├── data ├── predictions │ ├── MATH │ │ └── Arithmo-Mistral-7B │ │ │ ├── predictions_Arithmo_math_zero_shot_CoT.json │ │ │ └── incorrect_predictions_Arithmo_math_zero_shot_CoT.json │ └── gsm8k │ │ └── Arithmo-Mistral-7B │ │ ├── predictions_Arithmo_gsm8k_zero_shot_CoT.json │ │ ├── predictions_Arithmo_gsm8k_zero_shot_PoT.json │ │ └── gsm8k_zero_shot_PoT_results.txt └── python_coding_prompts.txt ├── eval ├── gsm8k │ ├── gsm8k_compute_metric_zero_shot_PoT.py │ ├── gsm8k_compute_metric_zero_shot_CoT.py │ ├── gsm8k_write_zero_shot_PoT_outputs.py │ ├── gsm8k_generate_response_zero_shot_CoT.py │ └── gsm8k_generate_response_zero_shot_PoT.py └── MATH │ ├── MATH_generate_response_zero_shot_CoT.py │ └── MATH_compute_metric_zero_shot_CoT.py ├── query_model.py ├── data_prep └── prepare_model_traininig_data.py ├── LICENSE └── README.md /data/predictions/MATH/Arithmo-Mistral-7B/predictions_Arithmo_math_zero_shot_CoT.json: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:08519621ba0daa4643c758179571aba56952c0fd758f946c4d876662d499d804 3 | size 8716400 4 | -------------------------------------------------------------------------------- /data/predictions/gsm8k/Arithmo-Mistral-7B/predictions_Arithmo_gsm8k_zero_shot_CoT.json: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:a101e55977c65257064721c36a2a49e2132b87ececda21cb88c59e3befc524d2 3 | size 1644185 4 | -------------------------------------------------------------------------------- /data/predictions/gsm8k/Arithmo-Mistral-7B/predictions_Arithmo_gsm8k_zero_shot_PoT.json: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:abe5504c9e1b0ee7f7cb8b98b58394eb1dd57b505100cc0baf1c88d97b49f1dc 3 | size 1661651 4 | -------------------------------------------------------------------------------- /data/predictions/MATH/Arithmo-Mistral-7B/incorrect_predictions_Arithmo_math_zero_shot_CoT.json: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:9e510639bde5d71dd19676fe597dc3ce227749af82198b5db26e5f36dd4b79fb 3 | size 7663790 4 | -------------------------------------------------------------------------------- /eval/gsm8k/gsm8k_compute_metric_zero_shot_PoT.py: -------------------------------------------------------------------------------- 1 | from fractions import Fraction 2 | 3 | lines = open("data/predictions/gsm8k/Arithmo-Mistral-7B//gsm8k_zero_shot_PoT_results.txt", "r").readlines() 4 | lines = [line.strip() for line in lines] 5 | 6 | predicted = None 7 | correct, total = 0,0 8 | for line in lines: 9 | if line.startswith("==="): 10 | predicted, truth = None, None 11 | elif predicted is None: 12 | # Assign a default value when predicted answer contains alphabets or spaces. This is not expected for GSM8K data and is inaccurate. There are very few cases. 13 | if any(c.isalpha() for c in line) or " " in line: 14 | predicted = "1e-9" # some default value 15 | else: 16 | if "/" in line: # eg: "27/3" => 9.0. Very few cases. 17 | predicted = float(Fraction(line)) 18 | else: 19 | predicted = float(line) 20 | else: 21 | if predicted == float(line): 22 | correct += 1 23 | total += 1 24 | print(f"Total Instances: {total}, Correct Count: {correct}, Accuracy (Correct Count/Total Instances): {correct/total}") 25 | 26 | -------------------------------------------------------------------------------- /eval/gsm8k/gsm8k_compute_metric_zero_shot_CoT.py: -------------------------------------------------------------------------------- 1 | import json 2 | 3 | file_path = "data/predictions/gsm8k/Arithmo-Mistral-7B/predictions_Arithmo_gsm8k_zero_shot_CoT.json" 4 | 5 | def extract_ground_truth_answer(ground_truth_gen): 6 | # there are cases when 250000 is written as 250,000. Normalize it to 250000 7 | answer = ground_truth_gen.split("####")[-1].strip().replace(",", "") 8 | return answer 9 | 10 | def extract_predcited_answer(predicted_gen): 11 | if "The answer is:" in predicted_gen: 12 | answer = predicted_gen.rsplit("The answer is:")[-1].strip() 13 | return answer 14 | elif "The answer is " in predicted_gen: 15 | answer = predicted_gen.rsplit("The answer is ")[-1].strip() 16 | return answer 17 | else: # Answer couldn't be found in generated text. Return empty string. 18 | return "" 19 | 20 | count, total = 0,0 21 | with open(file_path, 'r') as f: 22 | data = json.load(f) 23 | for d in data: 24 | question = d["question"] 25 | ground_truth_gen = d["ground_truth"] 26 | predicted_gen = d["prediction"] 27 | 28 | ground_truth_answer = extract_ground_truth_answer(ground_truth_gen) 29 | predicted_answer = extract_predcited_answer(predicted_gen) 30 | if ground_truth_answer == predicted_answer: 31 | count += 1 32 | total += 1 33 | print(f"Total Instances: {total}, Correct Count: {count}, Accuracy (Correct Count/Total Instances): {count/total}") 34 | -------------------------------------------------------------------------------- /eval/gsm8k/gsm8k_write_zero_shot_PoT_outputs.py: -------------------------------------------------------------------------------- 1 | # Run this file as 'python eval/gsm8k/gsm8k_write_zero_shot_PoT_outputs.py > data/predictions/gsm8k/Arithmo-Mistral-7B/gsm8k_zero_shot_PoT_results.txt' 2 | 3 | import json 4 | 5 | file_path = "data/predictions/gsm8k/Arithmo-Mistral-7B/predictions_Arithmo_gsm8k_zero_shot_PoT.json" 6 | 7 | def extract_ground_truth_answer(ground_truth_gen): 8 | # there are cases when 250000 is written as 250,000 9 | answer = ground_truth_gen.split("####")[-1].strip().replace(",", "") 10 | return answer 11 | 12 | def extract_python_program(predicted_gen): 13 | if "Answer: " in predicted_gen: 14 | program = predicted_gen.rsplit("Answer: ")[-1].strip() 15 | else: 16 | program = "" 17 | print(predicted_gen) 18 | return program 19 | 20 | 21 | 22 | with open(file_path, 'r') as f: 23 | data = json.load(f) 24 | for i, d in enumerate(data): 25 | question = d["question"] 26 | ground_truth_gen = d["ground_truth"] 27 | predicted_gen = d["prediction"] 28 | 29 | ground_truth_answer = extract_ground_truth_answer(ground_truth_gen) 30 | py_program = extract_python_program(predicted_gen) 31 | try: 32 | exec(py_program) # exec prints output of script to stdout and doesn't allow storing output in a variable. 33 | print(ground_truth_answer) 34 | print("=========") 35 | except: 36 | # Python program is not able to compile. Ignore it. 37 | pass 38 | -------------------------------------------------------------------------------- /eval/gsm8k/gsm8k_generate_response_zero_shot_CoT.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from transformers import ( 3 | AutoModelForCausalLM, 4 | AutoTokenizer 5 | ) 6 | import json 7 | from datasets import load_dataset 8 | 9 | model_path = "akjindal53244/Arithmo-Mistral-7B" 10 | 11 | device_map = {"": 0} 12 | 13 | ft_model = AutoModelForCausalLM.from_pretrained( 14 | model_path, 15 | device_map=device_map 16 | ) 17 | 18 | tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) 19 | tokenizer.pad_token = tokenizer.eos_token 20 | 21 | predictions = list() 22 | 23 | gsm8k_test = load_dataset("gsm8k", "main") 24 | dataset_size = len(gsm8k_test['test']) 25 | print(f"gsm8k_test size: {dataset_size}") 26 | 27 | count = 0 28 | # Adjust batch size based on available memory. 