├── .gitignore ├── LICENSE ├── README.md ├── demo ├── Airavata_7b_openhathi.ipynb ├── README.md ├── gemma_gradio.ipynb ├── hf_infer.ipynb ├── navarasa_infer_2b.ipynb ├── navarasa_infer_7b_4bit.ipynb ├── unsloth_infer_2b.ipynb └── unsloth_infer_7b_4bit.ipynb ├── misc └── requirements.txt ├── results ├── gemma2b-en_hi-dev-flores101 ├── gemma2b-hi-ft-v0.01 └── gemma2b-it-en_hi-flores101 └── scripts ├── inference ├── infer.py ├── infer_batch.py ├── infer_gemma.py └── infer_on_file.py ├── metrics └── calculate_bleu.py └── train └── train_hindi_2b_v0.01.py /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | share/python-wheels/ 24 | *.egg-info/ 25 | .installed.cfg 26 | *.egg 27 | MANIFEST 28 | 29 | # PyInstaller 30 | # Usually these files are written by a python script from a template 31 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 32 | *.manifest 33 | *.spec 34 | 35 | # Installer logs 36 | pip-log.txt 37 | pip-delete-this-directory.txt 38 | 39 | # Unit test / coverage reports 40 | htmlcov/ 41 | .tox/ 42 | .nox/ 43 | .coverage 44 | .coverage.* 45 | .cache 46 | nosetests.xml 47 | coverage.xml 48 | *.cover 49 | *.py,cover 50 | .hypothesis/ 51 | .pytest_cache/ 52 | cover/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | .pybuilder/ 76 | target/ 77 | 78 | # Jupyter Notebook 79 | .ipynb_checkpoints 80 | 81 | # IPython 82 | profile_default/ 83 | ipython_config.py 84 | 85 | # pyenv 86 | # For a library or package, you might want to ignore these files since the code is 87 | # intended to run in multiple environments; otherwise, check them in: 88 | # .python-version 89 | 90 | # pipenv 91 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 92 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 93 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 94 | # install all needed dependencies. 95 | #Pipfile.lock 96 | 97 | # poetry 98 | # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. 99 | # This is especially recommended for binary packages to ensure reproducibility, and is more 100 | # commonly ignored for libraries. 101 | # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control 102 | #poetry.lock 103 | 104 | # pdm 105 | # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. 106 | #pdm.lock 107 | # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it 108 | # in version control. 109 | # https://pdm.fming.dev/#use-with-ide 110 | .pdm.toml 111 | 112 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm 113 | __pypackages__/ 114 | 115 | # Celery stuff 116 | celerybeat-schedule 117 | celerybeat.pid 118 | 119 | # SageMath parsed files 120 | *.sage.py 121 | 122 | # Environments 123 | .env 124 | .venv 125 | env/ 126 | venv/ 127 | ENV/ 128 | env.bak/ 129 | venv.bak/ 130 | 131 | # Spyder project settings 132 | .spyderproject 133 | .spyproject 134 | 135 | # Rope project settings 136 | .ropeproject 137 | 138 | # mkdocs documentation 139 | /site 140 | 141 | # mypy 142 | .mypy_cache/ 143 | .dmypy.json 144 | dmypy.json 145 | 146 | # Pyre type checker 147 | .pyre/ 148 | 149 | # pytype static type analyzer 150 | .pytype/ 151 | 152 | # Cython debug symbols 153 | cython_debug/ 154 | 155 | # PyCharm 156 | # JetBrains specific template is maintained in a separate JetBrains.gitignore that can 157 | # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore 158 | # and can be added to the global gitignore or merged into this file. For a more nuclear 159 | # option (not recommended) you can uncomment the following to ignore the entire idea folder. 