0] 100 | task_input[batch_idx]['hidden_states'] = hidden_states.detach().cpu() 101 | 102 | inputs = send_inputs_to_device(inputs, "cpu") 103 | 104 | layer.to("cpu") 105 | 106 | return task_input 107 | 108 | def merge(self, **kwargs): 109 | reduction = kwargs["reduction"] 110 | 111 | exam_datasets = kwargs["task_names"].split("-") 112 | 113 | save_dir = self.save_path 114 | gram_dir = os.path.join(save_dir, "regmeanplusplus") 115 | param_dir = os.path.join(save_dir, "params") 116 | 117 | gram_dirs = [os.path.join(gram_dir, dataset_name) for dataset_name in exam_datasets] 118 | param_dirs = [os.path.join(param_dir, dataset_name) for dataset_name in exam_datasets] 119 | 120 | # 1. Compute inputs for 1st layer 121 | task_inputs = defaultdict(list) 122 | for idx, dataset_name in enumerate(exam_datasets): 123 | task_loader = TaskLoader(dataset_name, self.merged_model, self.tokenizer) 124 | trainer = task_loader.trainer 125 | dataloader = trainer.get_train_dataloader() 126 | with torch.no_grad(): 127 | task_inputs[dataset_name] = self.get_first_layer_input( 128 | self.merged_model, 129 | trainer, 130 | dataloader 131 | ) 132 | 133 | cleanup_task_loader(task_loader) 134 | torch.cuda.empty_cache() 135 | gc.collect() 136 | gc.collect() 137 | 138 | # 2. Merge each layer 139 | for layer_idx in tqdm(range(self.number_of_layers), desc="Merging layers"): 140 | # 2.1. Compute grams for each finetuned model 141 | for idx, dataset_name in enumerate(exam_datasets): 142 | finetuned_layer = eval(f"self.ft_ckpts[{idx}].{self.transformer_layers_string}")[layer_idx] 143 | with torch.no_grad(): 144 | grams = compute_grams(None, finetuned_layer, task_inputs[dataset_name]) 145 | save_tensor_dict(grams, os.path.join(gram_dir, dataset_name)) # contains most (linear) params grams 146 | save_tensor_dict(finetuned_layer.state_dict(), os.path.join(param_dir, dataset_name)) # contains all params 147 | 148 | finetuned_layer.to("cpu") 149 | del finetuned_layer, grams 150 | torch.cuda.empty_cache() 151 | gc.collect() 152 | gc.collect() 153 | 154 | layer_param_names = eval(f"self.merged_model.{self.transformer_layers_string}")[layer_idx].state_dict().keys() 155 | 156 | # 2.2. Merge parameters for this layer 157 | with torch.no_grad(): 158 | gram_module_names = {f[:-3] for f in os.listdir(gram_dirs[0]) if f.endswith(".pt")} 159 | avg_params = {} 160 | for name in layer_param_names: 161 | h_avged = False 162 | if name.endswith('.weight') and not name.startswith('lm_head'): 163 | module_name = name[:-len('.weight')] 164 | if module_name in gram_module_names: 165 | sum_gram, grams = None, None 166 | for model_id in range(len(gram_dirs)): 167 | param_grams = torch.load(os.path.join(gram_dirs[model_id], module_name + ".pt"), map_location='cpu').detach() 168 | param_grams = reduce_non_diag(param_grams, a=reduction) # avoid degeneration 169 | param = torch.load(os.path.join(param_dirs[model_id], name + ".pt"), map_location='cpu').detach() 170 | gram_m_w = torch.matmul(param_grams, param.transpose(0, 1)) 171 | if sum_gram is None: 172 | sum_gram = param_grams.clone() 173 | sum_gram_m_ws = gram_m_w.clone() 174 | else: 175 | sum_gram.add_(param_grams) 176 | sum_gram_m_ws.add_(gram_m_w) 177 | del param_grams, param, gram_m_w 178 | gc.collect() 179 | sum_gram_f32 = sum_gram.to(dtype=torch.float32) 180 | cond_number = torch.linalg.cond(sum_gram_f32) 181 | threshold = 1e8 182 | if cond_number > threshold or torch.any(torch.diag(sum_gram_f32) == 0): 183 | sum_gram_inv = torch.linalg.pinv(sum_gram_f32).to(dtype=sum_gram_m_ws.dtype) 184 | else: 185 | sum_gram_inv = torch.inverse(sum_gram_f32).to(dtype=sum_gram_m_ws.dtype) 186 | wt = torch.matmul(sum_gram_inv, sum_gram_m_ws) 187 | avg_params[name] = wt.transpose(0, 1) 188 | h_avged = True 189 | 190 | if not h_avged: # if not averaged with regmean, then do simple avg 191 | filtered_model_params = None 192 | for model_id in range(len(gram_dirs)): 193 | if not name.startswith('model.embed') and not name.startswith('lm_head'): # embed_tokens.weight have incompatible dimensions due to vocab size difference 194 | filtered_model_param = torch.load(os.path.join(param_dirs[model_id], name + ".pt"), map_location='cpu').detach() 195 | if filtered_model_params is None: 196 | filtered_model_params = filtered_model_param.clone() 197 | else: 198 | filtered_model_params.add_(filtered_model_param) 199 | del filtered_model_param 200 | gc.collect() 201 | avg_params[name] = filtered_model_params.div(len(gram_dirs)) 202 | 203 | eval(f"self.merged_model.{self.transformer_layers_string}")[layer_idx].load_state_dict(avg_params, strict=False) 204 | avg_params = {} 205 | del avg_params 206 | 207 | shutil.rmtree(gram_dir) 208 | shutil.rmtree(param_dir) 209 | 210 | # 2.3. Compute inputs for next layer 211 | if layer_idx == self.number_of_layers - 1: 212 | task_inputs = {} 213 | del task_inputs 214 | continue 215 | 216 | # # May be just need to update 'hidden_states' for task_inputs[dataset_name] 217 | # # Check 'past_key_values' 218 | for idx, dataset_name in enumerate(exam_datasets): 219 | with torch.no_grad(): 220 | task_inputs[dataset_name] = self.forward_layer( 221 | eval(f"self.merged_model.{self.transformer_layers_string}")[layer_idx], 222 | task_inputs[dataset_name] 223 | ) 224 | torch.cuda.empty_cache() 225 | gc.collect() 226 | 227 | gc.collect() 228 | 229 | self.merged_model.save_pretrained(save_dir) 230 | self.tokenizer.save_pretrained(save_dir) 231 | -------------------------------------------------------------------------------- /merging/taskloader.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | from datasets import load_dataset 4 | from trl import SFTConfig, SFTTrainer 5 | 6 | cache_dir = os.getenv('HF_HOME', '/data/huggingface') 7 | 8 | def formatting_prompts_func(examples, instruction_key='instruction', input_key='input', output_key='output'): 9 | # alpaca style prompts 10 | # also works for gpteacher because gpteacher inherits alpaca prompt 11 | # https://github.com/huggingface/trl/pull/444#issue-1760952763 12 | instruction = examples[instruction_key] 13 | if 'input' in examples: 14 | input_text = examples[input_key] 15 | else: 16 | input_text = '' 17 | response = examples[output_key] 18 | 19 | if len(input_text) > 0: 20 | text = f'''Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. 21 | 22 | ### Instruction: 23 | {instruction} 24 | 25 | ### Input: 26 | {input_text} 27 | 28 | ### Response: 29 | {response} 30 | ''' 31 | else: 32 | text = f'''Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. 33 | 34 | ### Instruction: 35 | {instruction} 36 | 37 | ### Response: 38 | {response} 39 | ''' 40 | 41 | return text 42 | 43 | 44 | class TaskLoader: 45 | def __new__(cls, task_name, *args, **kwargs): 46 | if task_name in globals() and issubclass(globals()[task_name], cls): 47 | subclass = globals()[task_name] 48 | return super().__new__(subclass) 49 | else: 50 | raise ValueError(f"Invalid task name: {task_name}") 51 | 52 | def __init__(self, task_name, *args, **kwargs): 53 | self.task_name = task_name 54 | 55 | 56 | class WildguardMix(TaskLoader): 57 | def __init__(self, task_name, model, tokenizer, sample_size=None): 58 | super().__init__(task_name, model, tokenizer, sample_size=sample_size) 59 | 60 | self.training_args = SFTConfig( 61 | learning_rate=1e-5, 62 | num_train_epochs=1, 63 | lr_scheduler_type='cosine', 64 | optim="adamw_torch", 65 | bf16=True, 66 | dataset_num_proc=48, 67 | packing=False, 68 | max_length=2048, # 4096 69 | gradient_checkpointing=True, 70 | per_device_train_batch_size=1, 71 | # deepspeed='/home/cindy2000_sh/MergeBench/deepspeed_configs/zero3.json', 72 | output_dir="./tmp", 73 | save_strategy='no', 74 | ) 75 | 76 | self.training_dataset = load_dataset('MergeBench/safety_val',cache_dir=cache_dir) 77 | self.training_dataset = self.training_dataset.rename_column("prompt", "query") 78 | 79 | if sample_size is None: 80 | self.training_dataset = self.training_dataset["train"] 81 | else: 82 | self.training_dataset = self.training_dataset["train"].shuffle(seed=42).select(range(sample_size)) 83 | self.trainer = SFTTrainer(model=model, 84 | args=self.training_args, 85 | train_dataset=self.training_dataset, 86 | formatting_func=lambda examples: formatting_prompts_func( 87 | examples, instruction_key="query", output_key="response" 88 | ), 89 | ) 90 | 91 | 92 | class MagiCoder(TaskLoader): 93 | def __init__(self, task_name, model, tokenizer, sample_size=None): 94 | super().__init__(task_name, model, tokenizer, sample_size=sample_size) 95 | 96 | self.training_args = SFTConfig( 97 | learning_rate=1e-5, 98 | num_train_epochs=1, 99 | lr_scheduler_type='cosine', 100 | optim="adamw_torch", 101 | bf16=True, 102 | dataset_num_proc=48, 103 | packing=False, 104 | max_length=2048, # 4096 105 | gradient_checkpointing=True, 106 | per_device_train_batch_size=1, 107 | # deepspeed='/home/cindy2000_sh/MergeBench/deepspeed_configs/zero3.json', 108 | output_dir="./tmp", 109 | save_strategy='no', 110 | ) 111 | 112 | self.training_dataset = load_dataset('MergeBench/coding_val',cache_dir=cache_dir) 113 | 114 | if sample_size is None: 115 | self.training_dataset = self.training_dataset["train"] 116 | else: 117 | self.training_dataset = self.training_dataset["train"].shuffle(seed=42).select(range(sample_size)) 118 | self.trainer = SFTTrainer(model=model, 119 | args=self.training_args, 120 | train_dataset=self.training_dataset, 121 | formatting_func=lambda examples: formatting_prompts_func( 122 | examples, output_key="response" 123 | ), 124 | ) 125 | 126 | 127 | class Aya(TaskLoader): 128 | # TODO: match with Yuzheng's config 129 | def __init__(self, task_name, model, tokenizer, sample_size=None): 130 | super().__init__(task_name, model, tokenizer, sample_size=sample_size) 131 | 132 | self.training_args = SFTConfig( 133 | learning_rate=2e-5, 134 | num_train_epochs=1, 135 | lr_scheduler_type='cosine', 136 | optim="adamw_torch", 137 | bf16=True, 138 | dataset_num_proc=48, 139 | packing=False, 140 | max_length=2048, 141 | gradient_checkpointing=True, 142 | per_device_train_batch_size=1, 143 | # deepspeed='/home/cindy2000_sh/MergeBench/deepspeed_configs/zero3.json', 144 | output_dir="./tmp", 145 | save_strategy='no', 146 | ) 147 | 148 | self.training_dataset = load_dataset('MergeBench/multilingual_val',cache_dir=cache_dir) 149 | if sample_size is None: 150 | self.training_dataset = self.training_dataset["train"] 151 | else: 152 | self.training_dataset = self.training_dataset["train"].shuffle(seed=42).select(range(sample_size)) 153 | self.trainer = SFTTrainer(model=model, 154 | args=self.training_args, 155 | train_dataset=self.training_dataset, 156 | formatting_func=lambda examples: formatting_prompts_func( 157 | examples, instruction_key="inputs", output_key="targets" 158 | ), 159 | ) 160 | 161 | 162 | class DartMath(TaskLoader): 163 | def __init__(self, task_name, model, tokenizer, sample_size=None): 164 | super().__init__(task_name, model, tokenizer, sample_size=sample_size) 165 | 166 | self.training_args = SFTConfig( 167 | learning_rate=1e-5, 168 | num_train_epochs=1, 169 | lr_scheduler_type='cosine', 170 | optim="adamw_torch", 171 | bf16=True, 172 | dataset_num_proc=48, 173 | packing=False, 174 | max_length=2048, 175 | gradient_checkpointing=True, 176 | per_device_train_batch_size=1, 177 | # deepspeed='/home/cindy2000_sh/MergeBench/deepspeed_configs/zero3.json', 178 | output_dir="./tmp", 179 | save_strategy='no', 180 | ) 181 | 182 | self.training_dataset = load_dataset('MergeBench/math_val',cache_dir=cache_dir) 183 | 184 | if sample_size is None: 185 | self.training_dataset = self.training_dataset["train"] 186 | else: 187 | self.training_dataset = self.training_dataset["train"].shuffle(seed=42).select(range(sample_size)) 188 | self.trainer = SFTTrainer(model=model, 189 | args=self.training_args, 190 | train_dataset=self.training_dataset, 191 | formatting_func=lambda examples: formatting_prompts_func( 192 | examples, instruction_key="query", output_key="response" 193 | ), 194 | ) 195 | 196 | class Tulu3IF(TaskLoader): 197 | def __init__(self, task_name, model, tokenizer, sample_size=None): 198 | super().