29 | batch_size = 16 30 | 31 | for i in range(0, dataset_size, batch_size): 32 | start = i 33 | end = start + batch_size if start + batch_size <= dataset_size else dataset_size 34 | examples = gsm8k_test["test"][start:end] 35 | input_text_ft = [f"Question: {each}\n\nAnswer:" for each in examples["question"]] 36 | inputs_ft = tokenizer(input_text_ft, return_tensors="pt", padding=True) 37 | generated_ids = ft_model.generate(**inputs_ft, max_new_tokens=1024, temperature=0.0) 38 | output = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) 39 | for j in range(len(output)): 40 | predictions.append( 41 | { 42 | "question": examples["question"][j], 43 | "ground_truth": examples['answer'][j], 44 | "prediction": output[j] 45 | } 46 | ) 47 | count += len(output) 48 | print(count) 49 | 50 | with open('data/predictions/gsm8k/Arithmo-Mistral-7B/predictions_Arithmo_gsm8k_zero_shot_CoT.json', 'w') as f: 51 | json.dump(predictions, f, indent=1) 52 | 53 | -------------------------------------------------------------------------------- /eval/gsm8k/gsm8k_generate_response_zero_shot_PoT.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from transformers import ( 3 | AutoModelForCausalLM, 4 | AutoTokenizer 5 | ) 6 | import json 7 | from datasets import load_dataset 8 | 9 | model_path = "akjindal53244/Arithmo-Mistral-7B" 10 | 11 | device_map = {"": 0} 12 | 13 | ft_model = AutoModelForCausalLM.from_pretrained( 14 | model_path, 15 | device_map=device_map 16 | ) 17 | 18 | tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) 19 | tokenizer.pad_token = tokenizer.eos_token 20 | 21 | predictions = list() 22 | 23 | gsm8k_test = load_dataset("gsm8k", "main") 24 | dataset_size = len(gsm8k_test['test']) 25 | print(f"gsm8k_test size: {dataset_size}") 26 | 27 | count = 0 28 | # Adjust batch size based on available memory. 29 | batch_size = 16 30 | 31 | 32 | 33 | for i in range(0, dataset_size, batch_size): 34 | start = i 35 | end = start + batch_size if start + batch_size <= dataset_size else dataset_size 36 | examples = gsm8k_test["test"][start:end] 37 | input_text_ft = [f"Question: {each}. Write a Python program to solve this.\n\nAnswer:" for each in examples["question"]] # Added Python prompt 38 | inputs_ft = tokenizer(input_text_ft, return_tensors="pt", padding=True) 39 | generated_ids = ft_model.generate(**inputs_ft, max_new_tokens=1024, temperature=0.0) 40 | output = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) 41 | for j in range(len(output)): 42 | predictions.append( 43 | { 44 | "question": examples["question"][j], 45 | "ground_truth": examples['answer'][j], 46 | "prediction": output[j] 47 | } 48 | ) 49 | count += len(output) 50 | print(count) 51 | 52 | with open('data/predictions/gsm8k/Arithmo-Mistral-7B/predictions_Arithmo_gsm8k_zero_shot_PoT.json', 'w') as f: 53 | json.dump(predictions, f, indent=1) 54 | 55 | -------------------------------------------------------------------------------- /eval/MATH/MATH_generate_response_zero_shot_CoT.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from transformers import ( 3 | AutoModelForCausalLM, 4 | AutoTokenizer, 5 | BitsAndBytesConfig, 6 | ) 7 | from peft import PeftModel 8 | import json 9 | 10 | model_path = "akjindal53244/Arithmo-Mistral-7B" 11 | 12 | from datasets import load_dataset, concatenate_datasets 13 | 14 | device_map = {"": 0} 15 | 16 | ft_model = AutoModelForCausalLM.from_pretrained( 17 | model_path, 18 | device_map=device_map 19 | ) 20 | 21 | tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) 22 | tokenizer.pad_token = tokenizer.eos_token 23 | 24 | predictions = list() 25 | 26 | math_test = load_dataset("competition_math") 27 | dataset_size = len(math_test['test']) 28 | print(f"math_test size: {dataset_size}") 29 | 30 | count = 0 31 | # Adjust batch size based on available memory. 32 | batch_size = 6 33 | 34 | for i in range(0, dataset_size, batch_size): 35 | start = i 36 | end = start + batch_size if start + batch_size <= dataset_size else dataset_size 37 | examples = math_test["test"][start:end] 38 | input_text_ft = [f"Question: {each}\n\nAnswer:" for each in examples["problem"]] 39 | inputs_ft = tokenizer(input_text_ft, return_tensors="pt", padding=True).to("cuda") 40 | generated_ids = ft_model.generate(**inputs_ft, max_new_tokens=2048, temperature=0.0) 41 | output = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) 42 | for j in range(len(output)): 43 | predictions.append( 44 | { 45 | "question": examples["problem"][j], 46 | "ground_truth": examples['solution'][j], 47 | "prediction": output[j] 48 | } 49 | ) 50 | count += len(output) 51 | print(count) 52 | 53 | with open('data/predictions/gsm8k/Arithmo-Mistral-7B/predictions_Arithmo_MATH_zero_shot_CoT.json', 'w') as f: 54 | json.dump(predictions, f, indent=1) 55 | 56 | 57 | -------------------------------------------------------------------------------- /query_model.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from transformers import ( 3 | AutoModelForCausalLM, 4 | AutoTokenizer, 5 | BitsAndBytesConfig, 6 | ) 7 | 8 | model_path = "akjindal53244/Arithmo-Mistral-7B" 9 | 10 | run_model_on_gpu = True 11 | 12 | ############################################################################################## 13 | # bitsandbytes parameters. Used if run_model_on_gpu = True. CPU doesn't support quantization 14 | ############################################################################################## 15 | 16 | # Activate 4-bit precision base model loading 17 | use_4bit = True 18 | 19 | # Compute dtype for 4-bit base models 20 | bnb_4bit_compute_dtype = "bfloat16" # Efficient. Newer GPUs support bfloat16 21 | 22 | # Quantization type (fp4 or nf4) 23 | bnb_4bit_quant_type = "nf4" 24 | 25 | # Activate nested quantization for 4-bit base models (double quantization) 26 | use_nested_quant = False 27 | 28 | ######################################### 29 | # Load Model and associated tokenizer. 30 | ######################################### 31 | 32 | if run_model_on_gpu: 33 | device_map = {"": 0} 34 | # Load tokenizer and model with QLoRA configuration 35 | compute_dtype = getattr(torch, bnb_4bit_compute_dtype) 36 | 37 | bnb_config = BitsAndBytesConfig( 38 | load_in_4bit=use_4bit, 39 | bnb_4bit_quant_type=bnb_4bit_quant_type, 40 | bnb_4bit_compute_dtype=compute_dtype, 41 | bnb_4bit_use_double_quant=use_nested_quant, 42 | ) 43 | arithmo_model = AutoModelForCausalLM.from_pretrained( 44 | model_path, 45 | quantization_config=bnb_config, 46 | device_map=device_map, 47 | ) 48 | else: 49 | device_map = {"": "cpu"} 50 | arithmo_model = AutoModelForCausalLM.from_pretrained( 51 | model_path, 52 | device_map=device_map, 53 | ) 54 | 55 | # Load Tokenizer 56 | tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) 57 | 58 | 59 | ############################################## 60 | # Query Model with CoT (default) and PoT 61 | ############################################## 62 | 63 | while True: 64 | input_text = input("Enter your question: ") 65 | 66 | # Default: Generate Reasoning steps i.e. CoT 67 | input_text_ft = f"Question: {input_text.strip()}\n\nAnswer:" 68 | # Uncomment this, if you want to generate python program i.e. POT 69 | # input_text_ft = f"Question: {input_text.strip()}. Write a Python program to solve this.