160 | #.idea/ 161 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2024 Vakyansh 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # gemma-experimentation 2 | Experimentation on google's gemma model 3 | 4 | ## Gradio Inference Demos 5 | | Model | Link | 6 | | --- | ---- | 7 | | Telugu-LLM-Labs/Indic-gemma-2b-finetuned-sft-Navarasa | [Link](https://github.com/vakyansh/gemma-experimentation/blob/main/demo/unsloth_infer_2b.ipynb) | 8 | | Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa | [Link](https://github.com/vakyansh/gemma-experimentation/blob/main/demo/unsloth_infer_7b_4bit.ipynb) | 9 | 10 | 11 | ## Datasets used for hi-gemma2b-ft-lora-v0.01 12 | BhabhaAI/indic-instruct-data-v0.1-filtered 13 | 14 | yahma/alpaca-cleaned 15 | 16 | ## Benchmarking 17 | 18 | ### Translation 19 | 20 | | Model | 0-shot Eng-Hi-dev BLEU | 21 | |------------------|----------------| 22 | | gemma2b | 2.84 | 23 | | gemma2b-it | 5.21 | 24 | | Telugu-LLM-Labs/Indic-gemma-2b-finetuned-sft-Navarasa | 6.22 | 25 | | hi-gemma2b-ft-lora-v0.01| 19.59 | 26 | | ai4bharat/Airavata | 30.69 | 27 | 28 | 29 | 30 | 31 | ## Learnings: 32 | 06/03/2024: Added inferene client using gradio. Nice looking UI 33 | 34 | 02/03/2024: Use packing=True as suggested by Ravi. Avoids nan and inf gradients during training. Also decreases training time. 35 | 36 | Will update more learnings as I keep one experimenting. 37 | -------------------------------------------------------------------------------- /demo/README.md: -------------------------------------------------------------------------------- 1 | ## How to use this demo? 2 | 3 | 1. Make sure you have atleast 16 GB GPU. (CPU also runs fine but it is very slow) 4 | 2. Run the cells, set HF_token and model_id 5 | 3. Set the system prompt. In this example I set the system prompt in alpaca_format 6 | 4. alpaca_format needs instruction and input. **Note : if you want to send instruction and input both, make sure they are separated with instruction ### input. The code will still work if you have not specified the ###** 7 | 5. If you are facing any issues in environment setup see the requirements.txt file in misc folder. 8 | 6. Also check the default parameters setup for inference like : 9 | ``` 10 | max_new_tokens=2048, 11 | top_p=0.2, 12 | top_k=20, 13 | temperature=0.1, 14 | repetition_penalty=2.0, 15 | length_penalty=-0.5, 16 | num_beams=1 17 | ``` 18 | -------------------------------------------------------------------------------- /demo/gemma_gradio.ipynb: -------------------------------------------------------------------------------- 1 | {"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"mount_file_id":"14uURzlJWFVSlkqluFbL7-Bnp8J-jul7L","authorship_tag":"ABX9TyNPLcHPuJmQ9dMKOEUSde42"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"wTrLKQlYMKqI"},"outputs":[],"source":["import accelerate\n","import gradio as gr\n","import torch\n","import os\n","from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer\n","from threading import Thread\n","from unsloth import FastLanguageModel\n","\n","\n","# Set the number of threads for Torch\n","torch.set_num_threads(1)\n","\n","# Get Hugging Face token from environment variable\n","HF_TOKEN = '< your hf token >'\n","\n","# Set device to CUDA if available, otherwise to CPU\n","device = 'cuda' if torch.cuda.is_available() else 'cpu'\n","\n","# Load tokenizer and model from Hugging Face's model hub\n","MODEL_NAME = \"< your model name >\"\n","tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, use_auth_token=HF_TOKEN)\n","model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, use_auth_token=HF_TOKEN).to(device)\n","FastLanguageModel.for_inference(model)\n","\n","\n","# Function