__init__(task_name, model, tokenizer, sample_size=sample_size) 199 | 200 | self.training_args = SFTConfig( 201 | learning_rate=1e-5, 202 | num_train_epochs=1, 203 | lr_scheduler_type='cosine', 204 | optim="adamw_torch", 205 | bf16=True, 206 | dataset_num_proc=48, 207 | packing=False, 208 | max_length=2048, 209 | gradient_checkpointing=True, 210 | per_device_train_batch_size=1, 211 | # deepspeed='/home/cindy2000_sh/MergeBench/deepspeed_configs/zero3.json', 212 | output_dir="./tmp", 213 | save_strategy='no', 214 | ) 215 | 216 | self.training_dataset = load_dataset('MergeBench/instruction_val',cache_dir=cache_dir) 217 | 218 | if sample_size is None: 219 | self.training_dataset = self.training_dataset['train'] 220 | else: 221 | self.training_dataset = self.training_dataset['train'].shuffle(seed=42).select(range(sample_size)) 222 | 223 | 224 | self.trainer = SFTTrainer(model=model, 225 | args=self.training_args, 226 | train_dataset=self.training_dataset, 227 | formatting_func=lambda examples: formatting_prompts_func( 228 | examples 229 | ), 230 | ) 231 | 232 | if __name__ == "__main__": 233 | from transformers import AutoTokenizer, AutoModelForCausalLM 234 | 235 | tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-3B") 236 | model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-3B", cache_dir=cache_dir) 237 | 238 | task_preprocessor = TaskLoader('WildguardMix', model, tokenizer, sample_size=None) 239 | # task_preprocessor = TaskLoader('MagiCoder', model, tokenizer, sample_size=None) 240 | # task_preprocessor = TaskLoader('Aya', model, tokenizer, sample_size=None) 241 | # task_preprocessor = TaskLoader('DartMath', model, tokenizer, sample_size=None) 242 | # task_preprocessor = TaskLoader('Tulu3IF', model, tokenizer, sample_size=None) 243 | -------------------------------------------------------------------------------- /merged_models/Llama-3.1-8B_merged/RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1/lm_eval.json: -------------------------------------------------------------------------------- 1 | { 2 | "results": { 3 | "arc_de": { 4 | "alias": "arc_de", 5 | "acc,none": 0.3746792130025663, 6 | "acc_stderr,none": 0.01416314857981058, 7 | "acc_norm,none": 0.41317365269461076, 8 | "acc_norm_stderr,none": 0.01440786699505746 9 | }, 10 | "arc_es": { 11 | "alias": "arc_es", 12 | "acc,none": 0.43504273504273505, 13 | "acc_stderr,none": 0.014499949963905008, 14 | "acc_norm,none": 0.4478632478632479, 15 | "acc_norm_stderr,none": 0.01454416474185364 16 | }, 17 | "arc_fr": { 18 | "alias": "arc_fr", 19 | "acc,none": 0.43199315654405473, 20 | "acc_stderr,none": 0.014494184864971343, 21 | "acc_norm,none": 0.4473909324208725, 22 | "acc_norm_stderr,none": 0.014548933904137591 23 | }, 24 | "arc_ru": { 25 | "alias": "arc_ru", 26 | "acc,none": 0.3652694610778443, 27 | "acc_stderr,none": 0.014088993137853638, 28 | "acc_norm,none": 0.41146278870829767, 29 | "acc_norm_stderr,none": 0.014398950037131911 30 | }, 31 | "gsm8k_cot": { 32 | "alias": "gsm8k_cot", 33 | "exact_match,strict-match": 0.6580742987111448, 34 | "exact_match_stderr,strict-match": 0.013066089625182818, 35 | "exact_match,flexible-extract": 0.733131159969674, 36 | "exact_match_stderr,flexible-extract": 0.012183780551887959 37 | }, 38 | "hellaswag_de": { 39 | "alias": "hellaswag_de", 40 | "acc,none": 0.48484201537147736, 41 | "acc_stderr,none": 0.005163807946876662, 42 | "acc_norm,none": 0.6253202391118702, 43 | "acc_norm_stderr,none": 0.005001279196555146 44 | }, 45 | "hellaswag_es": { 46 | "alias": "hellaswag_es", 47 | "acc,none": 0.5306165991039045, 48 | "acc_stderr,none": 0.005154837402482832, 49 | "acc_norm,none": 0.6901002773629187, 50 | "acc_norm_stderr,none": 0.004776693619654075 51 | }, 52 | "hellaswag_fr": { 53 | "alias": "hellaswag_fr", 54 | "acc,none": 0.5197044334975369, 55 | "acc_stderr,none": 0.005170455686499735, 56 | "acc_norm,none": 0.6726279717284215, 57 | "acc_norm_stderr,none": 0.0048562894826775825 58 | }, 59 | "hellaswag_ru": { 60 | "alias": "hellaswag_ru", 61 | "acc,none": 0.47249784296807595, 62 | "acc_stderr,none": 0.005184999806361907, 63 | "acc_norm,none": 0.6024590163934426, 64 | "acc_norm_stderr,none": 0.005082664197928633 65 | }, 66 | "hendrycks_math": { 67 | "exact_match,none": 0.0022, 68 | "exact_match_stderr,none": 0.0006628553260613068, 69 | "alias": "hendrycks_math" 70 | }, 71 | "hendrycks_math_algebra": { 72 | "alias": " - hendrycks_math_algebra", 73 | "exact_match,none": 0.003369839932603201, 74 | "exact_match_stderr,none": 0.0016827876052283514 75 | }, 76 | "hendrycks_math_counting_and_prob": { 77 | "alias": " - hendrycks_math_counting_and_prob", 78 | "exact_match,none": 0.002109704641350211, 79 | "exact_match_stderr,none": 0.0021097046413502104 80 | }, 81 | "hendrycks_math_geometry": { 82 | "alias": " - hendrycks_math_geometry", 83 | "exact_match,none": 0.0020876826722338203, 84 | "exact_match_stderr,none": 0.0020876826722338216 85 | }, 86 | "hendrycks_math_intermediate_algebra": { 87 | "alias": " - hendrycks_math_intermediate_algebra", 88 | "exact_match,none": 0.0011074197120708748, 89 | "exact_match_stderr,none": 0.0011074197120708852 90 | }, 91 | "hendrycks_math_num_theory": { 92 | "alias": " - hendrycks_math_num_theory", 93 | "exact_match,none": 0.0, 94 | "exact_match_stderr,none": 0.0 95 | }, 96 | "hendrycks_math_prealgebra": { 97 | "alias": " - hendrycks_math_prealgebra", 98 | "exact_match,none": 0.003444316877152698, 99 | "exact_match_stderr,none": 0.0019862902400464752 100 | }, 101 | "hendrycks_math_precalc": { 102 | "alias": " - hendrycks_math_precalc", 103 | "exact_match,none": 0.0018315018315018315, 104 | "exact_match_stderr,none": 0.0018315018315018376 105 | }, 106 | "ifeval": { 107 | "alias": "ifeval", 108 | "prompt_level_strict_acc,none": 0.11090573012939002, 109 | "prompt_level_strict_acc_stderr,none": 0.013513069747049506, 110 | "inst_level_strict_acc,none": 0.19184652278177458, 111 | "inst_level_strict_acc_stderr,none": "N/A", 112 | "prompt_level_loose_acc,none": 0.133086876155268, 113 | "prompt_level_loose_acc_stderr,none": 0.014617009342904507, 114 | "inst_level_loose_acc,none": 0.22182254196642687, 115 | "inst_level_loose_acc_stderr,none": "N/A" 116 | }, 117 | "m_mmlu_de": { 118 | "alias": "m_mmlu_de", 119 | "acc,none": 0.5123698898778096, 120 | "acc_stderr,none": 0.0043412463290572224 121 | }, 122 | "m_mmlu_es": { 123 | "alias": "m_mmlu_es", 124 | "acc,none": 0.5345732713364332, 125 | "acc_stderr,none": 0.00431981691610148 126 | }, 127 | "m_mmlu_fr": { 128 | "alias": "m_mmlu_fr", 129 | "acc,none": 0.5292949354518371, 130 | "acc_stderr,none": 0.00436268123167117 131 | }, 132 | "m_mmlu_ru": { 133 | "alias": "m_mmlu_ru", 134 | "acc,none": 0.4891212424079342, 135 | "acc_stderr,none": 0.00438324059821865 136 | } 137 | }, 138 | "groups": { 139 | "hendrycks_math": { 140 | "exact_match,none": 0.0022, 141 | "exact_match_stderr,none": 0.0006628553260613068, 142 | "alias": "hendrycks_math" 143 | } 144 | }, 145 | "group_subtasks": { 146 | "arc_de": [], 147 | "arc_es": [], 148 | "arc_fr": [], 149 | "arc_ru": [], 150 | "gsm8k_cot": [], 151 | "hellaswag_de": [], 152 | "hellaswag_es": [], 153 | "hellaswag_fr": [], 154 | "hellaswag_ru": [], 155 | "hendrycks_math": [ 156 | "hendrycks_math_algebra", 157 | "hendrycks_math_counting_and_prob", 158 | "hendrycks_math_geometry", 159 | "hendrycks_math_intermediate_algebra", 160 | "hendrycks_math_num_theory", 161 | "hendrycks_math_prealgebra", 162 | "hendrycks_math_precalc" 163 | ], 164 | "ifeval": [], 165 | "m_mmlu_de": [], 166 | "m_mmlu_es": [], 167 | "m_mmlu_fr": [], 168 | "m_mmlu_ru": [] 169 | }, 170 | "configs": { 171 | "arc_de": { 172 | "task": "arc_de", 173 | "tag": [ 174 | "arc_multilingual" 175 | ], 176 | "dataset_path": "alexandrainst/m_arc", 177 | "dataset_name": "de", 178 | "training_split": "train", 179 | "validation_split": "validation", 180 | "test_split": "test", 181 | "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc):\n # breakpoint()\n out_doc = {\n \"id\": doc[\"id\"],\n \"query\": \"Question: \" + preprocess(doc[\"instruction\"]) + \"\\nAnswer:\",\n \"choices\": [\n preprocess(option)\n for option in [\n doc[\"option_a\"],\n doc[\"option_b\"],\n doc[\"option_c\"],\n doc[\"option_d\"],\n doc[\"option_e\"],\n ]\n if option\n ],\n \"gold\": [\"A\", \"B\", \"C\", \"D\", \"E\"].index(doc[\"answer\"]),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n", 182 | "doc_to_text": "query", 183 | "doc_to_target": "gold", 184 | "unsafe_code": false, 185 | "doc_to_choice": "choices", 186 | "description": "", 187 | "target_delimiter": " ", 188 | "fewshot_delimiter": "\n\n", 189 | "num_fewshot": 0, 190 | "metric_list": [ 191 | { 192 | "metric": "acc", 193 | "aggregation": "mean", 194 | "higher_is_better": true 195 | }, 196 | { 197 | "metric": "acc_norm", 198 | "aggregation": "mean", 199 | "higher_is_better": true 200 | } 201 | ], 202 | "output_type": "multiple_choice", 203 | "repeats": 1, 204 | "should_decontaminate": true, 205 | "doc_to_decontamination_query": "query", 206 | "metadata": { 207 | "version": 2.0, 208 | "pretrained": "merged_models/Llama-3.1-8B_merged/RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1" 209 | } 210 | }, 211 | "arc_es": { 212 | "task": "arc_es", 213 | "tag": [ 214 | "arc_multilingual" 215 | ], 216 | "dataset_path": "alexandrainst/m_arc", 217 | "dataset_name": "es", 218 | "training_split": "train", 219 | "validation_split": "validation", 220 | "test_split": "test", 221 | "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc):\n # breakpoint()\n out_doc = {\n \"id\": doc[\"id\"],\n \"query\": \"Question: \" + preprocess(doc[\"instruction\"]) + \"\\nAnswer:\",\n \"choices\": [\n preprocess(option)\n for option in [\n doc[\"option_a\"],\n doc[\"option_b\"],\n doc[\"option_c\"],\n doc[\"option_d\"],\n doc[\"option_e\"],\n ]\n if option\n ],\n \"gold\": [\"A\", \"B\", \"C\", \"D\", \"E\"].index(doc[\"answer\"]),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n", 222 | "doc_to_text": "query", 223 | "doc_to_target": "gold", 224 | "unsafe_code": false, 225 | "doc_to_choice": "choices", 226 | "description": "", 227 | "target_delimiter": " ", 228 | "fewshot_delimiter": "\n\n", 229 | "num_fewshot": 0, 230 | "metric_list": [ 231 | { 232 | "metric": "acc", 233 | "aggregation": "mean", 234 | "higher_is_better": true 235 | }, 236 | { 237 | "metric": "acc_norm", 238 | "aggregation": "mean", 239 | "higher_is_better": true 240 | } 241 | ], 242 | "output_type": "multiple_choice", 243 | "repeats": 1, 244 | "should_decontaminate": true, 245 | "doc_to_decontamination_query": "query", 246 | "metadata": { 247 | "version": 2.0, 248 | "pretrained": "merged_models/Llama-3.1-8B_merged/RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1" 249 | } 250 | }, 251 | "arc_fr": { 252 | "task": "arc_fr", 253 | "tag": [ 254 | "arc_multilingual" 255 | ], 256 | "dataset_path": "alexandrainst/m_arc", 257 | "dataset_name": "fr", 258 | "training_split": "train", 259 | "validation_split": "validation", 260 | "test_split": "test", 261 | "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc):\n # breakpoint()\n out_doc = {\n \"id\": doc[\"id\"],\n \"query\": \"Question: \" + preprocess(doc[\"instruction\"]) + \"\\nAnswer:\",\n \"choices\": [\n preprocess(option)\n for option in [\n doc[\"option_a\"],\n doc[\"option_b\"],\n doc[\"option_c\"],\n doc[\"option_d\"],\n doc[\"option_e\"],\n ]\n if option\n ],\n \"gold\": [\"A\", \"B\", \"C\", \"D\", \"E\"].index(doc[\"answer\"]),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n", 262 | "doc_to_text": "query", 263 | "doc_to_target": "gold", 264 | "unsafe_code": false, 265 | "doc_to_choice": "choices", 266 | "description": "", 267 | "target_delimiter": " ", 268 | "fewshot_delimiter": "\n\n", 269 | "num_fewshot": 0, 270 | "metric_list": [ 271 | { 272 | "metric": "acc", 273 | "aggregation": "mean", 274 | "higher_is_better": true 275 | }, 276 | { 277 | "metric": "acc_norm", 278 | "aggregation": "mean", 279 | "higher_is_better": true 280 | } 281 | ], 282 | "output_type": "multiple_choice", 283 | "repeats": 1, 284 | "should_decontaminate": true, 285 | "doc_to_decontamination_query": "query", 286 | "metadata": { 287 | "version": 2.0, 288 | "pretrained": "merged_models/Llama-3.1-8B_merged/RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1" 289 | } 290 | }, 291 | "arc_ru": { 292 | "task": "arc_ru", 293 | "tag": [ 294 | "arc_multilingual" 295 | ], 296 | "dataset_path": "alexandrainst/m_arc", 297 | "dataset_name": "ru", 298 | "training_split": "train", 299 | "validation_split": "validation", 300 | "test_split": "test", 301 | "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc):\n # breakpoint()\n out_doc = {\n \"id\": doc[\"id\"],\n \"query\": \"Question: \" + preprocess(doc[\"instruction\"]) + \"\\nAnswer:\",\n \"choices\": [\n preprocess(option)\n for option in [\n doc[\"option_a\"],\n doc[\"option_b\"],\n doc[\"option_c\"],\n doc[\"option_d\"],\n doc[\"option_e\"],\n ]\n if option\n ],\n \"gold\": [\"A\", \"B\", \"C\", \"D\", \"E\"].index(doc[\"answer\"]),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n", 302 | "doc_to_text": "query", 303 | "doc_to_target": "gold", 304 | "unsafe_code": false, 305 | "doc_to_choice": "choices", 306 | "description": "", 307 | "target_delimiter": " ", 308 | "fewshot_delimiter": "\n\n", 309 | "num_fewshot": 0, 310 | "metric_list": [ 311 | { 312 | "metric": "acc", 313 | "aggregation": "mean", 314 | "higher_is_better": true 315 | }, 316 | { 317 | "metric": "acc_norm", 318 | "aggregation": "mean", 319 | "higher_is_better": true 320 | } 321 | ], 322 | "output_type": "multiple_choice", 323 | "repeats": 1, 324 | "should_decontaminate": true, 325 | "doc_to_decontamination_query": "query", 326 | "metadata": { 327 | "version": 2.0, 328 | "pretrained": "merged_models/Llama-3.1-8B_merged/RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1" 329 | } 330 | }, 331 | "gsm8k_cot": { 332 | "task": "gsm8k_cot", 333 | "tag": [ 334 | "chain_of_thought" 335 | ], 336 | "dataset_path": "gsm8k", 337 | "dataset_name": "main", 338 | "test_split": "test", 339 | "doc_to_text": "Q: {{question}}\nA:", 340 | "doc_to_target": "{{answer.split('####')[-1].strip() if answer is defined else target}}", 341 | "unsafe_code": false, 342 | "description": "", 343 | "target_delimiter": " ", 344 | "fewshot_delimiter": "\n\n", 345 | "fewshot_config": { 346 | "sampler": "first_n", 347 | "samples": [ 348 | { 349 | "question": "There are 15 trees in the grove. Grove workers will plant trees in the grove today. After they are done, there will be 21 trees. How many trees did the grove workers plant today?", 350 | "target": "There are 15 trees originally. Then there were 21 trees after some more were planted. So there must have been 21 - 15 = 6. The answer is 6." 351 | }, 352 | { 353 | "question": "If there are 3 cars in the parking lot and 2 more cars arrive, how many cars are in the parking lot?", 354 | "target": "There are originally 3 cars. 2 more cars arrive. 3 + 2 = 5. The answer is 5." 355 | }, 356 | { 357 | "question": "Leah had 32 chocolates and her sister had 42. If they ate 35, how many pieces do they have left in total?", 358 | "target": "Originally, Leah had 32 chocolates. Her sister had 42. So in total they had 32 + 42 = 74. After eating 35, they had 74 - 35 = 39. The answer is 39." 359 | }, 360 | { 361 | "question": "Jason had 20 lollipops. He gave Denny some lollipops. Now Jason has 12 lollipops. How many lollipops did Jason give to Denny?", 362 | "target": "Jason started with 20 lollipops. Then he had 12 after giving some to Denny. So he gave Denny 20 - 12 = 8. The answer is 8." 363 | }, 364 | { 365 | "question": "Shawn has five toys. For Christmas, he got two toys each from his mom and dad. How many toys does he have now?", 366 | "target": "Shawn started with 5 toys. If he got 2 toys each from his mom and dad, then that is 4 more toys. 5 + 4 = 9. The answer is 9." 367 | }, 368 | { 369 | "question": "There were nine computers in the server room. Five more computers were installed each day, from monday to thursday. How many computers are now in the server room?", 370 | "target": "There were originally 9 computers. For each of 4 days, 5 more computers were added. So 5 * 4 = 20 computers were added. 9 + 20 is 29. The answer is 29." 371 | }, 372 | { 373 | "question": "Michael had 58 golf balls. On tuesday, he lost 23 golf balls. On wednesday, he lost 2 more. How many golf balls did he have at the end of wednesday?", 374 | "target": "Michael started with 58 golf balls. After losing 23 on tuesday, he had 58 - 23 = 35. After losing 2 more, he had 35 - 2 = 33 golf balls. The answer is 33." 375 | }, 376 | { 377 | "question": "Olivia has $23. She bought five bagels for $3 each. How much money does she have left?", 378 | "target": "Olivia had 23 dollars. 5 bagels for 3 dollars each will be 5 x 3 = 15 dollars. So she has 23 - 15 dollars left. 23 - 15 is 8. The answer is 8." 379 | } 380 | ] 381 | }, 382 | "num_fewshot": 8, 383 | "metric_list": [ 384 | { 385 | "aggregation": "mean", 386 | "higher_is_better": true, 387 | "ignore_case": true, 388 | "ignore_punctuation": false, 389 | "metric": "exact_match", 390 | "regexes_to_ignore": [ 391 | ",", 392 | "\\$", 393 | "(?s).*#### ", 394 | "\\.$" 395 | ] 396 | } 397 | ], 398 | "output_type": "generate_until", 399 | "generation_kwargs": { 400 | "do_sample": false, 401 | "until": [ 402 | "Q:", 403 | "", 404 | "<|im_end|>" 405 | ] 406 | }, 407 | "repeats": 1, 408 | "filter_list": [ 409 | { 410 | "filter": [ 411 | { 412 | "function": "regex", 413 | "regex_pattern": "The answer is (\\-?[0-9\\.\\,]+)." 414 | }, 415 | { 416 | "function": "take_first" 417 | } 418 | ], 419 | "name": "strict-match" 420 | }, 421 | { 422 | "filter": [ 423 | { 424 | "function": "regex", 425 | "group_select": -1, 426 | "regex_pattern": "(-?[$0-9.,]{2,})|(-?[0-9]+)" 427 | }, 428 | { 429 | "function": "take_first" 430 | } 431 | ], 432 | "name": "flexible-extract" 433 | } 434 | ], 435 | "should_decontaminate": false, 436 | "metadata": { 437 | "version": 3.0, 438 | "pretrained": "merged_models/Llama-3.1-8B_merged/RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1" 439 | } 440 | }, 441 | "hellaswag_de": { 442 | "task": "hellaswag_de", 443 | "tag": [ 444 | "hellaswag_multilingual" 445 | ], 446 | "dataset_path": "alexandrainst/m_hellaswag", 447 | "dataset_name": "de", 448 | "validation_split": "val", 449 | "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc):\n ctx = doc[\"ctx_a\"] + \" \" + doc[\"ctx_b\"].capitalize()\n out_doc = {\n \"query\": preprocess(doc[\"activity_label\"] + \": \" + ctx),\n \"choices\": [preprocess(ending) for ending in doc[\"endings\"]],\n \"gold\": int(doc[\"label\"]),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n", 450 | "doc_to_text": "query", 451 | "doc_to_target": "{{label.lstrip()}}", 452 | "unsafe_code": false, 453 | "doc_to_choice": "choices", 454 | "description": "", 455 | "target_delimiter": " ", 456 | "fewshot_delimiter": "\n\n", 457 | "num_fewshot": 0, 458 | "metric_list": [ 459 | { 460 | "metric": "acc", 461 | "aggregation": "mean", 462 | "higher_is_better": true 463 | }, 464 | { 465 | "metric": "acc_norm", 466 | "aggregation": "mean", 467 | "higher_is_better": true 468 | } 469 | ], 470 | "output_type": "multiple_choice", 471 | "repeats": 1, 472 | "should_decontaminate": false, 473 | "metadata": { 474 | "version": 1.0, 475 | "pretrained": "merged_models/Llama-3.1-8B_merged/RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1" 476 | } 477 | }, 478 | "hellaswag_es": { 479 | "task": "hellaswag_es", 480 | "tag": [ 481 | "hellaswag_multilingual" 482 | ], 483 | "dataset_path": "alexandrainst/m_hellaswag", 484 | "dataset_name": "es", 485 | "validation_split": "val", 486 | "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc):\n ctx = doc[\"ctx_a\"] + \" \" + doc[\"ctx_b\"].capitalize()\n out_doc = {\n \"query\": preprocess(doc[\"activity_label\"] + \": \" + ctx),\n \"choices\": [preprocess(ending) for ending in doc[\"endings\"]],\n \"gold\": int(doc[\"label\"]),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n", 487 | "doc_to_text": "query", 488 | "doc_to_target": "{{label.lstrip()}}", 489 | "unsafe_code": false, 490 | "doc_to_choice": "choices", 491 | "description": "", 492 | "target_delimiter": " ", 493 | "fewshot_delimiter": "\n\n", 494 | "num_fewshot": 0, 495 | "metric_list": [ 496 | { 497 | "metric": "acc", 498 | "aggregation": "mean", 499 | "higher_is_better": true 500 | }, 501 | { 502 | "metric": "acc_norm", 503 | "aggregation": "mean", 504 | "higher_is_better": true 505 | } 506 | ], 507 | "output_type": "multiple_choice", 508 | "repeats": 1, 509 | "should_decontaminate": false, 510 | "metadata": { 511 | "version": 1.0, 512 | "pretrained": "merged_models/Llama-3.1-8B_merged/RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1" 513 | } 514 | }, 515 | "hellaswag_fr": { 516 | "task": "hellaswag_fr", 517 | "tag": [ 518 | "hellaswag_multilingual" 519 | ], 520 | "dataset_path": "alexandrainst/m_hellaswag", 521 | "dataset_name": "fr", 522 | "validation_split": "val", 523 | "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc):\n ctx = doc[\"ctx_a\"] + \" \" + doc[\"ctx_b\"].capitalize()\n out_doc = {\n \"query\": preprocess(doc[\"activity_label\"] + \": \" + ctx),\n \"choices\": [preprocess(ending) for ending in doc[\"endings\"]],\n \"gold\": int(doc[\"label\"]),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n", 524 | "doc_to_text": "query", 525 | "doc_to_target": "{{label.lstrip()}}", 526 | "unsafe_code": false, 527 | "doc_to_choice": "choices", 528 | "description": "", 529 | "target_delimiter": " ", 530 | "fewshot_delimiter": "\n\n", 531 | "num_fewshot": 0, 532 | "metric_list": [ 533 | { 534 | "metric": "acc", 535 | "aggregation": "mean", 536 | "higher_is_better": true 537 | }, 538 | { 539 | "metric": "acc_norm", 540 | "aggregation": "mean", 541 | "higher_is_better": true 542 | } 543 | ], 544 | "output_type": "multiple_choice", 545 | "repeats": 1, 546 | "should_decontaminate": false, 547 | "metadata": { 548 | "version": 1.0, 549 | "pretrained": "merged_models/Llama-3.1-8B_merged/RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1" 550 | } 551 | }, 552 | "hellaswag_ru": { 553 | "task": "hellaswag_ru", 554 | "tag": [ 555 | "hellaswag_multilingual" 556 | ], 557 | "dataset_path": "alexandrainst/m_hellaswag", 558 | "dataset_name": "ru", 559 | "validation_split": "val", 560 | "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc):\n ctx = doc[\"ctx_a\"] + \" \" + doc[\"ctx_b\"].capitalize()\n out_doc = {\n \"query\": preprocess(doc[\"activity_label\"] + \": \" + ctx),\n \"choices\": [preprocess(ending) for ending in doc[\"endings\"]],\n \"gold\": int(doc[\"label\"]),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n", 561 | "doc_to_text": "query", 562 | "doc_to_target": "{{label.lstrip()}}", 563 | "unsafe_code": false, 564 | "doc_to_choice": "choices", 565 | "description": "", 566 | "target_delimiter": " ", 567 | "fewshot_delimiter": "\n\n", 568 | "num_fewshot": 0, 569 | "metric_list": [ 570 | { 571 | "metric": "acc", 572 | "aggregation": "mean", 573 | "higher_is_better": true 574 | }, 575 | { 576 | "metric": "acc_norm", 577 | "aggregation": "mean", 578 | "higher_is_better": true 579 | } 580 | ], 581 | "output_type": "multiple_choice", 582 | "repeats": 1, 583 | "should_decontaminate": false, 584 | "metadata": { 585 | "version": 1.0, 586 | "pretrained": "merged_models/Llama-3.1-8B_merged/RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1" 587 | } 588 | }, 589 | "hendrycks_math_algebra": { 590 | "task": "hendrycks_math_algebra", 591 | "tag": [ 592 | "math_word_problems" 593 | ], 594 | "dataset_path": "EleutherAI/hendrycks_math", 595 | "dataset_name": "algebra", 596 | "training_split": "train", 597 | "test_split": "test", 598 | "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n", 599 | "doc_to_text": "Problem: {{problem}}\nAnswer:", 600 | "doc_to_target": "{{answer}}", 601 | "unsafe_code": false, 602 | "process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n", 603 | "description": "", 604 | "target_delimiter": " ", 605 | "fewshot_delimiter": "\n\n", 606 | "num_fewshot": 0, 607 | "metric_list": [ 608 | { 609 | "metric": "exact_match", 610 | "aggregation": "mean", 611 | "higher_is_better": true 612 | } 613 | ], 614 | "output_type": "generate_until", 615 | "generation_kwargs": { 616 | "until": [ 617 | "Problem:" 618 | ], 619 | "do_sample": false, 620 | "temperature": 0.0 621 | }, 622 | "repeats": 1, 623 | "should_decontaminate": false, 624 | "metadata": { 625 | "version": 1.0, 626 | "pretrained": "merged_models/Llama-3.1-8B_merged/RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1" 627 | } 628 | }, 629 | "hendrycks_math_counting_and_prob": { 630 | "task": "hendrycks_math_counting_and_prob", 631 | "tag": [ 632 | "math_word_problems" 633 | ], 634 | "dataset_path": "EleutherAI/hendrycks_math", 635 | "dataset_name": "counting_and_probability", 636 | "training_split": "train", 637 | "test_split": "test", 638 | "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n", 639 | "doc_to_text": "Problem: {{problem}}\nAnswer:", 640 | "doc_to_target": "{{answer}}", 