\n\nAnswer:" 70 | 71 | if run_model_on_gpu: 72 | inputs_ft = tokenizer(input_text_ft, return_tensors="pt").to("cuda") 73 | else: 74 | inputs_ft = tokenizer(input_text_ft, return_tensors="pt") 75 | 76 | generated_ids = arithmo_model.generate(**inputs_ft, max_new_tokens=1024, temperature=0.0) 77 | output = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] 78 | print(output + "\n") -------------------------------------------------------------------------------- /data_prep/prepare_model_traininig_data.py: -------------------------------------------------------------------------------- 1 | import json 2 | import os 3 | import random 4 | 5 | from datasets import load_dataset, concatenate_datasets 6 | import numpy as np 7 | 8 | from difflib import SequenceMatcher 9 | 10 | def similar(a, b): 11 | return SequenceMatcher(None, a, b).ratio() 12 | 13 | all_python_prompts = open("data/python_coding_prompts.txt", "r").readlines() 14 | all_python_prompts = list(set([each.strip() for each in all_python_prompts])) 15 | random.shuffle(all_python_prompts) 16 | 17 | 18 | # Found these prompts in existing datasets. 19 | existing_prompts = [ 20 | "Let's write a program.", 21 | "Let's write a Python program.", 22 | "Let's program in Python in the response.", 23 | "Let's write a Python program to solve it.", 24 | "Please write a program to solve it", 25 | ] 26 | 27 | all_QA = dict() 28 | 29 | def add_python_prompt(question): 30 | question = f"{question.strip()} {random.choice(all_python_prompts)}" 31 | return question 32 | 33 | def replace_python_prompt(question): 34 | for python_prompt in existing_prompts: 35 | if python_prompt in question: 36 | question = question.replace(python_prompt, random.choice(all_python_prompts)) 37 | return question 38 | 39 | return question 40 | 41 | def modify_input(question): 42 | # For python program prompts, replace original prompt with randomly choosen python prompt. 43 | num = random.randint(1, 10) 44 | if num <= 8: 45 | question = replace_python_prompt(question) 46 | 47 | # Convert input (question) to lower case for 30% of the instances. 48 | num = random.randint(1, 10) 49 | if num <= 3: 50 | question = question.lower() 51 | return question 52 | 53 | def remove_hash(answer: str): 54 | if "####" in answer: 55 | return answer[:answer.rindex("####")].strip() 56 | return answer 57 | 58 | def format_metamath_response(answer: str, answer_identifier: str): 59 | answer_prefix_len = len(answer_identifier) 60 | if answer_identifier in answer: 61 | answer_prefix_start_idx = answer.index(answer_identifier) 62 | reasoning = remove_hash(answer[:answer_prefix_start_idx].strip()) 63 | 64 | # ==== Enable it if we want to add "answer" as part of output 65 | answer = answer[answer_prefix_start_idx:].strip() 66 | assert len(answer) > 0 67 | # answer = "Answer: " + answer 68 | return f"{reasoning}\n{answer.strip()}" 69 | else: 70 | return answer 71 | 72 | 73 | 74 | outputs = [] 75 | 76 | metamath_dataset = load_dataset("meta-math/MetaMathQA", "train") 77 | print(f"MetaMathQA dataset size: {len(metamath_dataset['train'])}") 78 | print(f"Processing MetaMathQA dataset..") 79 | for each in metamath_dataset["train"]: 80 | output = {} 81 | if each['query'].lower() not in all_QA: 82 | all_QA[each['query'].lower()] = [each['response'].lower()] 83 | elif max([similar(x, each['response'].lower()) for x in all_QA[each['query'].lower()]]) < 0.7: 84 | all_QA[each['query'].lower()].append(each['response'].lower()) 85 | else: 86 | continue 87 | 88 | output['question'] = modify_input(each['query']).strip() 89 | output['answer'] = format_metamath_response(each['response'], "The answer is:").strip() 90 | if len(output['question']) > 0 and len(output['answer']) > 0: 91 | outputs.append(output) 92 | 93 | 94 | math_instruct_dataset = load_dataset("TIGER-Lab/MathInstruct", "train") 95 | print(f"MathInstruct dataset size: {len(math_instruct_dataset['train'])}") 96 | print(f"Processing MathInstruct dataset..") 97 | for each in math_instruct_dataset["train"]: 98 | output = {} 99 | if each['instruction'].lower() not in all_QA: 100 | all_QA[each['instruction'].lower()] = [each['output'].lower()] 101 | elif max([similar(x, each['output'].lower()) for x in all_QA[each['instruction'].lower()]]) < 0.7: 102 | all_QA[each['instruction'].lower()].append(each['output'].lower()) 103 | else: 104 | continue 105 | 106 | output['question'] = modify_input(each['instruction']).strip() 107 | output['answer'] = format_metamath_response(each['output'], "The answer is").strip() 108 | if len(output['question']) > 0 and len(output['answer']) > 0: 109 | outputs.append(output) 110 | 111 | 112 | lila_ood_dataset = load_dataset("allenai/lila", 'ood') 113 | lila_ood_dataset = concatenate_datasets([lila_ood_dataset['train'], lila_ood_dataset['validation'], lila_ood_dataset['test']]) 114 | print(f"lila ood dataset size: {len(lila_ood_dataset)}") 115 | print(f"Processing lila ood dataset..") 116 | for instance in lila_ood_dataset: 117 | output = {} 118 | if instance['input'].lower() not in all_QA: 119 | all_QA[instance['input'].lower()] = [instance['output_program'].lower()] 120 | elif max([similar(x, instance['output_program'].lower()) for x in all_QA[instance['input'].lower()]]) < 0.7: 121 | all_QA[instance['input'].lower()].append(instance['output_program'].lower()) 122 | else: 123 | continue 124 | 125 | output['question'] = add_python_prompt(instance['input']).strip() 126 | output['answer'] = instance['output_program'].strip() 127 | if len(output['question']) > 0 and len(output['answer']) > 0: 128 | outputs.append(output) 129 | 130 | print(f"Original datasets size: {len(metamath_dataset['train'])+len(math_instruct_dataset['train'])+len(lila_ood_dataset)}") 131 | print(f"Prepared dataset size: {len(outputs)}") 132 | random.shuffle(outputs) 133 | 134 | print(f"Assigning train/eval splits..") 135 | train_set = outputs[:int(0.98*len(outputs))] 136 | eval_set = outputs[int(0.98*len(outputs)):] 137 | 138 | print("Writing train/eval files..") 139 | 140 | with open('data/model_training/train.json', 'w') as f: 141 | json.dump(train_set, f, indent=1) 142 | 143 | with open('data/model_training/eval.json', 'w') as f: 144 | json.dump(eval_set, f, indent=1) 145 | 146 | print("DONE!") 147 | -------------------------------------------------------------------------------- /eval/MATH/MATH_compute_metric_zero_shot_CoT.py: -------------------------------------------------------------------------------- 1 | import pprint 2 | import json 3 | 4 | incorrect_prediction_records = [] 5 | 6 | def _fix_fracs(string): 7 | substrs = string.split("\\frac") 8 | new_str = substrs[0] 9 | if len(substrs) > 1: 10 | substrs = substrs[1:] 11 | for substr in substrs: 12 | new_str += "\\frac" 13 | if substr[0] == "{": 14 | new_str += substr 15 | else: 16 | try: 17 | assert len(substr) >= 2 18 | except: 19 | return string 20 | a = substr[0] 21 | b = substr[1] 22 | if b != "{": 23 | if len(substr) > 2: 24 | post_substr = substr[2:] 25 | new_str += "{" + a + "}{" + b + "}" + post_substr 26 | else: 27 | new_str += "{" + a + "}{" + b + "}" 28 | else: 29 | if len(substr) > 2: 30 | post_substr = substr[2:] 31 | new_str += "{" + a + "}" + b + post_substr 32 | else: 33 | new_str += "{" + a + "}" + b 34 | string = new_str 35 | return string 36 | 37 | 38 | def _fix_a_slash_b(string): 39 | if len(string.split("/")) != 2: 40 | return string 41 | a = string.split("/")[0] 42 | b = string.split("/")[1] 43 | try: 44 | a = int(a) 45 | b = int(b) 46 | assert string == "{}/{}".format(a, b) 47 | new_string = "\\frac{" + str(a) + "}{" + str(b) + "}" 48 | return new_string 49 | except: 50 | return string 51 | 52 | 53 | def _remove_right_units(string): 54 | # "\\text{ " only ever occurs (at least in the val set) when describing units 55 | if "\\text{ " in string: 56 | splits = string.split("\\text{ ") 57 | assert len(splits) == 2 58 | return splits[0] 59 | else: 60 | return string 61 | 62 | 63 | def _fix_sqrt(string): 64 | if "\\sqrt" not in string: 65 | return string 66 | splits = string.split("\\sqrt") 67 | new_string = splits[0] 68 | for split in splits[1:]: 69 | if split[0] != "{": 70 | a = split[0] 71 | new_substr = "\\sqrt{" + a + "}" + split[1:] 72 | else: 73 | new_substr = "\\sqrt" + split 74 | new_string += new_substr 75 | return new_string 76 | 77 | 78 | def _strip_string(string): 79 | # linebreaks 80 | string = string.replace("\n", "") 81 | # print(string) 82 | 83 | # remove inverse spaces 84 | string = string.replace("\\!", "") 85 | # print(string) 86 | 87 | # replace \\ with \ 88 | string = string.replace("\\\\", "\\") 89 | # print(string) 90 | 91 | # replace tfrac and dfrac with frac 92 | string = string.replace("tfrac", "frac") 93 | string = string.replace("dfrac", "frac") 94 | # print(string) 95 | 96 | # remove \left and \right 97 | string = string.replace("\\left", "") 98 | string = string.replace("\\right", "") 99 | # print(string) 100 | 101 | # Remove circ (degrees) 102 | string = string.replace("^{\\circ}", "") 103 | string = string.replace("^\\circ", "") 104 | 105 | # remove dollar signs 106 | string = string.replace("\\$", "") 107 | 108 | # remove units (on the right) 109 | string = _remove_right_units(string) 110 | 111 | # remove percentage 112 | string = string.replace("\\%", "") 113 | string = string.replace("\%", "") 114 | 115 | # " 0." equivalent to " ." and "{0." equivalent to "{." Alternatively, add "0" if "." is the start of the string 116 | string = string.replace(" .", " 0.") 117 | string = string.replace("{.", "{0.") 118 | # if empty, return empty string 119 | if len(string) == 0: 120 | return string 121 | if string[0] == ".": 122 | string = "0" + string 123 | 124 | # to consider: get rid of e.g. "k = " or "q = " at beginning 125 | if len(string.split("=")) == 2: 126 | if len(string.split("=")[0]) <= 2: 127 | string = string.split("=")[1] 128 | 129 | # fix sqrt3 --> sqrt{3} 130 | string = _fix_sqrt(string) 131 | 132 | # remove spaces 133 | string = string.replace(" ", "") 134 | 135 | # \frac1b or \frac12 --> \frac{1}{b} and \frac{1}{2}, etc. Even works with \frac1{72} (but not \frac{72}1). Also does a/b --> \\frac{a}{b} 136 | string = _fix_fracs(string) 137 | 138 | # manually change 0.5 --> \frac{1}{2} 139 | if string == "0.5": 140 | string = "\\frac{1}{2}" 141 | 142 | # NOTE: X/Y changed to \frac{X}{Y} in dataset, but in simple cases fix in case the model output is X/Y 143 | string = _fix_a_slash_b(string) 144 | 145 | return string 146 | 147 | 148 | def is_equiv(str1, str2, verbose=False): 149 | if str1 is None and str2 is None: 150 | print("WARNING: Both None") 151 | return True 152 | if str1 is None or str2 is None: 153 | return False 154 | 155 | try: 156 | ss1 = _strip_string(str1) 157 | ss2 = _strip_string(str2) 158 | if verbose: 159 | print(ss1, ss2) 160 | return ss1 == ss2 161 | except: 162 | return str1 == str2 163 | 164 | 165 | def last_boxed_only_string(string): 166 | idx = string.rfind("\\boxed") 167 | if idx < 0: 168 | idx = string.rfind("\\fbox") 169 | if idx < 0: 170 | return None 171 | 172 | i = idx 173 | right_brace_idx = None 174 | num_left_braces_open = 0 175 | while i < len(string): 176 | if string[i] == "{": 177 | num_left_braces_open += 1 178 | if string[i] == "}": 179 | num_left_braces_open -= 1 180 | if num_left_braces_open == 0: 181 | right_brace_idx = i 182 | break 183 | i += 1 184 | 185 | if right_brace_idx == None: 186 | retval = None 187 | else: 188 | retval = string[idx:right_brace_idx + 1] 189 | 190 | return retval 191 | 192 | def remove_boxed(s): 193 | left = "\\boxed{" 194 | try: 195 | assert s[:len(left)] == left 196 | assert s[-1] == "}" 197 | return s[len(left):-1] 198 | except: 199 | return None 200 | 201 | def get_result(ground_truth_solution, generated_text, question, pos): 202 | answer = remove_boxed(last_boxed_only_string(ground_truth_solution)) 203 | if "The answer is:" in generated_text: 204 | predicted_answer = generated_text.rsplit("The answer is:")[-1].strip() 205 | elif "The answer is " in generated_text: 206 | predicted_answer = generated_text.rsplit("The answer is ")[-1].strip() 207 | else: # TODO: This is most likely because we stopped generation in between. There are very rare cases when model doesn't generate "The answer is" format. 208 | predicted_answer = "" # answer is missing 209 | 210 | try: 211 | equiv = is_equiv(predicted_answer, answer) 212 | except: 213 | equiv = False 214 | if not equiv: 215 | incorrect_prediction_record = { 216 | "Record#": pos+1, 217 | "question": question, 218 | "correct_answer": answer, 219 | "predicted_answer": predicted_answer, 220 | "correct_completion": ground_truth_solution, 221 | "predicted_completion": generated_text, 222 | } 223 | incorrect_prediction_records.append(incorrect_prediction_record) 224 | return equiv, predicted_answer == "" 225 | 226 | 227 | correct, total, missing_answer_count = 0, 0, 0 228 | file_path = "data/predictions/MATH/Arithmo-Mistral-7B/predictions_Arithmo_math_zero_shot_CoT.json" 229 | 230 | with open(file_path, 'r') as f: 231 | data = json.load(f) 232 | for i, d in enumerate(data): 233 | question = d["question"] 234 | ground_truth_gen = d["ground_truth"] 235 | predicted_gen = d["prediction"] 236 | is_correct, is_answer_missing = get_result(ground_truth_gen, predicted_gen, question, i) 237 | correct += is_correct 238 | total += 1 239 | missing_answer_count += is_answer_missing 240 | 241 | print(f"\nTotal Instances: {total}, Correct Count: {correct}, Accuracy (Correct Count/Total Instances): {correct/total}") 242 | print(f"\nOut of {total} instances, couldn't find answer for {missing_answer_count} instances.") 243 | 244 | with open('data/predictions/MATH/Arithmo-Mistral-7B/incorrect_predictions_Arithmo_math_zero_shot_CoT.json', 'w') as f: 245 | json.dump(incorrect_prediction_records, f, indent=1) 246 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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We also recommend that a 185 | file or class name and description of purpose be included on the 186 | same "printed page" as the copyright notice for easier 187 | identification within third-party archives. 188 | 189 | Copyright [yyyy] [name of copyright owner] 190 | 191 | Licensed under the Apache License, Version 2.0 (the "License"); 192 | you may not use this file except in compliance with the License. 193 | You may obtain a copy of the License at 194 | 195 | http://www.apache.org/licenses/LICENSE-2.0 196 | 197 | Unless required by applicable law or agreed to in writing, software 198 | distributed under the License is distributed on an "AS IS" BASIS, 199 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 200 | See the License for the specific language governing permissions and 201 | limitations under the License. 