to count tokens\n","def count_tokens(text):\n"," return len(tokenizer.tokenize(text))\n"]},{"cell_type":"code","source":["# Function to generate model predictions\n","def predict(message, history):\n"," alpaca_format = \"\"\"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n"," ### Instruction:\n"," {}\n","\n"," ### Input:\n"," {}\n","\n"," ### Response:\n"," {}\"\"\"\n","\n"," # if no input is presetn make it work\n"," if '###' not in message:\n"," message = message + ' ### '\n","\n"," # Split message into instruction and input\n"," messages = message.split('###')\n"," model_inputs = tokenizer(alpaca_format.format(messages[0], messages[1], \"\"), return_tensors=\"pt\").to(device)\n","\n"," # Initialize TextIteratorStreamer\n"," streamer = TextIteratorStreamer(tokenizer, timeout=120., skip_prompt=True, skip_special_tokens=True)\n","\n"," # Generate model kwargs\n"," generate_kwargs = dict(\n"," model_inputs,\n"," streamer=streamer,\n"," max_new_tokens=2048 - count_tokens(alpaca_format),\n"," top_p=0.2,\n"," top_k=20,\n"," temperature=0.1,\n"," repetition_penalty=2.0,\n"," length_penalty=-0.5,\n"," num_beams=1\n"," )\n","\n"," # Start generation in a separate thread\n"," t = Thread(target=model.generate, kwargs=generate_kwargs)\n"," t.start()\n","\n"," # Yield partial message\n"," partial_message = \"\"\n"," for new_token in streamer:\n"," partial_message += new_token\n"," yield partial_message\n","\n","\n","# Setting up the Gradio chat interface\n","gr.ChatInterface(predict,\n"," title=\"Gemma 2b Instruct Chat\",\n"," description=None\n"," ).launch(share=True) # Launching the web interface.\n"],"metadata":{"id":"RSg2Y5UCMQev"},"execution_count":null,"outputs":[]}]} -------------------------------------------------------------------------------- /demo/hf_infer.ipynb: -------------------------------------------------------------------------------- 1 | {"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"collapsed_sections":["OD5ITGgLZb09"],"gpuType":"T4","authorship_tag":"ABX9TyMfTpFL0J6kEknyPwmqX9fx"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"markdown","source":["## Run Gemma-2b on Google Colab GPU"],"metadata":{"id":"FXXG6pV6ZUCe"}},{"cell_type":"markdown","source":["### Imports"],"metadata":{"id":"OD5ITGgLZb09"}},{"cell_type":"code","execution_count":1,"metadata":{"id":"anhS1vZMT18b","executionInfo":{"status":"ok","timestamp":1709705674629,"user_tz":-330,"elapsed":76901,"user":{"displayName":"Vakyansh","userId":"14121683683494953777"}}},"outputs":[],"source":["%%capture\n","import torch\n","major_version, minor_version = torch.cuda.get_device_capability()\n","if major_version >= 8:\n"," # Use this for new GPUs like Ampere, Hopper GPUs (RTX 30xx, RTX 40xx, A100, H100, L40)\n"," !pip install \"unsloth[colab-ampere] @ git+https://github.com/unslothai/unsloth.git\"\n","else:\n"," # Use this for older GPUs (V100, Tesla T4, RTX 20xx)\n"," !pip install \"unsloth[colab] @ git+https://github.com/unslothai/unsloth.git\"\n","pass\n","\n","!pip install -q gradio"]},{"cell_type":"code","source":["## Some Imports\n","\n","import accelerate\n","import gradio as gr\n","import torch, os\n","from transformers import AutoModelForCausalLM, AutoTokenizer\n","from transformers import StoppingCriteria, TextIteratorStreamer\n","from peft import AutoPeftModelForCausalLM\n","from transformers import AutoTokenizer\n","from threading import