641 | "unsafe_code": false, 642 | "process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n", 643 | "description": "", 644 | "target_delimiter": " ", 645 | "fewshot_delimiter": "\n\n", 646 | "num_fewshot": 0, 647 | "metric_list": [ 648 | { 649 | "metric": "exact_match", 650 | "aggregation": "mean", 651 | "higher_is_better": true 652 | } 653 | ], 654 | "output_type": "generate_until", 655 | "generation_kwargs": { 656 | "until": [ 657 | "Problem:" 658 | ], 659 | "do_sample": false, 660 | "temperature": 0.0 661 | }, 662 | "repeats": 1, 663 | "should_decontaminate": false, 664 | "metadata": { 665 | "version": 1.0, 666 | "pretrained": "merged_models/Llama-3.1-8B_merged/RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1" 667 | } 668 | }, 669 | "hendrycks_math_geometry": { 670 | "task": "hendrycks_math_geometry", 671 | "tag": [ 672 | "math_word_problems" 673 | ], 674 | "dataset_path": "EleutherAI/hendrycks_math", 675 | "dataset_name": "geometry", 676 | "training_split": "train", 677 | "test_split": "test", 678 | "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n", 679 | "doc_to_text": "Problem: {{problem}}\nAnswer:", 680 | "doc_to_target": "{{answer}}", 681 | "unsafe_code": false, 682 | "process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n", 683 | "description": "", 684 | "target_delimiter": " ", 685 | "fewshot_delimiter": "\n\n", 686 | "num_fewshot": 0, 687 | "metric_list": [ 688 | { 689 | "metric": "exact_match", 690 | "aggregation": "mean", 691 | "higher_is_better": true 692 | } 693 | ], 694 | "output_type": "generate_until", 695 | "generation_kwargs": { 696 | "until": [ 697 | "Problem:" 698 | ], 699 | "do_sample": false, 700 | "temperature": 0.0 701 | }, 702 | "repeats": 1, 703 | "should_decontaminate": false, 704 | "metadata": { 705 | "version": 1.0, 706 | "pretrained": "merged_models/Llama-3.1-8B_merged/RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1" 707 | } 708 | }, 709 | "hendrycks_math_intermediate_algebra": { 710 | "task": "hendrycks_math_intermediate_algebra", 711 | "tag": [ 712 | "math_word_problems" 713 | ], 714 | "dataset_path": "EleutherAI/hendrycks_math", 715 | "dataset_name": "intermediate_algebra", 716 | "training_split": "train", 717 | "test_split": "test", 718 | "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n", 719 | "doc_to_text": "Problem: {{problem}}\nAnswer:", 720 | "doc_to_target": "{{answer}}", 721 | "unsafe_code": false, 722 | "process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n", 723 | "description": "", 724 | "target_delimiter": " ", 725 | "fewshot_delimiter": "\n\n", 726 | "num_fewshot": 0, 727 | "metric_list": [ 728 | { 729 | "metric": "exact_match", 730 | "aggregation": "mean", 731 | "higher_is_better": true 732 | } 733 | ], 734 | "output_type": "generate_until", 735 | "generation_kwargs": { 736 | "until": [ 737 | "Problem:" 738 | ], 739 | "do_sample": false, 740 | "temperature": 0.0 741 | }, 742 | "repeats": 1, 743 | "should_decontaminate": false, 744 | "metadata": { 745 | "version": 1.0, 746 | "pretrained": "merged_models/Llama-3.1-8B_merged/RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1" 747 | } 748 | }, 749 | "hendrycks_math_num_theory": { 750 | "task": "hendrycks_math_num_theory", 751 | "tag": [ 752 | "math_word_problems" 753 | ], 754 | "dataset_path": "EleutherAI/hendrycks_math", 755 | "dataset_name": "number_theory", 756 | "training_split": "train", 757 | "test_split": "test", 758 | "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n", 759 | "doc_to_text": "Problem: {{problem}}\nAnswer:", 760 | "doc_to_target": "{{answer}}", 761 | "unsafe_code": false, 762 | "process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n", 763 | "description": "", 764 | "target_delimiter": " ", 765 | "fewshot_delimiter": "\n\n", 766 | "num_fewshot": 0, 767 | "metric_list": [ 768 | { 769 | "metric": "exact_match", 770 | "aggregation": "mean", 771 | "higher_is_better": true 772 | } 773 | ], 774 | "output_type": "generate_until", 775 | "generation_kwargs": { 776 | "until": [ 777 | "Problem:" 778 | ], 779 | "do_sample": false, 780 | "temperature": 0.0 781 | }, 782 | "repeats": 1, 783 | "should_decontaminate": false, 784 | "metadata": { 785 | "version": 1.0, 786 | "pretrained": "merged_models/Llama-3.1-8B_merged/RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1" 787 | } 788 | }, 789 | "hendrycks_math_prealgebra": { 790 | "task": "hendrycks_math_prealgebra", 791 | "tag": [ 792 | "math_word_problems" 793 | ], 794 | "dataset_path": "EleutherAI/hendrycks_math", 795 | "dataset_name": "prealgebra", 796 | "training_split": "train", 797 | "test_split": "test", 798 | "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n", 799 | "doc_to_text": "Problem: {{problem}}\nAnswer:", 800 | "doc_to_target": "{{answer}}", 801 | "unsafe_code": false, 802 | "process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n", 803 | "description": "", 804 | "target_delimiter": " ", 805 | "fewshot_delimiter": "\n\n", 806 | "num_fewshot": 0, 807 | "metric_list": [ 808 | { 809 | "metric": "exact_match", 810 | "aggregation": "mean", 811 | "higher_is_better": true 812 | } 813 | ], 814 | "output_type": "generate_until", 815 | "generation_kwargs": { 816 | "until": [ 817 | "Problem:" 818 | ], 819 | "do_sample": false, 820 | "temperature": 0.0 821 | }, 822 | "repeats": 1, 823 | "should_decontaminate": false, 824 | "metadata": { 825 | "version": 1.0, 826 | "pretrained": "merged_models/Llama-3.1-8B_merged/RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1" 827 | } 828 | }, 829 | "hendrycks_math_precalc": { 830 | "task": "hendrycks_math_precalc", 831 | "tag": [ 832 | "math_word_problems" 833 | ], 834 | "dataset_path": "EleutherAI/hendrycks_math", 835 | "dataset_name": "precalculus", 836 | "training_split": "train", 837 | "test_split": "test", 838 | "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n", 839 | "doc_to_text": "Problem: {{problem}}\nAnswer:", 840 | "doc_to_target": "{{answer}}", 841 | "unsafe_code": false, 842 | "process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n", 843 | "description": "", 844 | "target_delimiter": " ", 845 | "fewshot_delimiter": "\n\n", 846 | "num_fewshot": 0, 847 | "metric_list": [ 848 | { 849 | "metric": "exact_match", 850 | "aggregation": "mean", 851 | "higher_is_better": true 852 | } 853 | ], 854 | "output_type": "generate_until", 855 | "generation_kwargs": { 856 | "until": [ 857 | "Problem:" 858 | ], 859 | "do_sample": false, 860 | "temperature": 0.0 861 | }, 862 | "repeats": 1, 863 | "should_decontaminate": false, 864 | "metadata": { 865 | "version": 1.0, 866 | "pretrained": "merged_models/Llama-3.1-8B_merged/RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1" 867 | } 868 | }, 869 | "ifeval": { 870 | "task": "ifeval", 871 | "dataset_path": "google/IFEval", 872 | "test_split": "train", 873 | "doc_to_text": "prompt", 874 | "doc_to_target": 0, 875 | "unsafe_code": false, 876 | "process_results": "def process_results(doc, results):\n inp = InputExample(\n key=doc[\"key\"],\n instruction_id_list=doc[\"instruction_id_list\"],\n prompt=doc[\"prompt\"],\n kwargs=doc[\"kwargs\"],\n )\n response = results[0]\n\n out_strict = test_instruction_following_strict(inp, response)\n out_loose = test_instruction_following_loose(inp, response)\n\n return {\n \"prompt_level_strict_acc\": out_strict.follow_all_instructions,\n \"inst_level_strict_acc\": out_strict.follow_instruction_list,\n \"prompt_level_loose_acc\": out_loose.follow_all_instructions,\n \"inst_level_loose_acc\": out_loose.follow_instruction_list,\n }\n", 877 | "description": "", 878 | "target_delimiter": " ", 879 | "fewshot_delimiter": "\n\n", 880 | "num_fewshot": 0, 881 | "metric_list": [ 882 | { 883 | "metric": "prompt_level_strict_acc", 884 | "aggregation": "mean", 885 | "higher_is_better": true 886 | }, 887 | { 888 | "metric": "inst_level_strict_acc", 889 | "aggregation": "def agg_inst_level_acc(items):\n flat_items = [item for sublist in items for item in sublist]\n inst_level_acc = sum(flat_items) / len(flat_items)\n return inst_level_acc\n", 890 | "higher_is_better": true 891 | }, 892 | { 893 | "metric": "prompt_level_loose_acc", 894 | "aggregation": "mean", 895 | "higher_is_better": true 896 | }, 897 | { 898 | "metric": "inst_level_loose_acc", 899 | "aggregation": "def agg_inst_level_acc(items):\n flat_items = [item for sublist in items for item in sublist]\n inst_level_acc = sum(flat_items) / len(flat_items)\n return inst_level_acc\n", 900 | "higher_is_better": true 901 | } 902 | ], 903 | "output_type": "generate_until", 904 | "generation_kwargs": { 905 | "until": [], 906 | "do_sample": false, 907 | "temperature": 0.0, 908 | "max_gen_toks": 1280 909 | }, 910 | "repeats": 1, 911 | "should_decontaminate": false, 912 | "metadata": { 913 | "version": 4.0, 914 | "pretrained": "merged_models/Llama-3.1-8B_merged/RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1" 915 | } 916 | }, 917 | "m_mmlu_de": { 918 | "task": "m_mmlu_de", 919 | "tag": [ 920 | "m_mmlu" 921 | ], 922 | "dataset_path": "alexandrainst/m_mmlu", 923 | "dataset_name": "de", 924 | "test_split": "test", 925 | "fewshot_split": "train", 926 | "doc_to_text": "{{instruction.strip()}}\nA. {{option_a}}\nB. {{option_b}}\nC. {{option_c}}\nD. {{option_d}}\nAnswer:", 927 | "doc_to_target": "answer", 928 | "unsafe_code": false, 929 | "doc_to_choice": [ 930 | "A", 931 | "B", 932 | "C", 933 | "D" 934 | ], 935 | "description": "", 936 | "target_delimiter": " ", 937 | "fewshot_delimiter": "\n\n", 938 | "fewshot_config": { 939 | "sampler": "first_n" 940 | }, 941 | "num_fewshot": 0, 942 | "metric_list": [ 943 | { 944 | "metric": "acc", 945 | "aggregation": "mean", 946 | "higher_is_better": true 947 | } 948 | ], 949 | "output_type": "multiple_choice", 950 | "repeats": 1, 951 | "should_decontaminate": false, 952 | "metadata": { 953 | "version": 0.0, 954 | "pretrained": "merged_models/Llama-3.1-8B_merged/RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1" 955 | } 956 | }, 957 | "m_mmlu_es": { 958 | "task": "m_mmlu_es", 959 | "tag": [ 960 | "m_mmlu" 961 | ], 962 | "dataset_path": "alexandrainst/m_mmlu", 963 | "dataset_name": "es", 964 | "test_split": "test", 965 | "fewshot_split": "train", 966 | "doc_to_text": "{{instruction.strip()}}\nA. {{option_a}}\nB. {{option_b}}\nC. {{option_c}}\nD. {{option_d}}\nAnswer:", 967 | "doc_to_target": "answer", 968 | "unsafe_code": false, 969 | "doc_to_choice": [ 970 | "A", 971 | "B", 972 | "C", 973 | "D" 974 | ], 975 | "description": "", 976 | "target_delimiter": " ", 977 | "fewshot_delimiter": "\n\n", 978 | "fewshot_config": { 979 | "sampler": "first_n" 980 | }, 981 | "num_fewshot": 0, 982 | "metric_list": [ 983 | { 984 | "metric": "acc", 985 | "aggregation": "mean", 986 | "higher_is_better": true 987 | } 988 | ], 989 | "output_type": "multiple_choice", 990 | "repeats": 1, 991 | "should_decontaminate": false, 992 | "metadata": { 993 | "version": 0.0, 994 | "pretrained": "merged_models/Llama-3.1-8B_merged/RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1" 995 | } 996 | }, 997 | "m_mmlu_fr": { 998 | "task": "m_mmlu_fr", 999 | "tag": [ 1000 | "m_mmlu" 1001 | ], 1002 | "dataset_path": "alexandrainst/m_mmlu", 1003 | "dataset_name": "fr", 1004 | "test_split": "test", 1005 | "fewshot_split": "train", 1006 | "doc_to_text": "{{instruction.strip()}}\nA. {{option_a}}\nB. {{option_b}}\nC. {{option_c}}\nD. {{option_d}}\nAnswer:", 1007 | "doc_to_target": "answer", 1008 | "unsafe_code": false, 1009 | "doc_to_choice": [ 1010 | "A", 1011 | "B", 1012 | "C", 1013 | "D" 1014 | ], 1015 | "description": "", 1016 | "target_delimiter": " ", 1017 | "fewshot_delimiter": "\n\n", 1018 | "fewshot_config": { 1019 | "sampler": "first_n" 1020 | }, 1021 | "num_fewshot": 0, 1022 | "metric_list": [ 1023 | { 1024 | "metric": "acc", 1025 | "aggregation": "mean", 1026 | "higher_is_better": true 1027 | } 1028 | ], 1029 | "output_type": "multiple_choice", 1030 | "repeats": 1, 1031 | "should_decontaminate": false, 1032 | "metadata": { 1033 | "version": 0.0, 1034 | "pretrained": "merged_models/Llama-3.1-8B_merged/RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1" 1035 | } 1036 | }, 1037 | "m_mmlu_ru": { 1038 | "task": "m_mmlu_ru", 1039 | "tag": [ 1040 | "m_mmlu" 1041 | ], 1042 | "dataset_path": "alexandrainst/m_mmlu", 1043 | "dataset_name": "ru", 1044 | "test_split": "test", 1045 | "fewshot_split": "train", 1046 | "doc_to_text": "{{instruction.strip()}}\nA. {{option_a}}\nB. {{option_b}}\nC. {{option_c}}\nD. {{option_d}}\nAnswer:", 1047 | "doc_to_target": "answer", 1048 | "unsafe_code": false, 1049 | "doc_to_choice": [ 1050 | "A", 1051 | "B", 1052 | "C", 1053 | "D" 1054 | ], 1055 | "description": "", 1056 | "target_delimiter": " ", 1057 | "fewshot_delimiter": "\n\n", 1058 | "fewshot_config": { 1059 | "sampler": "first_n" 1060 | }, 1061 | "num_fewshot": 0, 1062 | "metric_list": [ 1063 | { 1064 | "metric": "acc", 1065 | "aggregation": "mean", 1066 | "higher_is_better": true 1067 | } 1068 | ], 1069 | "output_type": "multiple_choice", 1070 | "repeats": 1, 1071 | "should_decontaminate": false, 1072 | "metadata": { 1073 | "version": 0.0, 1074 | "pretrained": "merged_models/Llama-3.1-8B_merged/RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1" 1075 | } 1076 | } 1077 | }, 1078 | "versions": { 1079 | "arc_de": 2.0, 1080 | "arc_es": 2.0, 1081 | "arc_fr": 2.0, 1082 | "arc_ru": 2.0, 1083 | "gsm8k_cot": 3.0, 1084 | "hellaswag_de": 1.0, 1085 | "hellaswag_es": 1.0, 1086 | "hellaswag_fr": 1.0, 1087 | "hellaswag_ru": 1.0, 1088 | "hendrycks_math": 1.0, 1089 | "hendrycks_math_algebra": 1.0, 1090 | "hendrycks_math_counting_and_prob": 1.0, 1091 | "hendrycks_math_geometry": 1.0, 1092 | "hendrycks_math_intermediate_algebra": 1.0, 1093 | "hendrycks_math_num_theory": 1.0, 1094 | "hendrycks_math_prealgebra": 1.0, 1095 | "hendrycks_math_precalc": 1.0, 1096 | "ifeval": 4.0, 1097 | "m_mmlu_de": 0.0, 1098 | "m_mmlu_es": 0.0, 1099 | "m_mmlu_fr": 0.0, 1100 | "m_mmlu_ru": 0.0 1101 | }, 1102 | "n-shot": { 1103 | "arc_de": 0, 1104 | "arc_es": 0, 1105 | "arc_fr": 0, 1106 | "arc_ru": 0, 1107 | "gsm8k_cot": 8, 1108 | "hellaswag_de": 0, 1109 | "hellaswag_es": 0, 1110 | "hellaswag_fr": 0, 1111 | "hellaswag_ru": 0, 1112 | "hendrycks_math_algebra": 0, 1113 | "hendrycks_math_counting_and_prob": 0, 1114 | 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1160 | }, 1161 | "hendrycks_math": { 1162 | "exact_match": true 1163 | }, 1164 | "hendrycks_math_algebra": { 1165 | "exact_match": true 1166 | }, 1167 | "hendrycks_math_counting_and_prob": { 1168 | "exact_match": true 1169 | }, 1170 | "hendrycks_math_geometry": { 1171 | "exact_match": true 1172 | }, 1173 | "hendrycks_math_intermediate_algebra": { 1174 | "exact_match": true 1175 | }, 1176 | "hendrycks_math_num_theory": { 1177 | "exact_match": true 1178 | }, 1179 | "hendrycks_math_prealgebra": { 1180 | "exact_match": true 1181 | }, 1182 | "hendrycks_math_precalc": { 1183 | "exact_match": true 1184 | }, 1185 | "ifeval": { 1186 | "prompt_level_strict_acc": true, 1187 | "inst_level_strict_acc": true, 1188 | "prompt_level_loose_acc": true, 1189 | "inst_level_loose_acc": true 1190 | }, 1191 | "m_mmlu_de": { 1192 | "acc": true 1193 | }, 1194 | "m_mmlu_es": { 1195 | "acc": true 1196 | }, 1197 | "m_mmlu_fr": { 1198 | "acc": true 1199 | }, 1200 | "m_mmlu_ru": { 1201 | "acc": true 1202 | } 1203 | }, 1204 | "n-samples": { 1205 | "m_mmlu_ru": { 1206 | "original": 13007, 1207 | "effective": 13007 1208 | }, 1209 | "m_mmlu_fr": { 1210 | "original": 13091, 1211 | "effective": 13091 1212 | }, 1213 | "m_mmlu_es": { 1214 | "original": 13334, 1215 | "effective": 13334 1216 | }, 1217 | "m_mmlu_de": { 1218 | "original": 13258, 1219 | "effective": 13258 1220 | }, 1221 | "ifeval": { 1222 | "original": 541, 1223 | "effective": 541 1224 | }, 1225 | "hendrycks_math_algebra": { 1226 | "original": 1187, 1227 | "effective": 1187 1228 | }, 1229 | "hendrycks_math_counting_and_prob": { 1230 | "original": 474, 1231 | "effective": 474 1232 | }, 1233 | "hendrycks_math_geometry": { 1234 | "original": 479, 1235 | "effective": 479 1236 | }, 1237 | "hendrycks_math_intermediate_algebra": { 1238 | "original": 903, 1239 | "effective": 903 1240 | }, 1241 | "hendrycks_math_num_theory": { 1242 | "original": 540, 1243 | "effective": 540 1244 | }, 1245 | "hendrycks_math_prealgebra": { 1246 | "original": 871, 1247 | "effective": 871 1248 | }, 1249 | "hendrycks_math_precalc": { 1250 | "original": 546, 1251 | "effective": 546 1252 | }, 1253 | "hellaswag_ru": { 1254 | "original": 9272, 1255 | "effective": 9272 1256 | }, 1257 | "hellaswag_fr": { 1258 | "original": 9338, 1259 | "effective": 9338 1260 | }, 1261 | "hellaswag_es": { 1262 | "original": 9374, 1263 | "effective": 9374 1264 | }, 1265 | "hellaswag_de": { 1266 | "original": 9368, 1267 | "effective": 9368 1268 | }, 1269 | "gsm8k_cot": { 1270 | "original": 1319, 1271 | "effective": 1319 1272 | }, 1273 | "arc_ru": { 1274 | "original": 1169, 1275 | "effective": 1169 1276 | }, 1277 | "arc_fr": { 1278 | "original": 1169, 1279 | "effective": 1169 1280 | }, 1281 | "arc_es": { 1282 | "original": 1170, 1283 | "effective": 1170 1284 | }, 1285 | "arc_de": { 1286 | "original": 1169, 1287 | "effective": 1169 1288 | } 1289 | }, 1290 | "config": { 1291 | "model": "vllm", 1292 | "model_args": "pretrained=merged_models/Llama-3.1-8B_merged/RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1", 1293 | "batch_size": "auto", 1294 | "batch_sizes": [], 1295 | "device": "cuda:0", 1296 | "use_cache": null, 1297 | "limit": null, 1298 | "bootstrap_iters": 100000, 1299 | "gen_kwargs": null, 1300 | "random_seed": 0, 1301 | "numpy_seed": 1234, 1302 | "torch_seed": 1234, 1303 | "fewshot_seed": 1234 1304 | }, 1305 | "git_hash": "f91dd3c", 1306 | "date": 1763373562.252434, 1307 | "pretty_env_info": "PyTorch version: 2.8.0+cu128\nIs debug build: False\nCUDA used to build PyTorch: 12.8\nROCM used to build PyTorch: N/A\n\nOS: Ubuntu 20.04.3 LTS (x86_64)\nGCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0\nClang version: Could not collect\nCMake version: version 3.16.3\nLibc version: glibc-2.31\n\nPython version: 3.10.9 (main, Mar 8 2023, 10:47:38) [GCC 11.2.0] (64-bit runtime)\nPython platform: Linux-5.15.0-69-generic-x86_64-with-glibc2.31\nIs CUDA available: True\nCUDA runtime version: Could not collect\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: GPU 0: NVIDIA A40\nNvidia driver version: 570.133.20\ncuDNN version: Could not collect\nHIP runtime version: N/A\nMIOpen runtime version: N/A\nIs XNNPACK available: True\n\nCPU:\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nByte Order: Little Endian\nAddress sizes: 46 bits physical, 57 bits virtual\nCPU(s): 52\nOn-line CPU(s) list: 0-51\nThread(s) per core: 1\nCore(s) per socket: 26\nSocket(s): 2\nNUMA node(s): 4\nVendor ID: GenuineIntel\nCPU family: 6\nModel: 106\nModel name: Intel(R) Xeon(R) Gold 5320 CPU @ 2.20GHz\nStepping: 6\nCPU MHz: 814.783\nCPU max MHz: 3400.0000\nCPU min MHz: 800.0000\nBogoMIPS: 4400.00\nL1d cache: 2.4 MiB\nL1i cache: 1.6 MiB\nL2 cache: 65 MiB\nL3 cache: 78 MiB\nNUMA node0 CPU(s): 0,4,8,12,16,20,24,28,32,36,40,44,48\nNUMA node1 CPU(s): 2,6,10,14,18,22,26,30,34,38,42,46,50\nNUMA node2 CPU(s): 1,5,9,13,17,21,25,29,33,37,41,45,49\nNUMA node3 CPU(s): 3,7,11,15,19,23,27,31,35,39,43,47,51\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT disabled\nVulnerability Retbleed: Not affected\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Not affected\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 invpcid_single intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect wbnoinvd dtherm ida arat pln pts avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid fsrm md_clear pconfig flush_l1d arch_capabilities\n\nVersions of relevant libraries:\n[pip3] numpy==2.2.6\n[pip3] nvidia-cublas-cu12==12.8.4.1\n[pip3] nvidia-cuda-cupti-cu12==12.8.90\n[pip3] nvidia-cuda-nvrtc-cu12==12.8.93\n[pip3] nvidia-cuda-runtime-cu12==12.8.90\n[pip3] nvidia-cudnn-cu12==9.10.2.21\n[pip3] nvidia-cufft-cu12==11.3.3.83\n[pip3] nvidia-curand-cu12==10.3.9.90\n[pip3] nvidia-cusolver-cu12==11.7.3.90\n[pip3] nvidia-cusparse-cu12==12.5.8.93\n[pip3] nvidia-cusparselt-cu12==0.7.1\n[pip3] nvidia-nccl-cu12==2.27.3\n[pip3] nvidia-nvjitlink-cu12==12.8.93\n[pip3] nvidia-nvtx-cu12==12.8.90\n[pip3] torch==2.8.0\n[pip3] torchaudio==2.8.0\n[pip3] torchvision==0.23.0\n[pip3] triton==3.4.0\n[conda] numpy 2.2.6 pypi_0 pypi\n[conda] nvidia-cublas-cu12 12.8.4.1 pypi_0 pypi\n[conda] nvidia-cuda-cupti-cu12 12.8.90 pypi_0 pypi\n[conda] nvidia-cuda-nvrtc-cu12 12.8.93 pypi_0 pypi\n[conda] nvidia-cuda-runtime-cu12 12.8.90 pypi_0 pypi\n[conda] nvidia-cudnn-cu12 9.10.2.21 pypi_0 pypi\n[conda] nvidia-cufft-cu12 11.3.3.83 pypi_0 pypi\n[conda] nvidia-curand-cu12 10.3.9.90 pypi_0 pypi\n[conda] nvidia-cusolver-cu12 11.7.3.90 pypi_0 pypi\n[conda] nvidia-cusparse-cu12 12.5.8.93 pypi_0 pypi\n[conda] nvidia-cusparselt-cu12 0.7.1 pypi_0 pypi\n[conda] nvidia-nccl-cu12 2.27.3 pypi_0 pypi\n[conda] nvidia-nvjitlink-cu12 12.8.93 pypi_0 pypi\n[conda] nvidia-nvtx-cu12 12.8.90 pypi_0 pypi\n[conda] torch 2.8.0 pypi_0 pypi\n[conda] torchaudio 2.8.0 pypi_0 pypi\n[conda] torchvision 0.23.0 pypi_0 pypi\n[conda] triton 3.4.0 pypi_0 pypi", 1308 | "transformers_version": "4.57.1", 1309 | "lm_eval_version": "0.4.9.1", 1310 | "upper_git_hash": null, 1311 | "tokenizer_pad_token": [ 1312 | "<|end_of_text|>", 1313 | "128001" 1314 | ], 1315 | "tokenizer_eos_token": [ 1316 | "<|end_of_text|>", 1317 | "128001" 1318 | ], 1319 | "tokenizer_bos_token": [ 1320 | "<|begin_of_text|>", 1321 | "128000" 1322 | ], 1323 | "eot_token_id": 128001, 1324 | "max_length": 131072, 1325 | "task_hashes": {}, 1326 | "model_source": "vllm", 1327 | "model_name": "merged_models/Llama-3.1-8B_merged/RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1", 1328 | "model_name_sanitized": "merged_models__Llama-3.1-8B_merged__RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1", 1329 | "system_instruction": null, 1330 | "system_instruction_sha": null, 1331 | "fewshot_as_multiturn": false, 1332 | "chat_template": null, 1333 | "chat_template_sha": null, 1334 | "start_time": 15748933.200144514, 1335 | "end_time": 15757932.640946288, 1336 | "total_evaluation_time_seconds": "8999.44080177322" 1337 | } -------------------------------------------------------------------------------- /merged_models/Llama-3.2-3B_merged/RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1/lm_eval.json: -------------------------------------------------------------------------------- 1 | { 2 | "results": { 3 | "arc_de": { 4 | "alias": "arc_de", 5 | "acc,none": 0.31137724550898205, 6 | "acc_stderr,none": 0.01354917023720016, 7 | "acc_norm,none": 0.358426005132592, 8 | "acc_norm_stderr,none": 0.014031422783275219 9 | }, 10 | "arc_es": { 11 | "alias": "arc_es", 12 | "acc,none": 0.3700854700854701, 13 | "acc_stderr,none": 0.014121621753736043, 14 | "acc_norm,none": 0.4008547008547009, 15 | "acc_norm_stderr,none": 0.014333502054419352 16 | }, 17 | "arc_fr": { 18 | "alias": "arc_fr", 19 | "acc,none": 0.35243798118049613, 20 | "acc_stderr,none": 0.013978501429969674, 21 | "acc_norm,none": 0.40461933276304535, 22 | "acc_norm_stderr,none": 0.014361481979772708 23 | }, 24 | "arc_ru": { 25 | "alias": "arc_ru", 26 | "acc,none": 0.34473909324208724, 27 | "acc_stderr,none": 0.013906920607432557, 28 | "acc_norm,none": 0.369546621043627, 29 | "acc_norm_stderr,none": 0.014123413837443284 30 | }, 31 | "gsm8k_cot": { 32 | "alias": "gsm8k_cot", 33 | "exact_match,strict-match": 0.3510235026535254, 34 | "exact_match_stderr,strict-match": 0.01314694594139722, 35 | "exact_match,flexible-extract": 0.400303260045489, 36 | "exact_match_stderr,flexible-extract": 0.013495926436566438 37 | }, 38 | "hellaswag_de": { 39 | "alias": "hellaswag_de", 40 | "acc,none": 0.42410333048676346, 41 | "acc_stderr,none": 0.005106318088351463, 42 | "acc_norm,none": 0.5470751494449189, 43 | "acc_norm_stderr,none": 0.0051432342942479385 44 | }, 45 | "hellaswag_es": { 46 | "alias": "hellaswag_es", 47 | "acc,none": 0.46607638148069125, 48 | "acc_stderr,none": 0.005152628194218952, 49 | "acc_norm,none": 0.6067847237038617, 50 | "acc_norm_stderr,none": 0.005045372103545006 51 | }, 52 | "hellaswag_fr": { 53 | "alias": "hellaswag_fr", 54 | "acc,none": 0.44688370100663954, 55 | "acc_stderr,none": 0.005145194613284539, 56 | "acc_norm,none": 0.5938102377382737, 57 | "acc_norm_stderr,none": 0.0050825849670259144 58 | }, 59 | "hellaswag_ru": { 60 | "alias": "hellaswag_ru", 61 | "acc,none": 0.4074633304572908, 62 | "acc_stderr,none": 0.005103153017972229, 63 | "acc_norm,none": 0.5282571182053495, 64 | "acc_norm_stderr,none": 0.005184561926719888 65 | }, 66 | "hendrycks_math": { 67 | "exact_match,none": 0.0172, 68 | "exact_match_stderr,none": 0.0018375943530154295, 69 | "alias": "hendrycks_math" 70 | }, 71 | "hendrycks_math_algebra": { 72 | "alias": " - hendrycks_math_algebra", 73 | "exact_match,none": 0.017691659646166806, 74 | "exact_match_stderr,none": 0.0038279464976423414 75 | }, 76 | "hendrycks_math_counting_and_prob": { 77 | "alias": " - hendrycks_math_counting_and_prob", 78 | "exact_match,none": 0.014767932489451477, 79 | "exact_match_stderr,none": 0.005546238589668472 80 | }, 81 | "hendrycks_math_geometry": { 82 | "alias": " - hendrycks_math_geometry", 83 | "exact_match,none": 0.025052192066805846, 84 | "exact_match_stderr,none": 0.007148247838013836 85 | }, 86 | "hendrycks_math_intermediate_algebra": { 87 | "alias": " - hendrycks_math_intermediate_algebra", 88 | "exact_match,none": 0.007751937984496124, 89 | "exact_match_stderr,none": 0.0029201960269643937 90 | }, 91 | "hendrycks_math_num_theory": { 92 | "alias": " - hendrycks_math_num_theory", 93 | "exact_match,none": 0.014814814814814815, 94 | "exact_match_stderr,none": 0.005203704987512652 95 | }, 96 | "hendrycks_math_prealgebra": { 97 | "alias": " - hendrycks_math_prealgebra", 98 | "exact_match,none": 0.027554535017221583, 99 | "exact_match_stderr,none": 0.005549700480393211 100 | }, 101 | "hendrycks_math_precalc": { 102 | "alias": " - hendrycks_math_precalc", 103 | "exact_match,none": 0.01282051282051282, 104 | "exact_match_stderr,none": 0.004818950982487616 105 | }, 106 | "ifeval": { 107 | "alias": "ifeval", 108 | "prompt_level_strict_acc,none": 0.06839186691312385, 109 | "prompt_level_strict_acc_stderr,none": 0.010862304803962516, 110 | "inst_level_strict_acc,none": 0.09712230215827339, 111 | "inst_level_strict_acc_stderr,none": "N/A", 112 | "prompt_level_loose_acc,none": 0.08687615526802218, 113 | "prompt_level_loose_acc_stderr,none": 0.012120436438929415, 114 | "inst_level_loose_acc,none": 0.11151079136690648, 115 | "inst_level_loose_acc_stderr,none": "N/A" 116 | }, 117 | "m_mmlu_de": { 118 | "alias": "m_mmlu_de", 119 | "acc,none": 0.4712626338814301, 120 | "acc_stderr,none": 0.004335397038388265 121 | }, 122 | "m_mmlu_es": { 123 | "alias": "m_mmlu_es", 124 | "acc,none": 0.4887505624718764, 125 | "acc_stderr,none": 0.0043290850402920265 126 | }, 127 | "m_mmlu_fr": { 128 | "alias": "m_mmlu_fr", 129 | "acc,none": 0.47979527919945003, 130 | "acc_stderr,none": 0.0043666190641897815 131 | }, 132 | "m_mmlu_ru": { 133 | "alias": "m_mmlu_ru", 134 | "acc,none": 0.4423771815176443, 135 | "acc_stderr,none": 0.0043550661143529155 136 | } 137 | }, 138 | "groups": { 139 | "hendrycks_math": { 140 | "exact_match,none": 0.0172, 141 | "exact_match_stderr,none": 0.0018375943530154295, 142 | "alias": "hendrycks_math" 143 | } 144 | }, 145 | "group_subtasks": { 146 | "arc_de": [], 147 | "arc_es": [], 148 | "arc_fr": [], 149 | "arc_ru": [], 150 | "gsm8k_cot": [], 151 | "hellaswag_de": [], 152 | "hellaswag_es": [], 153 | "hellaswag_fr": [], 154 | "hellaswag_ru": [], 155 | "hendrycks_math": [ 156 | "hendrycks_math_algebra", 157 | "hendrycks_math_counting_and_prob", 158 | "hendrycks_math_geometry", 159 | "hendrycks_math_intermediate_algebra", 160 | "hendrycks_math_num_theory", 161 | "hendrycks_math_prealgebra", 162 | "hendrycks_math_precalc" 163 | ], 164 | "ifeval": [], 165 | "m_mmlu_de": [], 166 | "m_mmlu_es": [], 167 | "m_mmlu_fr": [], 168 | "m_mmlu_ru": [] 169 | }, 170 | "configs": { 171 | "arc_de": { 172 | "task": "arc_de", 173 | "tag": [ 174 | "arc_multilingual" 175 | ], 176 | "dataset_path": "alexandrainst/m_arc", 177 | "dataset_name": "de", 178 | "training_split": "train", 179 | "validation_split": "validation", 180 | "test_split": "test", 181 | "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc):\n # breakpoint()\n out_doc = {\n \"id\": doc[\"id\"],\n \"query\": \"Question: \" + preprocess(doc[\"instruction\"]) + \"\\nAnswer:\",\n \"choices\": [\n preprocess(option)\n for option in [\n doc[\"option_a\"],\n doc[\"option_b\"],\n doc[\"option_c\"],\n doc[\"option_d\"],\n doc[\"option_e\"],\n ]\n if option\n ],\n \"gold\": [\"A\", \"B\", \"C\", \"D\", \"E\"].index(doc[\"answer\"]),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n", 182 | "doc_to_text": "query", 183 | "doc_to_target": "gold", 184 | "unsafe_code": false, 185 | "doc_to_choice": "choices", 186 | "description": "", 187 | "target_delimiter": " ", 188 | "fewshot_delimiter": "\n\n", 189 | "num_fewshot": 0, 190 | "metric_list": [ 191 | { 192 | "metric": "acc", 193 | "aggregation": "mean", 194 | "higher_is_better": true 195 | }, 196 | { 197 | "metric": "acc_norm", 198 | "aggregation": "mean", 199 | "higher_is_better": true 200 | } 201 | ], 202 | "output_type": "multiple_choice", 203 | "repeats": 1, 204 | "should_decontaminate": true, 205 | "doc_to_decontamination_query": "query", 206 | "metadata": { 207 | "version": 2.0, 208 | "pretrained": "merged_models/Llama-3.2-3B_merged/RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1" 209 | } 210 | }, 211 | "arc_es": { 212 | "task": "arc_es", 213 | "tag": [ 214 | "arc_multilingual" 215 | ], 216 | "dataset_path": "alexandrainst/m_arc", 217 | "dataset_name": "es", 218 | "training_split": "train", 219 | "validation_split": "validation", 220 | "test_split": "test", 221 | "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc):\n # breakpoint()\n out_doc = {\n \"id\": doc[\"id\"],\n \"query\": \"Question: \" + preprocess(doc[\"instruction\"]) + \"\\nAnswer:\",\n \"choices\": [\n preprocess(option)\n for option in [\n doc[\"option_a\"],\n doc[\"option_b\"],\n doc[\"option_c\"],\n doc[\"option_d\"],\n doc[\"option_e\"],\n ]\n if option\n ],\n \"gold\": [\"A\", \"B\", \"C\", \"D\", \"E\"].index(doc[\"answer\"]),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n", 222 | "doc_to_text": "query", 223 | "doc_to_target": "gold", 224 | "unsafe_code": false, 225 | "doc_to_choice": "choices", 226 | "description": "", 227 | "target_delimiter": " ", 228 | "fewshot_delimiter": "\n\n", 229 | "num_fewshot": 0, 230 | "metric_list": [ 231 | { 232 | "metric": "acc", 233 | "aggregation": "mean", 234 | "higher_is_better": true 235 | }, 236 | { 237 | "metric": "acc_norm", 238 | "aggregation": "mean", 239 | "higher_is_better": true 240 | } 241 | ], 242 | "output_type": "multiple_choice", 243 | "repeats": 1, 244 | "should_decontaminate": true, 245 | "doc_to_decontamination_query": "query", 246 | "metadata": { 247 | "version": 2.0, 248 | "pretrained": "merged_models/Llama-3.2-3B_merged/RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1" 249 | } 250 | }, 251 | "arc_fr": { 252 | "task": "arc_fr", 253 | "tag": [ 254 | "arc_multilingual" 255 | ], 256 | "dataset_path": "alexandrainst/m_arc", 257 | "dataset_name": "fr", 258 | "training_split": "train", 259 | "validation_split": "validation", 260 | "test_split": "test", 261 | "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc):\n # breakpoint()\n out_doc = {\n \"id\": doc[\"id\"],\n \"query\": \"Question: \" + preprocess(doc[\"instruction\"]) + \"\\nAnswer:\",\n \"choices\": [\n preprocess(option)\n for option in [\n doc[\"option_a\"],\n doc[\"option_b\"],\n doc[\"option_c\"],\n doc[\"option_d\"],\n doc[\"option_e\"],\n ]\n if option\n ],\n \"gold\": [\"A\", \"B\", \"C\", \"D\", \"E\"].index(doc[\"answer\"]),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n", 262 | "doc_to_text": "query", 263 | "doc_to_target": "gold", 264 | "unsafe_code": false, 265 | "doc_to_choice": "choices", 266 | "description": "", 267 | "target_delimiter": " ", 268 | "fewshot_delimiter": "\n\n", 269 | "num_fewshot": 0, 270 | "metric_list": [ 271 | { 272 | "metric": "acc", 273 | "aggregation": "mean", 274 | "higher_is_better": true 275 | }, 276 | { 277 | "metric": "acc_norm", 278 | "aggregation": "mean", 279 | "higher_is_better": true 280 | } 281 | ], 282 | "output_type": "multiple_choice", 283 | "repeats": 1, 284 | "should_decontaminate": true, 285 | "doc_to_decontamination_query": "query", 286 | "metadata": { 287 | "version": 2.0, 288 | "pretrained": "merged_models/Llama-3.2-3B_merged/RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1" 289 | } 290 | }, 291 | "arc_ru": { 292 | "task": "arc_ru", 293 | "tag": [ 294 | "arc_multilingual" 295 | ], 296 | "dataset_path": "alexandrainst/m_arc", 297 | "dataset_name": "ru", 298 | "training_split": "train", 299 | "validation_split": "validation", 300 | "test_split": "test", 301 | "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc):\n # breakpoint()\n out_doc = {\n \"id\": doc[\"id\"],\n \"query\": \"Question: \" + preprocess(doc[\"instruction\"]) + \"\\nAnswer:\",\n \"choices\": [\n preprocess(option)\n for option in [\n doc[\"option_a\"],\n doc[\"option_b\"],\n doc[\"option_c\"],\n doc[\"option_d\"],\n doc[\"option_e\"],\n ]\n if option\n ],\n \"gold\": [\"A\", \"B\", \"C\", \"D\", \"E\"].index(doc[\"answer\"]),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n", 302 | "doc_to_text": "query", 303 | "doc_to_target": "gold", 304 | "unsafe_code": false, 305 | "doc_to_choice": "choices", 306 | "description": "", 307 | "target_delimiter": " ", 308 | "fewshot_delimiter": "\n\n", 309 | "num_fewshot": 0, 310 | "metric_list": [ 311 | { 312 | "metric": "acc", 313 | "aggregation": "mean", 314 | "higher_is_better": true 315 | }, 316 | { 317 | "metric": "acc_norm", 318 | "aggregation": "mean", 319 | "higher_is_better": true 320 | } 321 | ], 322 | "output_type": "multiple_choice", 323 | "repeats": 1, 324 | "should_decontaminate": true, 325 | "doc_to_decontamination_query": "query", 326 | "metadata": { 327 | "version": 2.0, 328 | "pretrained": "merged_models/Llama-3.2-3B_merged/RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1" 329 | } 330 | }, 331 | "gsm8k_cot": { 332 | "task": "gsm8k_cot", 333 | "tag": [ 334 | "chain_of_thought" 335 | ], 336 | "dataset_path": "gsm8k", 337 | "dataset_name": "main", 338 | "test_split": "test", 339 | "doc_to_text": "Q: {{question}}\nA:", 340 | "doc_to_target": "{{answer.split('####')[-1].strip() if answer is defined else target}}", 341 | "unsafe_code": false, 342 | "description": "", 343 | "target_delimiter": " ", 344 | "fewshot_delimiter": "\n\n", 345 | "fewshot_config": { 346 | "sampler": "first_n", 347 | "samples": [ 348 | { 349 | "question": "There are 15 trees in the grove. Grove workers will plant trees in the grove today. After they are done, there will be 21 trees. How many trees did the grove workers plant today?", 350 | "target": "There are 15 trees originally. Then there were 21 trees after some more were planted. So there must have been 21 - 15 = 6. The answer is 6." 351 | }, 352 | { 353 | "question": "If there are 3 cars in the parking lot and 2 more cars arrive, how many cars are in the parking lot?", 354 | "target": "There are originally 3 cars. 2 more cars arrive. 3 + 2 = 5. The answer is 5." 355 | }, 356 | { 357 | "question": "Leah had 32 chocolates and her sister had 42. If they ate 35, how many pieces do they have left in total?", 358 | "target": "Originally, Leah had 32 chocolates. Her sister had 42. So in total they had 32 + 42 = 74. After eating 35, they had 74 - 35 = 39. The answer is 39." 359 | }, 360 | { 361 | "question": "Jason had 20 lollipops. He gave Denny some lollipops. Now Jason has 12 lollipops. How many lollipops did Jason give to Denny?", 362 | "target": "Jason started with 20 lollipops. Then he had 12 after giving some to Denny. So he gave Denny 20 - 12 = 8. The answer is 8." 363 | }, 364 | { 365 | "question": "Shawn has five toys. For Christmas, he got two toys each from his mom and dad. How many toys does he have now?", 366 | "target": "Shawn started with 5 toys. If he got 2 toys each from his mom and dad, then that is 4 more toys. 5 + 4 = 9. The answer is 9." 367 | }, 368 | { 369 | "question": "There were nine computers in the server room. Five more computers were installed each day, from monday to thursday. How many computers are now in the server room?", 370 | "target": "There were originally 9 computers. For each of 4 days, 5 more computers were added. So 5 * 4 = 20 computers were added. 9 + 20 is 29. The answer is 29." 371 | }, 372 | { 373 | "question": "Michael had 58 golf balls. On tuesday, he lost 23 golf balls. On wednesday, he lost 2 more. How many golf balls did he have at the end of wednesday?", 374 | "target": "Michael started with 58 golf balls. After losing 23 on tuesday, he had 58 - 23 = 35. After losing 2 more, he had 35 - 2 = 33 golf balls. The answer is 33." 375 | }, 376 | { 377 | "question": "Olivia has $23. She bought five bagels for $3 each. How much money does she have left?", 378 | "target": "Olivia had 23 dollars. 