202 | -------------------------------------------------------------------------------- /data/python_coding_prompts.txt: -------------------------------------------------------------------------------- 1 | Invent a Python code that dances through this mathematical maze with clever reasoning. 2 | Solve this mathematical enigma with a Python program that thinks logically. 3 | Pythonize the problem. 4 | Let's do Python coding. 5 | Utilize your programming finesse to fashion a Python solution for this intricate mathematical query. 6 | Pythonize your solution now. 7 | Solve it, Python way. 8 | Solve it with Python. 9 | Craft a Python program that adeptly reasons through this math puzzle, leading to a creative and elegant solution. 10 | I challenge you to write a program that employs logical reasoning to answer this math query, ensuring it compiles and provides the correct result. 11 | Pythonize this task. 12 | Can you write a Python solution for this math question that requires reasoning? 13 | Python it into reality. 14 | How would you write a Python program that applies logical thinking to this math challenge and produces a correct solution? 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Your program should be simple, error-free, and accurate. 155 | Think through this math problem and Python your way to a solution. 156 | Craft a Python program that reasons through this math puzzle. 157 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ## Updates 2 | 3 | ### [January 2024] New Model Release: Arithmo2-Mistral-7B 4 | 5 | **Arithmo2-Mistral-7B** model improves initially released Arithmo-Mistral-7B model on both GSM8K and MATH benchmarks. Specifically, there is absolute improvement of **+1.7% on GSM8K, +3.0% on GSM8K PoT, and +1.9% on MATH benchmarks**. We release both [merged model](https://huggingface.co/upaya07/Arithmo2-Mistral-7B) and [LoRA Adapter](https://huggingface.co/upaya07/Arithmo2-Mistral-7B-adapter). 6 | - Arithmo2-Mistral-7B is trained on same data as Arithmo-Mistral-7B except that we removed both validation and test set of [lila ood subset](https://huggingface.co/datasets/allenai/lila/viewer/ood) to avoid possibility of data leakage. 7 | - Added [NEFTune](https://arxiv.org/pdf/2310.05914.pdf) 8 | - Enabled sample packing = true for faster training. 9 | 10 | 11 | # Arithmo Models 12 | [![Code License](https://img.shields.io/badge/Code%20License-Apache_2.0-green.svg)](LICENSE) 13 | [![Model Weight License](https://img.shields.io/badge/Model%20Weights%20License-Apache_2.0-green.svg)](LICENSE) 14 | [![Python 3.9+](https://img.shields.io/badge/python-3.9+-blue.svg)](https://www.python.org/downloads/release/python-390/) 15 | 16 | 17 | Both [Arithmo2-Mistral-7B](https://huggingface.co/upaya07/Arithmo2-Mistral-7B) and [Arithmo-Mistral-7B](https://huggingface.co/akjindal53244/Arithmo-Mistral-7B) models are trained to reason and answer mathematical problems and is also capable of writing a Python program that upon execution prints answer to the question. We used [Mistral-7B](https://huggingface.co/mistralai/Mistral-7B-v0.1) as a base model and used **QLoRA to fine-tune it on a single RTX 4090 GPU**. 18 | 19 | 20 | ## Benchmark Results 21 | 22 | Arithmo2-Mistral-7B model is fine-tuned with 4-bit QLoRA on single GPU and is competitive with supervised full-finetuned state-of-the-art Mathematical Reasoning models. Refer to [Comparing Arithmo models with other SFT LLM models](https://github.com/akjindal53244/Arithmo/tree/master?tab=readme-ov-file#comparing-arithmo-models-with-other-sft-llm-models) section for more details. 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 |
Model NameCheckpointTraining ApproachPrompt ApproachGSM8kMATHLicense
Arithmo-Mistral-7B🤗 Model4-bit QLoRA Fine-tuning on 1x4090Zero-Shot CoT74.725.3Apache-2.0
Zero-Shot PoT71.2-
🔥 Arithmo2-Mistral-7B 🤗 Model
🤗 LoRA Adapter 		4-bit QLoRA Fine-tuning on 1x4090Zero-Shot CoT76.427.2Apache-2.0
Zero-Shot PoT74.2-
67 | 68 | - **Zero-Shot CoT**: On providing a question as prompt, model generates reasoning steps to solve the question along with answer. We check if answer matches with ground-truth. 69 | - **Zero-Shot PoT**: We prompt the model to generate a Python program for the given question. During inference, we execute the Python program generated by the model and check if the program output matches with ground-truth answer. Visit [Model Card](https://huggingface.co/akjindal53244/Arithmo-Mistral-7B) to see few PoT examples. 70 | 71 | 72 | ## Installation 73 | 74 | ``` 75 | pip install transformers >=4.34.0 76 | pip install accelerate 77 | pip install sentencepiece 78 | pip install protobuf 79 | 80 | # If you are GPU poor like me 81 | pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu 82 | 83 | # If you have a GPU. 84 | pip install --pre torch --index-url https://download.pytorch.org/whl/nightly/cu118 85 | pip install scipy 86 | pip install bitsandbytes 87 | ``` 88 | 89 | 90 | ## How to query the model 91 | 92 | ``` 93 | # Set `run_model_on_gpu` to `False` if you are running on CPU. Model will generate reasoning steps with answer for your question. If you want to generate Python program, uncomment line-69 that adds a Python prompt. 94 | # This script automatically does formatting for you, so you just need to type question (eg: `What is 2+2?`) without any prefix like `Question:`, etc.** 95 | 96 | $ python query_model.py 97 | ``` 98 | **Note:** Above script automatically does formatting for you, so you just need to type question (eg: `What is 2+2?`) without any prefix like `Question:`, etc. Checkout `query_model.py` for more details.

99 | 100 | ##### Sample Input: 101 | ``` 102 | Question: There are total 10 children. I have to give 1 apple to first child, 2 apples to second child, 3 apples to third child, and so on. How many apples do I need? 103 | ``` 104 | ##### Model Output: 105 | ``` 106 | Answer: The total number of apples needed is the sum of the first 10 positive integers. 107 | This can be calculated using the formula for the sum of an arithmetic series: 108 | \[S = \frac{n}{2}(a_1 + a_n),\] 109 | where $S$ is the sum, $n$ is the number of terms, $a_1$ is the first term, and $a_n$ is the last term. 110 | In this case, $n = 10$, $a_1 = 1$, and $a_n = 10$. 111 | Plugging these values into the formula, we get: 112 | \[S = \frac{10}{2}(1 + 10) = 5(11) = \boxed{55}.\] 113 | The answer is: 55 114 | ``` 115 | 116 | Arithmo-Mistral-7B is trained with the following format: 117 | #### CoT Format (generate reasoning steps with answer): 118 | ``` 119 | Question: 120 | 121 | Answer: 122 | ``` 123 | 124 | #### PoT Format (generate a python program): 125 | ``` 126 | Question: 127 | 128 | Answer: 129 | ``` 130 | It will perform best if queried in this way with your own script. 131 | 132 | 133 | ## Model Finetuning Details 134 | Due to limited compute budget, Mistral-7B model is fine-tuned with QLoRA using Single RTX 4090 GPU. We plan to do a full finetuning of Mistral-7B model on this dataset to further improve performance.