Thread"],"metadata":{"id":"mfYDmD9wZJrO","executionInfo":{"status":"ok","timestamp":1709705764470,"user_tz":-330,"elapsed":7889,"user":{"displayName":"Vakyansh","userId":"14121683683494953777"}}},"execution_count":4,"outputs":[]},{"cell_type":"markdown","source":["## Set the model you want to use"],"metadata":{"id":"eMoeDq0XZjdc"}},{"cell_type":"code","source":["# Load tokenizer and model from Hugging Face's model hub\n","MODEL_NAME = \"Telugu-LLM-Labs/Indic-gemma-2b-finetuned-sft-Navarasa\"\n","hf_token = \"\""],"metadata":{"id":"GTSkSsi8aEqn","executionInfo":{"status":"ok","timestamp":1709705989462,"user_tz":-330,"elapsed":700,"user":{"displayName":"Vakyansh","userId":"14121683683494953777"}}},"execution_count":10,"outputs":[]},{"cell_type":"code","source":["# Set the number of threads for Torch\n","torch.set_num_threads(2)\n","\n","\n","# Set device to CUDA if available, otherwise to CPU\n","device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n","\n","\n","model = AutoPeftModelForCausalLM.from_pretrained(MODEL_NAME, load_in_4bit = False, token=hf_token).to(device)\n","tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)\n","\n","\n","# Function to count tokens\n","def count_tokens(text):\n"," return len(tokenizer.tokenize(text))"],"metadata":{"id":"1AmbB_37Ze-v"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Run Gradio Interface"],"metadata":{"id":"GJIJP1u4bJMG"}},{"cell_type":"code","source":["# Function to generate model predictions\n","def predict(message, history):\n"," alpaca_format = \"\"\"\n"," ### Instruction:\n"," {}\n","\n"," ### Input:\n"," {}\n","\n"," ### Response:\n"," {}\"\"\"\n","\n"," # if no input is presetn make it work\n"," if '###' not in message:\n"," message = message + ' ### '\n","\n"," # Split message into instruction and input\n"," messages = message.split('###')\n"," model_inputs = tokenizer(alpaca_format.format(messages[0], messages[1], \"\"), return_tensors=\"pt\").to(device)\n","\n"," # Initialize TextIteratorStreamer\n"," streamer = TextIteratorStreamer(tokenizer, timeout=120., skip_prompt=True, skip_special_tokens=True)\n","\n"," # Generate model kwargs\n"," generate_kwargs = dict(\n"," model_inputs,\n"," streamer=streamer,\n"," max_new_tokens=2048 - count_tokens(alpaca_format),\n"," # top_p=0.2,\n"," # top_k=20,\n"," # temperature=0.1,\n"," repetition_penalty=2.0,\n"," # length_penalty=-0.5,\n"," # num_beams=1\n","\n"," )\n","\n"," # Start generation in a separate thread\n"," t = Thread(target=model.generate, kwargs=generate_kwargs)\n"," t.start()\n","\n"," # Yield partial message\n"," partial_message = \"\"\n"," for new_token in streamer:\n"," partial_message += new_token\n"," yield partial_message\n","\n","\n","# Setting up the Gradio chat interface\n","gr.ChatInterface(predict,\n"," title=\"Navarasa 2b chat demo\",\n"," description=None\n"," ).launch(share=True) # Launching the web interface.\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":590},"id":"f4J9vYjRaIjw","executionInfo":{"status":"ok","timestamp":1709706734297,"user_tz":-330,"elapsed":5573,"user":{"displayName":"Vakyansh","userId":"14121683683494953777"}},"outputId":"3b547085-0374-4217-ea6d-bb64fb884961"},"execution_count":14,"outputs":[{"output_type":"stream","name":"stdout","text":["Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n","Running on public URL: https://ec81cea79e17ca3a13.gradio.live\n","\n","This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["