5 bagels for 3 dollars each will be 5 x 3 = 15 dollars. So she has 23 - 15 dollars left. 23 - 15 is 8. The answer is 8." 379 | } 380 | ] 381 | }, 382 | "num_fewshot": 8, 383 | "metric_list": [ 384 | { 385 | "aggregation": "mean", 386 | "higher_is_better": true, 387 | "ignore_case": true, 388 | "ignore_punctuation": false, 389 | "metric": "exact_match", 390 | "regexes_to_ignore": [ 391 | ",", 392 | "\\$", 393 | "(?s).*#### ", 394 | "\\.$" 395 | ] 396 | } 397 | ], 398 | "output_type": "generate_until", 399 | "generation_kwargs": { 400 | "do_sample": false, 401 | "until": [ 402 | "Q:", 403 | "", 404 | "<|im_end|>" 405 | ] 406 | }, 407 | "repeats": 1, 408 | "filter_list": [ 409 | { 410 | "filter": [ 411 | { 412 | "function": "regex", 413 | "regex_pattern": "The answer is (\\-?[0-9\\.\\,]+)." 414 | }, 415 | { 416 | "function": "take_first" 417 | } 418 | ], 419 | "name": "strict-match" 420 | }, 421 | { 422 | "filter": [ 423 | { 424 | "function": "regex", 425 | "group_select": -1, 426 | "regex_pattern": "(-?[$0-9.,]{2,})|(-?[0-9]+)" 427 | }, 428 | { 429 | "function": "take_first" 430 | } 431 | ], 432 | "name": "flexible-extract" 433 | } 434 | ], 435 | "should_decontaminate": false, 436 | "metadata": { 437 | "version": 3.0, 438 | "pretrained": "merged_models/Llama-3.2-3B_merged/RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1" 439 | } 440 | }, 441 | "hellaswag_de": { 442 | "task": "hellaswag_de", 443 | "tag": [ 444 | "hellaswag_multilingual" 445 | ], 446 | "dataset_path": "alexandrainst/m_hellaswag", 447 | "dataset_name": "de", 448 | "validation_split": "val", 449 | "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc):\n ctx = doc[\"ctx_a\"] + \" \" + doc[\"ctx_b\"].capitalize()\n out_doc = {\n \"query\": preprocess(doc[\"activity_label\"] + \": \" + ctx),\n \"choices\": [preprocess(ending) for ending in doc[\"endings\"]],\n \"gold\": int(doc[\"label\"]),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n", 450 | "doc_to_text": "query", 451 | "doc_to_target": "{{label.lstrip()}}", 452 | "unsafe_code": false, 453 | "doc_to_choice": "choices", 454 | "description": "", 455 | "target_delimiter": " ", 456 | "fewshot_delimiter": "\n\n", 457 | "num_fewshot": 0, 458 | "metric_list": [ 459 | { 460 | "metric": "acc", 461 | "aggregation": "mean", 462 | "higher_is_better": true 463 | }, 464 | { 465 | "metric": "acc_norm", 466 | "aggregation": "mean", 467 | "higher_is_better": true 468 | } 469 | ], 470 | "output_type": "multiple_choice", 471 | "repeats": 1, 472 | "should_decontaminate": false, 473 | "metadata": { 474 | "version": 1.0, 475 | "pretrained": "merged_models/Llama-3.2-3B_merged/RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1" 476 | } 477 | }, 478 | "hellaswag_es": { 479 | "task": "hellaswag_es", 480 | "tag": [ 481 | "hellaswag_multilingual" 482 | ], 483 | "dataset_path": "alexandrainst/m_hellaswag", 484 | "dataset_name": "es", 485 | "validation_split": "val", 486 | "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc):\n ctx = doc[\"ctx_a\"] + \" \" + doc[\"ctx_b\"].capitalize()\n out_doc = {\n \"query\": preprocess(doc[\"activity_label\"] + \": \" + ctx),\n \"choices\": [preprocess(ending) for ending in doc[\"endings\"]],\n \"gold\": int(doc[\"label\"]),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n", 487 | "doc_to_text": "query", 488 | "doc_to_target": "{{label.lstrip()}}", 489 | "unsafe_code": false, 490 | "doc_to_choice": "choices", 491 | "description": "", 492 | "target_delimiter": " ", 493 | "fewshot_delimiter": "\n\n", 494 | "num_fewshot": 0, 495 | "metric_list": [ 496 | { 497 | "metric": "acc", 498 | "aggregation": "mean", 499 | "higher_is_better": true 500 | }, 501 | { 502 | "metric": "acc_norm", 503 | "aggregation": "mean", 504 | "higher_is_better": true 505 | } 506 | ], 507 | "output_type": "multiple_choice", 508 | "repeats": 1, 509 | "should_decontaminate": false, 510 | "metadata": { 511 | "version": 1.0, 512 | "pretrained": "merged_models/Llama-3.2-3B_merged/RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1" 513 | } 514 | }, 515 | "hellaswag_fr": { 516 | "task": "hellaswag_fr", 517 | "tag": [ 518 | "hellaswag_multilingual" 519 | ], 520 | "dataset_path": "alexandrainst/m_hellaswag", 521 | "dataset_name": "fr", 522 | "validation_split": "val", 523 | "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc):\n ctx = doc[\"ctx_a\"] + \" \" + doc[\"ctx_b\"].capitalize()\n out_doc = {\n \"query\": preprocess(doc[\"activity_label\"] + \": \" + ctx),\n \"choices\": [preprocess(ending) for ending in doc[\"endings\"]],\n \"gold\": int(doc[\"label\"]),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n", 524 | "doc_to_text": "query", 525 | "doc_to_target": "{{label.lstrip()}}", 526 | "unsafe_code": false, 527 | "doc_to_choice": "choices", 528 | "description": "", 529 | "target_delimiter": " ", 530 | "fewshot_delimiter": "\n\n", 531 | "num_fewshot": 0, 532 | "metric_list": [ 533 | { 534 | "metric": "acc", 535 | "aggregation": "mean", 536 | "higher_is_better": true 537 | }, 538 | { 539 | "metric": "acc_norm", 540 | "aggregation": "mean", 541 | "higher_is_better": true 542 | } 543 | ], 544 | "output_type": "multiple_choice", 545 | "repeats": 1, 546 | "should_decontaminate": false, 547 | "metadata": { 548 | "version": 1.0, 549 | "pretrained": "merged_models/Llama-3.2-3B_merged/RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1" 550 | } 551 | }, 552 | "hellaswag_ru": { 553 | "task": "hellaswag_ru", 554 | "tag": [ 555 | "hellaswag_multilingual" 556 | ], 557 | "dataset_path": "alexandrainst/m_hellaswag", 558 | "dataset_name": "ru", 559 | "validation_split": "val", 560 | "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc):\n ctx = doc[\"ctx_a\"] + \" \" + doc[\"ctx_b\"].capitalize()\n out_doc = {\n \"query\": preprocess(doc[\"activity_label\"] + \": \" + ctx),\n \"choices\": [preprocess(ending) for ending in doc[\"endings\"]],\n \"gold\": int(doc[\"label\"]),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n", 561 | "doc_to_text": "query", 562 | "doc_to_target": "{{label.lstrip()}}", 563 | "unsafe_code": false, 564 | "doc_to_choice": "choices", 565 | "description": "", 566 | "target_delimiter": " ", 567 | "fewshot_delimiter": "\n\n", 568 | "num_fewshot": 0, 569 | "metric_list": [ 570 | { 571 | "metric": "acc", 572 | "aggregation": "mean", 573 | "higher_is_better": true 574 | }, 575 | { 576 | "metric": "acc_norm", 577 | "aggregation": "mean", 578 | "higher_is_better": true 579 | } 580 | ], 581 | "output_type": "multiple_choice", 582 | "repeats": 1, 583 | "should_decontaminate": false, 584 | "metadata": { 585 | "version": 1.0, 586 | "pretrained": "merged_models/Llama-3.2-3B_merged/RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1" 587 | } 588 | }, 589 | "hendrycks_math_algebra": { 590 | "task": "hendrycks_math_algebra", 591 | "tag": [ 592 | "math_word_problems" 593 | ], 594 | "dataset_path": "EleutherAI/hendrycks_math", 595 | "dataset_name": "algebra", 596 | "training_split": "train", 597 | "test_split": "test", 598 | "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n", 599 | "doc_to_text": "Problem: {{problem}}\nAnswer:", 600 | "doc_to_target": "{{answer}}", 601 | "unsafe_code": false, 602 | "process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n", 603 | "description": "", 604 | "target_delimiter": " ", 605 | "fewshot_delimiter": "\n\n", 606 | "num_fewshot": 0, 607 | "metric_list": [ 608 | { 609 | "metric": "exact_match", 610 | "aggregation": "mean", 611 | "higher_is_better": true 612 | } 613 | ], 614 | "output_type": "generate_until", 615 | "generation_kwargs": { 616 | "until": [ 617 | "Problem:" 618 | ], 619 | "do_sample": false, 620 | "temperature": 0.0 621 | }, 622 | "repeats": 1, 623 | "should_decontaminate": false, 624 | "metadata": { 625 | "version": 1.0, 626 | "pretrained": "merged_models/Llama-3.2-3B_merged/RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1" 627 | } 628 | }, 629 | "hendrycks_math_counting_and_prob": { 630 | "task": "hendrycks_math_counting_and_prob", 631 | "tag": [ 632 | "math_word_problems" 633 | ], 634 | "dataset_path": "EleutherAI/hendrycks_math", 635 | "dataset_name": "counting_and_probability", 636 | "training_split": "train", 637 | "test_split": "test", 638 | "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n", 639 | "doc_to_text": "Problem: {{problem}}\nAnswer:", 640 | "doc_to_target": "{{answer}}", 641 | "unsafe_code": false, 642 | "process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n", 643 | "description": "", 644 | "target_delimiter": " ", 645 | "fewshot_delimiter": "\n\n", 646 | "num_fewshot": 0, 647 | "metric_list": [ 648 | { 649 | "metric": "exact_match", 650 | "aggregation": "mean", 651 | "higher_is_better": true 652 | } 653 | ], 654 | "output_type": "generate_until", 655 | "generation_kwargs": { 656 | "until": [ 657 | "Problem:" 658 | ], 659 | "do_sample": false, 660 | "temperature": 0.0 661 | }, 662 | "repeats": 1, 663 | "should_decontaminate": false, 664 | "metadata": { 665 | "version": 1.0, 666 | "pretrained": "merged_models/Llama-3.2-3B_merged/RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1" 667 | } 668 | }, 669 | "hendrycks_math_geometry": { 670 | "task": "hendrycks_math_geometry", 671 | "tag": [ 672 | "math_word_problems" 673 | ], 674 | "dataset_path": "EleutherAI/hendrycks_math", 675 | "dataset_name": "geometry", 676 | "training_split": "train", 677 | "test_split": "test", 678 | "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n", 679 | "doc_to_text": "Problem: {{problem}}\nAnswer:", 680 | "doc_to_target": "{{answer}}", 681 | "unsafe_code": false, 682 | "process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n", 683 | "description": "", 684 | "target_delimiter": " ", 685 | "fewshot_delimiter": "\n\n", 686 | "num_fewshot": 0, 687 | "metric_list": [ 688 | { 689 | "metric": "exact_match", 690 | "aggregation": "mean", 691 | "higher_is_better": true 692 | } 693 | ], 694 | "output_type": "generate_until", 695 | "generation_kwargs": { 696 | "until": [ 697 | "Problem:" 698 | ], 699 | "do_sample": false, 700 | "temperature": 0.0 701 | }, 702 | "repeats": 1, 703 | "should_decontaminate": false, 704 | "metadata": { 705 | "version": 1.0, 706 | "pretrained": "merged_models/Llama-3.2-3B_merged/RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1" 707 | } 708 | }, 709 | "hendrycks_math_intermediate_algebra": { 710 | "task": "hendrycks_math_intermediate_algebra", 711 | "tag": [ 712 | "math_word_problems" 713 | ], 714 | "dataset_path": "EleutherAI/hendrycks_math", 715 | "dataset_name": "intermediate_algebra", 716 | "training_split": "train", 717 | "test_split": "test", 718 | "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n", 719 | "doc_to_text": "Problem: {{problem}}\nAnswer:", 720 | "doc_to_target": "{{answer}}", 721 | "unsafe_code": false, 722 | "process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n", 723 | "description": "", 724 | "target_delimiter": " ", 725 | "fewshot_delimiter": "\n\n", 726 | "num_fewshot": 0, 727 | "metric_list": [ 728 | { 729 | "metric": "exact_match", 730 | "aggregation": "mean", 731 | "higher_is_better": true 732 | } 733 | ], 734 | "output_type": "generate_until", 735 | "generation_kwargs": { 736 | "until": [ 737 | "Problem:" 738 | ], 739 | "do_sample": false, 740 | "temperature": 0.0 741 | }, 742 | "repeats": 1, 743 | "should_decontaminate": false, 744 | "metadata": { 745 | "version": 1.0, 746 | "pretrained": "merged_models/Llama-3.2-3B_merged/RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1" 747 | } 748 | }, 749 | "hendrycks_math_num_theory": { 750 | "task": "hendrycks_math_num_theory", 751 | "tag": [ 752 | "math_word_problems" 753 | ], 754 | "dataset_path": "EleutherAI/hendrycks_math", 755 | "dataset_name": "number_theory", 756 | "training_split": "train", 757 | "test_split": "test", 758 | "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n", 759 | "doc_to_text": "Problem: {{problem}}\nAnswer:", 760 | "doc_to_target": "{{answer}}", 761 | "unsafe_code": false, 762 | "process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n", 763 | "description": "", 764 | "target_delimiter": " ", 765 | "fewshot_delimiter": "\n\n", 766 | "num_fewshot": 0, 767 | "metric_list": [ 768 | { 769 | "metric": "exact_match", 770 | "aggregation": "mean", 771 | "higher_is_better": true 772 | } 773 | ], 774 | "output_type": "generate_until", 775 | "generation_kwargs": { 776 | "until": [ 777 | "Problem:" 778 | ], 779 | "do_sample": false, 780 | "temperature": 0.0 781 | }, 782 | "repeats": 1, 783 | "should_decontaminate": false, 784 | "metadata": { 785 | "version": 1.0, 786 | "pretrained": "merged_models/Llama-3.2-3B_merged/RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1" 787 | } 788 | }, 789 | "hendrycks_math_prealgebra": { 790 | "task": "hendrycks_math_prealgebra", 791 | "tag": [ 792 | "math_word_problems" 793 | ], 794 | "dataset_path": "EleutherAI/hendrycks_math", 795 | "dataset_name": "prealgebra", 796 | "training_split": "train", 797 | "test_split": "test", 798 | "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n", 799 | "doc_to_text": "Problem: {{problem}}\nAnswer:", 800 | "doc_to_target": "{{answer}}", 801 | "unsafe_code": false, 802 | "process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n", 803 | "description": "", 804 | "target_delimiter": " ", 805 | "fewshot_delimiter": "\n\n", 806 | "num_fewshot": 0, 807 | "metric_list": [ 808 | { 809 | "metric": "exact_match", 810 | "aggregation": "mean", 811 | "higher_is_better": true 812 | } 813 | ], 814 | "output_type": "generate_until", 815 | "generation_kwargs": { 816 | "until": [ 817 | "Problem:" 818 | ], 819 | "do_sample": false, 820 | "temperature": 0.0 821 | }, 822 | "repeats": 1, 823 | "should_decontaminate": false, 824 | "metadata": { 825 | "version": 1.0, 826 | "pretrained": "merged_models/Llama-3.2-3B_merged/RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1" 827 | } 828 | }, 829 | "hendrycks_math_precalc": { 830 | "task": "hendrycks_math_precalc", 831 | "tag": [ 832 | "math_word_problems" 833 | ], 834 | "dataset_path": "EleutherAI/hendrycks_math", 835 | "dataset_name": "precalculus", 836 | "training_split": "train", 837 | "test_split": "test", 838 | "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n", 839 | "doc_to_text": "Problem: {{problem}}\nAnswer:", 840 | "doc_to_target": "{{answer}}", 841 | "unsafe_code": false, 842 | "process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n", 843 | "description": "", 844 | "target_delimiter": " ", 845 | "fewshot_delimiter": "\n\n", 846 | "num_fewshot": 0, 847 | "metric_list": [ 848 | { 849 | "metric": "exact_match", 850 | "aggregation": "mean", 851 | "higher_is_better": true 852 | } 853 | ], 854 | "output_type": "generate_until", 855 | "generation_kwargs": { 856 | "until": [ 857 | "Problem:" 858 | ], 859 | "do_sample": false, 860 | "temperature": 0.0 861 | }, 862 | "repeats": 1, 863 | "should_decontaminate": false, 864 | "metadata": { 865 | "version": 1.0, 866 | "pretrained": "merged_models/Llama-3.2-3B_merged/RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1" 867 | } 868 | }, 869 | "ifeval": { 870 | "task": "ifeval", 871 | "dataset_path": "google/IFEval", 872 | "test_split": "train", 873 | "doc_to_text": "prompt", 874 | "doc_to_target": 0, 875 | "unsafe_code": false, 876 | "process_results": "def process_results(doc, results):\n inp = InputExample(\n key=doc[\"key\"],\n instruction_id_list=doc[\"instruction_id_list\"],\n prompt=doc[\"prompt\"],\n kwargs=doc[\"kwargs\"],\n )\n response = results[0]\n\n out_strict = test_instruction_following_strict(inp, response)\n out_loose = test_instruction_following_loose(inp, response)\n\n return {\n \"prompt_level_strict_acc\": out_strict.follow_all_instructions,\n \"inst_level_strict_acc\": out_strict.follow_instruction_list,\n \"prompt_level_loose_acc\": out_loose.follow_all_instructions,\n \"inst_level_loose_acc\": out_loose.follow_instruction_list,\n }\n", 877 | "description": "", 878 | "target_delimiter": " ", 879 | "fewshot_delimiter": "\n\n", 880 | "num_fewshot": 0, 881 | "metric_list": [ 882 | { 883 | "metric": "prompt_level_strict_acc", 884 | "aggregation": "mean", 885 | "higher_is_better": true 886 | }, 887 | { 888 | "metric": "inst_level_strict_acc", 889 | "aggregation": "def agg_inst_level_acc(items):\n flat_items = [item for sublist in items for item in sublist]\n inst_level_acc = sum(flat_items) / len(flat_items)\n return inst_level_acc\n", 890 | "higher_is_better": true 891 | }, 892 | { 893 | "metric": "prompt_level_loose_acc", 894 | "aggregation": "mean", 895 | "higher_is_better": true 896 | }, 897 | { 898 | "metric": "inst_level_loose_acc", 899 | "aggregation": "def agg_inst_level_acc(items):\n flat_items = [item for sublist in items for item in sublist]\n inst_level_acc = sum(flat_items) / len(flat_items)\n return inst_level_acc\n", 900 | "higher_is_better": true 901 | } 902 | ], 903 | "output_type": "generate_until", 904 | "generation_kwargs": { 905 | "until": [], 906 | "do_sample": false, 907 | "temperature": 0.0, 908 | "max_gen_toks": 1280 909 | }, 910 | "repeats": 1, 911 | "should_decontaminate": false, 912 | "metadata": { 913 | "version": 4.0, 914 | "pretrained": "merged_models/Llama-3.2-3B_merged/RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1" 915 | } 916 | }, 917 | "m_mmlu_de": { 918 | "task": "m_mmlu_de", 919 | "tag": [ 920 | "m_mmlu" 921 | ], 922 | "dataset_path": "alexandrainst/m_mmlu", 923 | "dataset_name": "de", 924 | "test_split": "test", 925 | "fewshot_split": "train", 926 | "doc_to_text": "{{instruction.strip()}}\nA. {{option_a}}\nB. {{option_b}}\nC. {{option_c}}\nD. {{option_d}}\nAnswer:", 927 | "doc_to_target": "answer", 928 | "unsafe_code": false, 929 | "doc_to_choice": [ 930 | "A", 931 | "B", 932 | "C", 933 | "D" 934 | ], 935 | "description": "", 936 | "target_delimiter": " ", 937 | "fewshot_delimiter": "\n\n", 938 | "fewshot_config": { 939 | "sampler": "first_n" 940 | }, 941 | "num_fewshot": 0, 942 | "metric_list": [ 943 | { 944 | "metric": "acc", 945 | "aggregation": "mean", 946 | "higher_is_better": true 947 | } 948 | ], 949 | "output_type": "multiple_choice", 950 | "repeats": 1, 951 | "should_decontaminate": false, 952 | "metadata": { 953 | "version": 0.0, 954 | "pretrained": "merged_models/Llama-3.2-3B_merged/RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1" 955 | } 956 | }, 957 | "m_mmlu_es": { 958 | "task": "m_mmlu_es", 959 | "tag": [ 960 | "m_mmlu" 961 | ], 962 | "dataset_path": "alexandrainst/m_mmlu", 963 | "dataset_name": "es", 964 | "test_split": "test", 965 | "fewshot_split": "train", 966 | "doc_to_text": "{{instruction.strip()}}\nA. {{option_a}}\nB. {{option_b}}\nC. {{option_c}}\nD. {{option_d}}\nAnswer:", 967 | "doc_to_target": "answer", 968 | "unsafe_code": false, 969 | "doc_to_choice": [ 970 | "A", 971 | "B", 972 | "C", 973 | "D" 974 | ], 975 | "description": "", 976 | "target_delimiter": " ", 977 | "fewshot_delimiter": "\n\n", 978 | "fewshot_config": { 979 | "sampler": "first_n" 980 | }, 981 | "num_fewshot": 0, 982 | "metric_list": [ 983 | { 984 | "metric": "acc", 985 | "aggregation": "mean", 986 | "higher_is_better": true 987 | } 988 | ], 989 | "output_type": "multiple_choice", 990 | "repeats": 1, 991 | "should_decontaminate": false, 992 | "metadata": { 993 | "version": 0.0, 994 | "pretrained": "merged_models/Llama-3.2-3B_merged/RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1" 995 | } 996 | }, 997 | "m_mmlu_fr": { 998 | "task": "m_mmlu_fr", 999 | "tag": [ 1000 | "m_mmlu" 1001 | ], 1002 | "dataset_path": "alexandrainst/m_mmlu", 1003 | "dataset_name": "fr", 1004 | "test_split": "test", 1005 | "fewshot_split": "train", 1006 | "doc_to_text": "{{instruction.strip()}}\nA. {{option_a}}\nB. {{option_b}}\nC. 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"pretrained=merged_models/Llama-3.2-3B_merged/RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1", 1293 | "batch_size": "auto", 1294 | "batch_sizes": [], 1295 | "device": "cuda:0", 1296 | "use_cache": null, 1297 | "limit": null, 1298 | "bootstrap_iters": 100000, 1299 | "gen_kwargs": null, 1300 | "random_seed": 0, 1301 | "numpy_seed": 1234, 1302 | "torch_seed": 1234, 1303 | "fewshot_seed": 1234 1304 | }, 1305 | "git_hash": "f91dd3c", 1306 | "date": 1763359638.5471172, 1307 | "pretty_env_info": "PyTorch version: 2.8.0+cu128\nIs debug build: False\nCUDA used to build PyTorch: 12.8\nROCM used to build PyTorch: N/A\n\nOS: Ubuntu 20.04.3 LTS (x86_64)\nGCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0\nClang version: Could not collect\nCMake version: version 3.16.3\nLibc version: glibc-2.31\n\nPython version: 3.10.9 (main, Mar 8 2023, 10:47:38) [GCC 11.2.0] (64-bit runtime)\nPython platform: Linux-5.15.0-69-generic-x86_64-with-glibc2.31\nIs CUDA available: True\nCUDA runtime version: Could not collect\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: GPU 0: NVIDIA A100-PCIE-40GB\nNvidia driver version: 570.133.20\ncuDNN version: Could not collect\nHIP runtime version: N/A\nMIOpen runtime version: N/A\nIs XNNPACK available: True\n\nCPU:\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nByte Order: Little Endian\nAddress sizes: 46 bits physical, 57 bits virtual\nCPU(s): 52\nOn-line CPU(s) list: 0-51\nThread(s) per core: 1\nCore(s) per socket: 26\nSocket(s): 2\nNUMA node(s): 4\nVendor ID: GenuineIntel\nCPU family: 6\nModel: 106\nModel name: Intel(R) Xeon(R) Gold 5320 CPU @ 2.20GHz\nStepping: 6\nCPU MHz: 800.000\nCPU max MHz: 3400.0000\nCPU min MHz: 800.0000\nBogoMIPS: 4400.00\nL1d cache: 2.4 MiB\nL1i cache: 1.6 MiB\nL2 cache: 65 MiB\nL3 cache: 78 MiB\nNUMA node0 CPU(s): 0,4,8,12,16,20,24,28,32,36,40,44,48\nNUMA node1 CPU(s): 2,6,10,14,18,22,26,30,34,38,42,46,50\nNUMA node2 CPU(s): 1,5,9,13,17,21,25,29,33,37,41,45,49\nNUMA node3 CPU(s): 3,7,11,15,19,23,27,31,35,39,43,47,51\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT disabled\nVulnerability Retbleed: Not affected\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Not affected\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 invpcid_single intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect wbnoinvd dtherm ida arat pln pts avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid fsrm md_clear pconfig flush_l1d arch_capabilities\n\nVersions of relevant libraries:\n[pip3] numpy==2.2.6\n[pip3] nvidia-cublas-cu12==12.8.4.1\n[pip3] nvidia-cuda-cupti-cu12==12.8.90\n[pip3] nvidia-cuda-nvrtc-cu12==12.8.93\n[pip3] nvidia-cuda-runtime-cu12==12.8.90\n[pip3] nvidia-cudnn-cu12==9.10.2.21\n[pip3] nvidia-cufft-cu12==11.3.3.83\n[pip3] nvidia-curand-cu12==10.3.9.90\n[pip3] nvidia-cusolver-cu12==11.7.3.90\n[pip3] nvidia-cusparse-cu12==12.5.8.93\n[pip3] nvidia-cusparselt-cu12==0.7.1\n[pip3] nvidia-nccl-cu12==2.27.3\n[pip3] nvidia-nvjitlink-cu12==12.8.93\n[pip3] nvidia-nvtx-cu12==12.8.90\n[pip3] torch==2.8.0\n[pip3] torchaudio==2.8.0\n[pip3] torchvision==0.23.0\n[pip3] triton==3.4.0\n[conda] numpy 2.2.6 pypi_0 pypi\n[conda] nvidia-cublas-cu12 12.8.4.1 pypi_0 pypi\n[conda] nvidia-cuda-cupti-cu12 12.8.90 pypi_0 pypi\n[conda] nvidia-cuda-nvrtc-cu12 12.8.93 pypi_0 pypi\n[conda] nvidia-cuda-runtime-cu12 12.8.90 pypi_0 pypi\n[conda] nvidia-cudnn-cu12 9.10.2.21 pypi_0 pypi\n[conda] nvidia-cufft-cu12 11.3.3.83 pypi_0 pypi\n[conda] nvidia-curand-cu12 10.3.9.90 pypi_0 pypi\n[conda] nvidia-cusolver-cu12 11.7.3.90 pypi_0 pypi\n[conda] nvidia-cusparse-cu12 12.5.8.93 pypi_0 pypi\n[conda] nvidia-cusparselt-cu12 0.7.1 pypi_0 pypi\n[conda] nvidia-nccl-cu12 2.27.3 pypi_0 pypi\n[conda] nvidia-nvjitlink-cu12 12.8.93 pypi_0 pypi\n[conda] nvidia-nvtx-cu12 12.8.90 pypi_0 pypi\n[conda] torch 2.8.0 pypi_0 pypi\n[conda] torchaudio 2.8.0 pypi_0 pypi\n[conda] torchvision 0.23.0 pypi_0 pypi\n[conda] triton 3.4.0 pypi_0 pypi", 1308 | "transformers_version": "4.57.1", 1309 | "lm_eval_version": "0.4.9.1", 1310 | "upper_git_hash": null, 1311 | "tokenizer_pad_token": [ 1312 | "<|end_of_text|>", 1313 | "128001" 1314 | ], 1315 | "tokenizer_eos_token": [ 1316 | "<|end_of_text|>", 1317 | "128001" 1318 | ], 1319 | "tokenizer_bos_token": [ 1320 | "<|begin_of_text|>", 1321 | "128000" 1322 | ], 1323 | "eot_token_id": 128001, 1324 | "max_length": 131072, 1325 | "task_hashes": {}, 1326 | "model_source": "vllm", 1327 | "model_name": "merged_models/Llama-3.2-3B_merged/RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1", 1328 | "model_name_sanitized": "merged_models__Llama-3.2-3B_merged__RegMeanPlusPlus_task_names_DartMath-WildguardMix-MagiCoder-Aya-Tulu3IF_reduction_0.1", 1329 | "system_instruction": null, 1330 | "system_instruction_sha": null, 1331 | "fewshot_as_multiturn": false, 1332 | "chat_template": null, 1333 | "chat_template_sha": null, 1334 | "start_time": 15734951.01637468, 1335 | "end_time": 15737821.727023767, 1336 | "total_evaluation_time_seconds": "2870.710649088025" 1337 | } --------------------------------------------------------------------------------