135 |
136 | 137 | ## Reproducing Results 138 | 139 | ### Model Training Data 140 | Model training data is prepared by combining [MetaMathQA](https://huggingface.co/datasets/meta-math/MetaMathQA) (train split), [lila OOD](https://huggingface.co/datasets/allenai/lila/viewer/ood) (train, validation, and test splits), and [MathInstruct](https://huggingface.co/datasets/TIGER-Lab/MathInstruct) (train split) datasets. We have verified that our training data has no overlap with GSM8K and MATH test set. Further post-processing steps are applied such as 1) deduplication, 2) randomly lower-casing x% inputs, 3) adding diverse set of Python prompts for PoT, and 4) standardizing answer format. Final dataset is of size ~540,000. Also, to train Arithmo2-Mistral-7B model, we removed both validation and test set of [lila ood subset](https://huggingface.co/datasets/allenai/lila/viewer/ood) to avoid possibility of data leakage. 141 | 142 | ``` 143 | # This script generates train and eval sets. 144 | $ python data_prep/prepare_model_traininig_data.py 145 | ``` 146 | 147 | Here is [Huggingface link](https://huggingface.co/datasets/akjindal53244/Arithmo-Data) for our dataset. 148 | 149 | ### Answer/Response Generation 150 | 151 | #### Prediction on [GSM8K Test set](https://huggingface.co/datasets/gsm8k/viewer/main/test) 152 | ##### Zero-Shot with CoT: 153 | ``` 154 | # This script saves output to `data/predictions/gsm8k/Arithmo-Mistral-7B/predictions_Arithmo_gsm8k_zero_shot_CoT.json` path. 155 | $ python eval/gsm8k/gsm8k_generate_response_zero_shot_CoT.py 156 | ``` 157 | 158 | ##### Zero-Shot with PoT: 159 | ``` 160 | # This script saves output to `data/predictions/gsm8k/Arithmo-Mistral-7B/predictions_Arithmo_gsm8k_zero_shot_PoT.json` path. 161 | $ python eval/gsm8k/gsm8k_generate_response_zero_shot_PoT.py 162 | ``` 163 | 164 | #### Prediction on [MATH Test set](https://huggingface.co/datasets/competition_math/viewer/default/test) 165 | ##### Zero-Shot with CoT: 166 | ``` 167 | # This script saves output to `data/predictions/gsm8k/Arithmo-Mistral-7B/predictions_Arithmo_MATH_zero_shot_CoT.json` path. 168 | $ python eval/MATH/MATH_generate_response_zero_shot_CoT.py 169 | ``` 170 | 171 | **Zero-Shot with PoT**: Answers in MATH test set consist of expressions like `(x+2)/5` instead of a numeric value. Currently, Arithmo-Mistral-7B's PoT training data doesn't contain expressions as answers. Hence, we don't run PoT based inference on MATH dataset. 172 | 173 | 174 | ### Metrics Computation 175 | 176 | #### [GSM8K Test set](https://huggingface.co/datasets/gsm8k/viewer/main/test) 177 | ##### Zero-Shot with CoT: 178 | ``` 179 | $ python eval/gsm8k/gsm8k_compute_metric_zero_shot_CoT.py 180 | ``` 181 | Expected output: `Total Instances: 1319, Correct Count: 985, Accuracy (Correct Count/Total Instances): 0.7467`

182 | ##### Zero-Shot with PoT: 183 | ``` 184 | # Step-1: This script executes generated python programs and saves results into a file. 185 | $ python eval/gsm8k/gsm8k_write_zero_shot_PoT_outputs.py > data/predictions/gsm8k/Arithmo-Mistral-7B/gsm8k_zero_shot_PoT_results.txt 186 | 187 | # Step-2: This script computes accuracy by taking above file as input. 188 | $ python eval/gsm8k/gsm8k_compute_metric_zero_shot_PoT.py 189 | ``` 190 | Expected output: `Total Instances: 1309, Correct Count: 932, Accuracy: 0.7119` 191 | 192 | #### [MATH Test set](https://huggingface.co/datasets/competition_math/viewer/default/test) 193 | ##### Zero-Shot with CoT: 194 | ``` 195 | $ python eval/MATH/MATH_compute_metric_zero_shot_CoT.py 196 | ``` 197 | Script is borrowed from official [math repository](https://github.com/hendrycks/math/blob/main/modeling/math_equivalence.py)
198 | Expected output: `Total Instances: 5000, Correct Count: 1266, Accuracy (Correct Count/Total Instances): 0.2532` 199 | 200 | 201 | ## Comparing Arithmo models with other SFT LLM models 202 | Results for all models except `Arithmo2-Mistral-7B` and `Arithmo-Mistral-7B` are taken from [MetaMath](https://github.com/meta-math/MetaMath/blob/main/README.MD) repository. 203 | 204 | | Model | GSM8k Pass@1 | MATH Pass@1 | Model Training details | 205 | |---------------------|--------------|-------------|------------------------| 206 | | MPT-7B | 6.8 | 3.0 | 207 | | Falcon-7B | 6.8 | 2.3 | 208 | | LLaMA-1-7B | 11.0 | 2.9 | 209 | | LLaMA-2-7B | 14.6 | 2.5 | 210 | | MPT-30B | 15.2 | 3.1 | 211 | | LLaMA-1-13B | 17.8 | 3.9 | 212 | | GPT-Neo-2.7B | 19.5 | -- | 213 | | Falcon-40B | 19.6 | 2.5 | 214 | | Baichuan-chat-13B | 23.9 | -- | 215 | | Vicuna-v1.3-13B | 27.6 | -- | 216 | | LLaMA-2-13B | 28.7 | 3.9 | 217 | | InternLM-7B | 31.2 | -- | 218 | | ChatGLM-2-6B | 32.4 | -- | 219 | | GPT-J-6B | 34.9 | -- | 220 | | LLaMA-1-33B | 35.6 | 3.9 | 221 | | LLaMA-2-34B | 42.2 | 6.24 | 222 | | RFT-7B | 50.3 | -- | 223 | | LLaMA-1-65B | 50.9 | 10.6 | 224 | | Qwen-7B | 51.6 | -- | 225 | | WizardMath-7B | 54.9 | 10.7 | 226 | | LLaMA-2-70B | 56.8 | 13.5 | 227 | | WizardMath-13B | 63.9 | 14.0 | 228 | | MetaMath-7B | 66.5 | 19.8 | 229 | | MetaMath-13B | 72.3 | 22.4 | 230 | | Arithmo-Mistral-7B (PoT) | 71.2 | -- | SFT: 4-bit QLoRA | 231 | | Arithmo2-Mistral-7B (PoT) | 74.2 | -- | SFT: 4-bit QLoRA | 232 | | MetaMath-Mistral-7B | 77.7 | 28.2 | SFT: Full fine-tuned | 233 | | Arithmo-Mistral-7B| 74.7 | 25.3 | SFT: 4-bit QLoRA | 234 | | 🔥 **Arithmo2-Mistral-7B** | **76.4** | **27.2** | **SFT: 4-bit QLoRA** | 235 | 236 | 237 | ### Citation 238 | To cite Arithmo models: 239 | ``` 240 | @misc{jindal_2023_arithmo, 241 | author = {Jindal, Ashvini}, 242 | title = {Arithmo-Mistral-7B: Mathematical Reasoning Model}, 243 | howpublished = {Hugging Face}, 244 | month = {October}, 245 | year = {2023}, 246 | url = {https://huggingface.co/akjindal53244/Arithmo-Mistral-7B} 247 | } 248 | ``` 249 | 250 | ### Support My Work 251 | Building LLMs takes time and resources; if you find my work interesting, your support would be epic!