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Run Gemma-7b 4bit on Google Colab GPU"],"metadata":{"id":"FXXG6pV6ZUCe"}},{"cell_type":"markdown","source":["### Imports"],"metadata":{"id":"OD5ITGgLZb09"}},{"cell_type":"code","execution_count":1,"metadata":{"id":"anhS1vZMT18b","executionInfo":{"status":"ok","timestamp":1709742992192,"user_tz":-330,"elapsed":75718,"user":{"displayName":"Vakyansh","userId":"14121683683494953777"}}},"outputs":[],"source":["%%capture\n","import torch\n","major_version, minor_version = torch.cuda.get_device_capability()\n","if major_version >= 8:\n"," # Use this for new GPUs like Ampere, Hopper GPUs (RTX 30xx, RTX 40xx, A100, H100, L40)\n"," !pip install \"unsloth[colab-ampere] @ git+https://github.com/unslothai/unsloth.git\"\n","else:\n"," # Use this for older GPUs (V100, Tesla T4, RTX 20xx)\n"," !pip install \"unsloth[colab] @ git+https://github.com/unslothai/unsloth.git\"\n","pass\n","\n","!pip install -q gradio"]},{"cell_type":"code","source":["## Some Imports\n","\n","import accelerate\n","import gradio as gr\n","import torch, os\n","from transformers import StoppingCriteria, TextIteratorStreamer\n","from transformers import AutoTokenizer\n","from threading import Thread\n","from unsloth import FastLanguageModel"],"metadata":{"id":"mfYDmD9wZJrO","executionInfo":{"status":"ok","timestamp":1709743003411,"user_tz":-330,"elapsed":11230,"user":{"displayName":"Vakyansh","userId":"14121683683494953777"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"42b15c25-8e9a-4fbc-92de-644b7e1320c5"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/unsloth/__init__.py:71: UserWarning: Unsloth: Running `ldconfig /usr/lib64-nvidia` to link CUDA.\n"," warnings.warn(\n"]}]},{"cell_type":"markdown","source":["## Set the model you want to use"],"metadata":{"id":"eMoeDq0XZjdc"}},{"cell_type":"code","source":["# Load tokenizer and model from Hugging Face's model hub\n","MODEL_NAME = 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Run Gemma-7b 4bit on Google Colab GPU"],"metadata":{"id":"FXXG6pV6ZUCe"}},{"cell_type":"markdown","source":["### Imports"],"metadata":{"id":"OD5ITGgLZb09"}},{"cell_type":"code","execution_count":1,"metadata":{"id":"anhS1vZMT18b","executionInfo":{"status":"ok","timestamp":1709716424386,"user_tz":-330,"elapsed":137960,"user":{"displayName":"Vakyansh","userId":"14121683683494953777"}}},"outputs":[],"source":["%%capture\n","import torch\n","major_version, minor_version = torch.cuda.get_device_capability()\n","if major_version >= 8:\n"," # Use this for new GPUs like Ampere, Hopper GPUs (RTX 30xx, RTX 40xx, A100, H100, L40)\n"," !pip install \"unsloth[colab-ampere] @ git+https://github.com/unslothai/unsloth.git\"\n","else:\n"," # Use this for older GPUs (V100, Tesla T4, RTX 20xx)\n"," !pip install \"unsloth[colab] @ git+https://github.com/unslothai/unsloth.git\"\n","pass\n","\n","!pip install -q gradio"]},{"cell_type":"code","source":["## Some Imports\n","\n","import accelerate\n","import gradio as gr\n","import torch, os\n","from transformers import StoppingCriteria, TextIteratorStreamer\n","from transformers import AutoTokenizer\n","from threading import Thread\n","from unsloth import FastLanguageModel"],"metadata":{"id":"mfYDmD9wZJrO","executionInfo":{"status":"ok","timestamp":1709716435715,"user_tz":-330,"elapsed":11339,"user":{"displayName":"Vakyansh","userId":"14121683683494953777"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"361ebba7-e5fc-4c24-9668-8c161c7862bb"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/unsloth/__init__.py:71: UserWarning: Unsloth: Running `ldconfig /usr/lib64-nvidia` to link CUDA.