252 | 253 | Buy Me A Coffee 254 | 255 | P.S.: If you are interested in providing compute support, please reach out to [Ashvini Jindal](https://www.linkedin.com/in/ashvini-jindal-26653262/) 256 | 257 | 258 |

References

259 | 260 | ``` 261 | @article{yu2023metamath, 262 | title={MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models}, 263 | author={Yu, Longhui and Jiang, Weisen and Shi, Han and Yu, Jincheng and Liu, Zhengying and Zhang, Yu and Kwok, James T and Li, Zhenguo and Weller, Adrian and Liu, Weiyang}, 264 | journal={arXiv preprint arXiv:2309.12284}, 265 | year={2023} 266 | } 267 | 268 | @article{Yue2023mammoth, 269 | title={MAmmoTH: Building math generalist models through hybrid instruction tuning}, 270 | author={Xiang Yue, Xingwei Qu, Ge Zhang, Yao Fu, Wenhao Huang, Huan Sun, Yu Su, and Wenhu Chen}, 271 | journal={arXiv preprint arXiv:2309.05653}, 272 | year={2023} 273 | } 274 | 275 | @article{mishra2022lila, 276 | title={Lila: A unified benchmark for mathematical reasoning}, 277 | author={Swaroop Mishra, Matthew Finlayson, Pan Lu, Leonard Tang, Sean Welleck, Chitta Baral, Tanmay Rajpurohit, Oyvind Tafjord, Ashish Sabharwal, Peter Clark, and Ashwin Kalyan}, 278 | journal={arXiv preprint arXiv:2210.17517}, 279 | year={2022} 280 | } 281 | ``` 282 | 283 | ## Todos 284 | - 285 | -------------------------------------------------------------------------------- /data/predictions/gsm8k/Arithmo-Mistral-7B/gsm8k_zero_shot_PoT_results.txt: -------------------------------------------------------------------------------- 1 | 18 2 | 18 3 | ========= 4 | 3.0 5 | 3 6 | ========= 7 | 65000.0 8 | 70000 9 | ========= 10 | 540 11 | 540 12 | ========= 13 | 140 14 | 20 15 | ========= 16 | 40.0 17 | 64 18 | ========= 19 | 260 20 | 260 21 | ========= 22 | 120.0 23 | 160 24 | ========= 25 | 300.0 26 | 45 27 | ========= 28 | 460.0 29 | 460 30 | ========= 31 | 366.0 32 | 366 33 | ========= 34 | 694 35 | 694 36 | ========= 37 | 12 38 | 13 39 | ========= 40 | 1 41 | 18 42 | ========= 43 | 60.0 44 | 60 45 | ========= 46 | 125.0 47 | 125 48 | ========= 49 | 310 50 | 230 51 | ========= 52 | 57500 53 | 57500 54 | ========= 55 | 7.0 56 | 7 57 | ========= 58 | 4.0 59 | 6 60 | 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| 34 1062 | ========= 1063 | 21 1064 | 21 1065 | ========= 1066 | 3 1067 | 2 1068 | ========= 1069 | 20.0 1070 | 20 1071 | ========= 1072 | 5.333333333333333 1073 | 4 1074 | ========= 1075 | 25 1076 | 25 1077 | ========= 1078 | 0.05 1079 | 20 1080 | ========= 1081 | 23.0 1082 | 23 1083 | ========= 1084 | 12.053571428571429 1085 | 6 1086 | ========= 1087 | 49 1088 | 49 1089 | ========= 1090 | 18 1091 | 18 1092 | ========= 1093 | 9 1094 | 9 1095 | ========= 1096 | 19 1097 | 19 1098 | ========= 1099 | -10 1100 | 18 1101 | ========= 1102 | 1198.0 1103 | 1198 1104 | ========= 1105 | 320.0 1106 | 320 1107 | ========= 1108 | 50 1109 | 50 1110 | ========= 1111 | 5 1112 | 5 1113 | ========= 1114 | 240000.0 1115 | 240000 1116 | ========= 1117 | 60.0 1118 | 45 1119 | ========= 1120 | 48 1121 | 48 1122 | ========= 1123 | 15 1124 | 15 1125 | ========= 1126 | 50.0 1127 | 50 1128 | ========= 1129 | 15 1130 | 15 1131 | ========= 1132 | 21 1133 | 21 1134 | ========= 1135 | 73.0 1136 | 803 1137 | ========= 1138 | 67 1139 | 67 1140 | ========= 1141 | 350 1142 | 350 1143 | ========= 1144 | 4.5 1145 | 2 1146 | ========= 1147 | 32.0 1148 | 32 1149 | ========= 1150 | 16 1151 | 16 1152 | ========= 1153 | 80.0 1154 | 80 1155 | ========= 1156 | 36 1157 | 36 1158 | ========= 1159 | 88.0 1160 | 88 1161 | ========= 1162 | -12 1163 | 6 1164 | ========= 1165 | 12.0 1166 | 12 1167 | ========= 1168 | 15 1169 | 15 1170 | ========= 1171 | 34.0 1172 | 34 1173 | ========= 1174 | 27.27272727272727 1175 | 20 1176 | ========= 1177 | 56 1178 | 92 1179 | ========= 1180 | 38 1181 | 38 1182 | ========= 1183 | 3 1184 | 3 1185 | ========= 1186 | 25 1187 | 25 1188 | ========= 1189 | 168 1190 | 168 1191 | ========= 1192 | 12 1193 | 12 1194 | ========= 1195 | 48 1196 | 48 1197 | ========= 1198 | 14400 1199 | 14400 1200 | ========= 1201 | 4 1202 | 4 1203 | ========= 1204 | 135000 1205 | 81 1206 | ========= 1207 | 22.0 1208 | 22 1209 | ========= 1210 | 200.0 1211 | 50 1212 | ========= 1213 | 150.0 1214 | 200 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========= 1525 | 300.0 1526 | 300 1527 | ========= 1528 | 30000 1529 | 90000 1530 | ========= 1531 | 288 1532 | 160 1533 | ========= 1534 | 375 1535 | 375 1536 | ========= 1537 | 18 1538 | 18 1539 | ========= 1540 | 32.0 1541 | 32 1542 | ========= 1543 | 280 1544 | 280 1545 | ========= 1546 | 63 1547 | 63 1548 | ========= 1549 | 39 1550 | 39 1551 | ========= 1552 | 29 1553 | 29 1554 | ========= 1555 | 74 1556 | 74 1557 | ========= 1558 | 9 1559 | 9 1560 | ========= 1561 | 36 1562 | 12 1563 | ========= 1564 | -21.0 1565 | 21 1566 | ========= 1567 | 2.0 1568 | 48 1569 | ========= 1570 | 78 1571 | 172 1572 | ========= 1573 | 4 1574 | 11 1575 | ========= 1576 | 48 1577 | 36 1578 | ========= 1579 | 122 1580 | 66 1581 | ========= 1582 | 19 1583 | 25 1584 | ========= 1585 | 300.0 1586 | 300 1587 | ========= 1588 | 60.0 1589 | 300 1590 | ========= 1591 | 16 1592 | 16 1593 | ========= 1594 | 8 1595 | 8 1596 | ========= 1597 | 188 1598 | 188 1599 | ========= 1600 | 18 1601 | 18 1602 | ========= 1603 | 60.0 1604 | 35 1605 | ========= 1606 | 39.0 1607 | 39 1608 | ========= 1609 | 150.0 1610 | 50 1611 | ========= 1612 | 7.0 1613 | 7 1614 | ========= 1615 | 6.0 1616 | 6 1617 | ========= 1618 | 20 1619 | 80 1620 | ========= 1621 | 30 1622 | 30 1623 | ========= 1624 | 130.0 1625 | 130 1626 | ========= 1627 | 81 1628 | 81 1629 | ========= 1630 | 100 1631 | 100 1632 | ========= 1633 | 400 1634 | 398 1635 | ========= 1636 | 27.0 1637 | 27 1638 | ========= 1639 | 17.0 1640 | 17 1641 | ========= 1642 | 550.0 1643 | 450 1644 | ========= 1645 | 92 1646 | 92 1647 | ========= 1648 | 54 1649 | 54 1650 | ========= 1651 | 2.0 1652 | 2 1653 | ========= 1654 | 160 1655 | 160 1656 | ========= 1657 | 70 1658 | 70 1659 | ========= 1660 | 3.0 1661 | 3 1662 | ========= 1663 | 1.0 1664 | 16 1665 | ========= 1666 | 90 1667 | 45 1668 | ========= 1669 | 180.0 1670 | 180 1671 | ========= 1672 | 82 1673 | 82 1674 | ========= 1675 | 12 1676 | 12 1677 | ========= 1678 | 120.0 1679 | 240 1680 | ========= 1681 | 5 1682 | 5 1683 | ========= 1684 | 59.0 1685 | 10 1686 | ========= 1687 | 8.5 1688 | 9 1689 | ========= 1690 | 175 1691 | 175 1692 | ========= 1693 | 21 1694 | 21 1695 | ========= 1696 | 15 1697 | 23 1698 | ========= 1699 | 308 1700 | 308 1701 | ========= 1702 | 220 1703 | 100 1704 | ========= 1705 | 600 1706 | 600 1707 | ========= 1708 | 37.0 1709 | 37 1710 | ========= 1711 | 36.0 1712 | 36 1713 | ========= 1714 | 11232 1715 | 11232 1716 | ========= 1717 | 40.0 1718 | 40 1719 | ========= 1720 | 48.0 1721 | 48 1722 | ========= 1723 | 7 1724 | 7 1725 | ========= 1726 | 500.0 1727 | 500 1728 | ========= 1729 | 215 1730 | 215 1731 | ========= 1732 | 129200.0 1733 | 129200 1734 | ========= 1735 | 136 1736 | 120 1737 | ========= 1738 | 1.0 1739 | 2 1740 | ========= 1741 | 40 1742 | 40 1743 | ========= 1744 | 800.0 1745 | 800 1746 | ========= 1747 | 450 1748 | 30 1749 | ========= 1750 | 52.0 1751 | 52 1752 | ========= 1753 | 15.0 1754 | 15 1755 | ========= 1756 | 318 1757 | 319 1758 | ========= 1759 | 220.0 1760 | 220 1761 | ========= 1762 | 1 1763 | 1 1764 | ========= 1765 | 3 1766 | 3 1767 | ========= 1768 | 42.0 1769 | 42 1770 | ========= 1771 | 19 1772 | 13 1773 | ========= 1774 | 293.3333333333333 1775 | 260 1776 | ========= 1777 | 30.0 1778 | 90 1779 | ========= 1780 | 69 1781 | 69 1782 | ========= 1783 | 48 1784 | 48 1785 | ========= 1786 | 10.0 1787 | 10 1788 | ========= 1789 | 104 1790 | 104 1791 | ========= 1792 | 5.0 1793 | 5 1794 | ========= 1795 | 1260.0 1796 | 1800 1797 | ========= 1798 | 12 1799 | 12 1800 | ========= 1801 | 42.0 1802 | 42 1803 | ========= 1804 | 3.0 1805 | 6 1806 | ========= 1807 | 10.0 1808 | 10 1809 | ========= 1810 | 8.0 1811 | 8 1812 | ========= 1813 | 84.0 1814 | 7 1815 | ========= 1816 | 65960 1817 | 65960 1818 | ========= 1819 | 5500000 1820 | 1450000 1821 | ========= 1822 | 30 1823 | 30 1824 | ========= 1825 | 93000 1826 | 93000 1827 | ========= 1828 | 312.0 1829 | 312 1830 | ========= 1831 | 33.0 1832 | 33 1833 | ========= 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========= 1912 | 10800.0 1913 | 10800 1914 | ========= 1915 | 840 1916 | 840 1917 | ========= 1918 | 29.0 1919 | 29 1920 | ========= 1921 | 48 1922 | 48 1923 | ========= 1924 | 79.0 1925 | 79 1926 | ========= 1927 | 10.0 1928 | 10 1929 | ========= 1930 | 156 1931 | 54 1932 | ========= 1933 | 162000 1934 | 162000 1935 | ========= 1936 | 140.0 1937 | 142 1938 | ========= 1939 | 2100.0 1940 | 2100 1941 | ========= 1942 | 75 1943 | 75 1944 | ========= 1945 | 80 1946 | 80 1947 | ========= 1948 | 2.0 1949 | 2 1950 | ========= 1951 | 10 1952 | 10 1953 | ========= 1954 | -10.0 1955 | 10 1956 | ========= 1957 | 330000 1958 | 330000 1959 | ========= 1960 | 120.0 1961 | 120 1962 | ========= 1963 | 40.0 1964 | 3 1965 | ========= 1966 | 7 1967 | 15 1968 | ========= 1969 | 44.0 1970 | 44 1971 | ========= 1972 | 7.0 1973 | 7 1974 | ========= 1975 | 193 1976 | 193 1977 | ========= 1978 | 32 1979 | 32 1980 | ========= 1981 | 360.0 1982 | 360 1983 | ========= 1984 | 120.0 1985 | 120 1986 | ========= 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68 2066 | 68 2067 | ========= 2068 | 2 2069 | 9 2070 | ========= 2071 | 5 2072 | 5 2073 | ========= 2074 | 145.0 2075 | 145 2076 | ========= 2077 | 27 2078 | 27 2079 | ========= 2080 | 720.0 2081 | 720 2082 | ========= 2083 | 8.0 2084 | 8 2085 | ========= 2086 | 135 2087 | 135 2088 | ========= 2089 | 130 2090 | 200 2091 | ========= 2092 | 2800 2093 | 2800 2094 | ========= 2095 | 42 2096 | 50 2097 | ========= 2098 | 50.0 2099 | 50 2100 | ========= 2101 | 120.0 2102 | 120 2103 | ========= 2104 | 1400.0 2105 | 9 2106 | ========= 2107 | 8.0 2108 | 8 2109 | ========= 2110 | 168 2111 | 168 2112 | ========= 2113 | 3000.0 2114 | 3000 2115 | ========= 2116 | 45.0 2117 | 45 2118 | ========= 2119 | 3.0 2120 | 6 2121 | ========= 2122 | 14 2123 | 14 2124 | ========= 2125 | 576 2126 | 576 2127 | ========= 2128 | 10.0 2129 | 10 2130 | ========= 2131 | 385000.0 2132 | 385000 2133 | ========= 2134 | 770 2135 | 770 2136 | ========= 2137 | 5.0 2138 | 5 2139 | ========= 2140 | 2 2141 | 2 2142 | ========= 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