\n"," warnings.warn(\n"]}]},{"cell_type":"markdown","source":["## Set the model you want to use"],"metadata":{"id":"eMoeDq0XZjdc"}},{"cell_type":"code","source":["# Load tokenizer and model from Hugging Face's model hub\n","MODEL_NAME = \"Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa\"\n","hf_token = \"\""],"metadata":{"id":"GTSkSsi8aEqn","executionInfo":{"status":"ok","timestamp":1709716435716,"user_tz":-330,"elapsed":52,"user":{"displayName":"Vakyansh","userId":"14121683683494953777"}}},"execution_count":3,"outputs":[]},{"cell_type":"code","source":["# Set the number of threads for Torch\n","torch.set_num_threads(2)\n","device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n","\n","max_seq_length = 2048\n","dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+\n","load_in_4bit = True\n","\n","model, tokenizer = FastLanguageModel.from_pretrained(\n"," model_name = MODEL_NAME,\n"," max_seq_length = max_seq_length,\n"," dtype = dtype,\n"," load_in_4bit = load_in_4bit,\n"," device_map=device,\n"," token=hf_token\n",")\n","FastLanguageModel.for_inference(model)\n","\n","# Function to count tokens\n","def count_tokens(text):\n"," return len(tokenizer.tokenize(text))"],"metadata":{"id":"1AmbB_37Ze-v","colab":{"base_uri":"https://localhost:8080/","height":499,"referenced_widgets":["f7b22f6515b34b14841c86cf37beb00f","13025ba3e4c847fe87a3dfc0f5c8b61d","25865025d4ea46d98c167618e7a83876","42cd28071be64358b2fc3d59b0b1a5b5","eee178a80f42402fb02c42a01c036135","fa71868e94d042acacf268461170f2b8","37fd0eadaab14ac6b0bf4e16d6a1d019","79b45aad5ead4d9d8436f5867d86a2a6","d968494ac09140808eaa5d7ea472a17f","0d68b5241db446d9958771d94901068f","2746ef83373d4ec39b9e4ac94ddcabab","d1f9112f2f9c4af1a92fa586cc1e6a39","6a731316b30f4742ab6a726de54341d5","57f75a36fdf64073b2d5c6e55dff4f95","dde0b4689da047e99c6d14487249b386","532ef1f72f3c4c45ad0efa3067b35273","6cf6686ecacc434cbb31db9667edd7af","0b108a604bae4e14b26011fc3e7fbc9c","a095822e8d4944dbb1a25e9c62f0ea0c","b51b30e56b574dfe82b55a620473894f","709bc603b0274afd81f1bf0425f8a507","9a1eb6870664426593585187a086134f","f238e4e91d5a4280b9231871014385b1","2fbc6b2d19354fab843ef1f9881ea795","6f71a882f5f047808704ccb83919863c","54b0de160b374e818d516c6d3922f746","2b15fcc1754640a48ddfdce2b61fbb6c","a004c4e4874844b6877ff352cf237cf9","3c08f5357c8141d9972810585322aa72","ef4bc6c4ba514e13b804106ead446d72","e29829a0f8eb48ec990a7c16c8648e0f","3b975121af1249dc82d1577add233d50","f892c5f692004341872f1db48de4b2ba","37f0189535d2403eb4b14b7bdc444650","f4aa8232a3a94e679a23a28f04a8ea8e","7c9b8cc0df6c4b5983284b5c0592d9ed","816bf5fc063249bb9c089481c3e08238","0fe1108e5d8a4aed8ec71a2e69b80cf7","9c57d4809f76477f800d5e28394401fe","d301cd7437234953853cbf7b273c2b1a","9994323463a24e22ad7cbf955b48dd24","a675e0e8f486458db3182d623e2323ae","ee91e416fa9d40d9a1ad4b19f4f956f9","a458e4c7804e474db3856e9f3c183e44","e7616a7e2b7043919120de531bce81f2","1ed44adeccfb49e7a6f3b4ae75edcb99","5b5cd7a4756d469fb1a86ef43397c225","13a7cb41787c452bb2a6da50f8680bcf","f1dbe231bebe4f34a4daa9660b354312","dc73b2c3024348bbafdb382299926c48","a77198ff07a8483e87f051c8765aff1e","41a6bb507a3d41bb9285fae168b81c21","964d732fdfd64e9db685528aebd64c91","449105c343e74846970e71937411eb21","638c111d0409498eb7791b61ee213968","e1e5b09d725e4480a8c43d17aa3243bc","2f3803c0d2204ce8a59c376afbf64f35","75ffefe981184120a154c4f87e550fc4","f5215a010f61447f9e57140a38a97d57","6949d2011c2a4fdd9e5047e83bec6ebe","d952678bdd2943ae89dbed39dbb325b4","8d3034be82c445b6afe7b46a1d094b29","28a3d3bead34429d995a8f1b14d82bd2","e97996c0a7de42b2a9a1e99d490c714d","9f2af5f0583a4cef905611f097118568","80345a960eb94f90825fda811b31f33e","2110e704e8b0432ebd49b9b4063d7adc","a858c228826640b1ac5c02b267f767cf","5b3962b272544cecae29008a652d3da5","56cdb10817ce4f5e8b93a383eac6be11","898002cbd40741cf8cdd32a4e36c0bf2","9c999e6d1e0f4f8aa10d3c64533d8d8c","7b7dc74e738846638d9cbad3d59caad2","256c5c31a3aa4161b2f76565808300ab","8294d5db584342679730cdd00137e383","9207402c1cc44934b6934b62721903fd","7ab736075ef14269b341d3ac04ca3e1f","acbd4383cd8144079da3ba1540c61000","f438f10a9a4a4e1894f486cd36c313d6","79bde90b7d3a46e3b631d16bf2164ad8","43d73d095c184e5ca88a854016943110","f7120ec22281451b80a06f3d2c409993","d558453129564e4a9b0606b805571438","1897032276f34a2cace245ade03ba299","e42ad27795f9426e9abe97f0b6ffa3f5","3671176511ff4c229e1765f713acef85","e63fd54de08c4f579e9542776b69ee55","e6cfa3e2c6ad4cc99af40055734fbe2a","8efa9066213446e1bf90a00529471b5a","38b8cea8fa5a461aabdd3c245c9a7a0b","fbccfac300ac4d6686b631c7e9b081ff","29ec41d8c0b241c8bf847479863a1b1b","b6a030e2efde498682182d5c445dbbe5","6325a2c870984343bd20c238dcdb463b","82932235a3b94eb48f1a4d1a82fd422c","7c41d89288184c41910bdf2ce95f368b","31bc3e01daa44558a5783a73e0e00e73","bed47d62f6ea4f67907bbdc47fc170ba","eede7395dcde4ac6b2e2b4a1e41823ef"]},"executionInfo":{"status":"ok","timestamp":1709716527444,"user_tz":-330,"elapsed":91779,"user":{"displayName":"Vakyansh","userId":"14121683683494953777"}},"outputId":"fc6b0142-4920-49e3-ec5e-3f2f6ed8147f"},"execution_count":4,"outputs":[{"output_type":"display_data","data":{"text/plain":["adapter_config.json: 0%| | 0.00/692 [00:00"],"text/html":["
"]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":[]},"metadata":{},"execution_count":6}]},{"cell_type":"code","source":[],"metadata":{"id":"UEKFrSl4bM9H","executionInfo":{"status":"ok","timestamp":1709716536264,"user_tz":-330,"elapsed":17,"user":{"displayName":"Vakyansh","userId":"14121683683494953777"}}},"execution_count":5,"outputs":[]}]} -------------------------------------------------------------------------------- /misc/requirements.txt: -------------------------------------------------------------------------------- 1 | absl-py==2.1.0 2 | accelerate==0.27.2 3 | addict==2.4.0 4 | aiofiles==23.2.1 5 | aiohttp==3.9.3 6 | aiosignal==1.3.1 7 | alabaster==0.7.16 8 | altair==5.2.0 9 | aniso8601==9.0.1 10 | annotated-types==0.6.0 11 | antlr4-python3-runtime==4.9.3 12 | anyio==4.3.0 13 | appdirs==1.4.4 14 | argon2-cffi==23.1.0 15 | argon2-cffi-bindings==21.2.0 16 | arrow==1.3.0 17 | asciitree==0.3.3 18 | asttokens==2.4.1 19 | async-lru==2.0.4 20 | async-timeout==4.0.3 21 | attrdict==2.0.1 22 | attrs==23.2.0 23 | audioread==3.0.1 24 | Babel==2.14.0 25 | beautifulsoup4==4.12.3 26 | bitsandbytes==0.42.0 27 | black==19.10b0 28 | bleach==6.1.0 29 | boto3==1.34.55 30 | botocore==1.34.55 31 | braceexpand==0.1.7 32 | cdifflib==1.2.6 33 | certifi==2024.2.2 34 | cffi==1.16.0 35 | charset-normalizer==3.3.2 36 | click==8.0.2 37 | clip==0.2.0 38 | colorama==0.4.6 39 | comm==0.2.1 40 | contourpy==1.2.0 41 | cycler==0.12.1 42 | Cython==3.0.8 43 | cytoolz==0.12.3 44 | datasets==2.17.1 45 | debugpy==1.8.1 46 | decorator==5.1.1 47 | defusedxml==0.7.1 48 | diffusers==0.26.3 49 | dill==0.3.8 50 | Distance==0.1.3 51 | docker-pycreds==0.4.0 52 | docopt==0.6.2 53 | docstring-parser==0.15 54 | docutils==0.20.1 55 | editdistance==0.8.1 56 | einops==0.7.0 57 | einops-exts==0.0.4 58 | exceptiongroup==1.2.0 59 | executing==2.0.1 60 | faiss-cpu==1.8.0 61 | fastapi==0.110.0 62 | fasteners==0.19 63 | fastjsonschema==2.19.1 64 | fasttext==0.9.2 65 | ffmpeg==1.4 66 | ffmpy==0.3.2 67 | filelock==3.13.1 68 | Flask==2.2.5 69 | Flask-RESTful==0.3.10 70 | fonttools==4.49.0 71 | fqdn==1.5.1 72 | frozenlist==1.4.1 73 | fsspec==2023.10.0 74 | ftfy==6.1.3 75 | future==1.0.0 76 | g2p-en==2.1.0 77 | gdown==5.1.0 78 | gitdb==4.0.11 79 | GitPython==3.1.42 80 | gradio==4.19.2 81 | gradio_client==0.10.1 82 | grpcio==1.62.0 83 | h11==0.14.0 84 | h5py==3.10.0 85 | httpcore==1.0.4 86 | httpx==0.27.0 87 | huggingface-hub==0.21.3 88 | hydra-core==1.3.2 89 | idna==3.6 90 | ijson==3.2.3 91 | imageio==2.34.0 92 | imagesize==1.4.1 93 | importlib-metadata==7.0.1 94 | importlib_resources==6.1.2 95 | inflect==7.0.0 96 | iniconfig==2.0.0 97 | intervaltree==3.1.0 98 | ipykernel==6.29.3 99 | ipython==8.22.1 100 | ipywidgets==8.1.2 101 | isoduration==20.11.0 102 | isort==5.13.2 103 | itsdangerous==2.1.2 104 | jedi==0.19.1 105 | jieba==0.42.1 106 | Jinja2==3.1.3 107 | jiwer==2.5.2 108 | jmespath==1.0.1 109 | joblib==1.3.2 110 | json5==0.9.18 111 | jsonpointer==2.4 112 | jsonschema==4.21.1 113 | jsonschema-specifications==2023.12.1 114 | jupyter==1.0.0 115 | jupyter-console==6.6.3 116 | jupyter-events==0.9.0 117 | jupyter-lsp==2.2.3 118 | jupyter_client==8.6.0 119 | jupyter_core==5.7.1 120 | jupyter_server==2.12.5 121 | jupyter_server_terminals==0.5.2 122 | jupyterlab==4.1.2 123 | jupyterlab_pygments==0.3.0 124 | jupyterlab_server==2.25.3 125 | jupyterlab_widgets==3.0.10 126 | kaldi-python-io==1.2.2 127 | kaldiio==2.18.0 128 | kiwisolver==1.4.5 129 | kornia==0.7.1 130 | latexcodec==2.0.1 131 | lazy_loader==0.3 132 | Levenshtein==0.22.0 133 | lhotse==1.21.0 134 | librosa==0.10.1 135 | lightning-utilities==0.10.1 136 | lilcom==1.7 137 | llvmlite==0.42.0 138 | loguru==0.7.2 139 | lxml==5.1.0 140 | Markdown==3.5.2 141 | markdown-it-py==3.0.0 142 | markdown2==2.4.13 143 | MarkupSafe==2.1.5 144 | marshmallow==3.21.0 145 | matplotlib==3.8.3 146 | matplotlib-inline==0.1.6 147 | mdurl==0.1.2 148 | megatron-core==0.4.0 149 | mistune==3.0.2 150 | mpmath==1.3.0 151 | msgpack==1.0.8 152 | multidict==6.0.5 153 | multiprocess==0.70.16 154 | nbclient==0.9.0 155 | nbconvert==7.16.1 156 | nbformat==5.9.2 157 | nemo-text-processing==0.2.2rc0 158 | nemo_toolkit @ git+https://github.com/NVIDIA/NeMo.git@0bb9e66a6d29b28e8831d1d1dd8a30310173ce46 159 | nerfacc==0.5.3 160 | nest-asyncio==1.6.0 161 | networkx==3.2.1 162 | nltk==3.8.1 163 | notebook==7.1.1 164 | notebook_shim==0.2.4 165 | numba==0.59.0 166 | numcodecs==0.12.1 167 | numpy==1.26.4 168 | nvidia-cublas-cu12==12.1.3.1 169 | nvidia-cuda-cupti-cu12==12.1.105 170 | nvidia-cuda-nvrtc-cu12==12.1.105 171 | nvidia-cuda-runtime-cu12==12.1.105 172 | nvidia-cudnn-cu12==8.9.2.26 173 | nvidia-cufft-cu12==11.0.2.54 174 | nvidia-curand-cu12==10.3.2.106 175 | nvidia-cusolver-cu12==11.4.5.107 176 | nvidia-cusparse-cu12==12.1.0.106 177 | nvidia-nccl-cu12==2.19.3 178 | nvidia-nvjitlink-cu12==12.3.101 179 | nvidia-nvtx-cu12==12.1.105 180 | omegaconf==2.3.0 181 | onnx==1.15.0 182 | open-clip-torch==2.24.0 183 | OpenCC==1.1.6 184 | opencv-python==4.9.0.80 185 | orjson==3.9.15 186 | overrides==7.7.0 187 | packaging==23.2 188 | pandas==2.2.1 189 | pandocfilters==1.5.1 190 | pangu==4.0.6.1 191 | parameterized==0.9.0 192 | parso==0.8.3 193 | pathspec==0.12.1 194 | peft==0.9.0 195 | pexpect==4.9.0 196 | pillow==10.2.0 197 | plac==1.4.3 198 | platformdirs==4.2.0 199 | pluggy==1.4.0 200 | pooch==1.8.1 201 | portalocker==2.8.2 202 | progress==1.6 203 | prometheus_client==0.20.0 204 | prompt-toolkit==3.0.43 205 | protobuf==4.25.3 206 | psutil==5.9.8 207 | ptyprocess==0.7.0 208 | pure-eval==0.2.2 209 | pyannote.core==5.0.0 210 | pyannote.database==5.0.1 211 | pyannote.metrics==3.2.1 212 | pyarrow==15.0.0 213 | pyarrow-hotfix==0.6 214 | pybind11==2.11.1 215 | pybtex==0.24.0 216 | pybtex-docutils==1.0.3 217 | pycparser==2.21 218 | pydantic==2.6.3 219 | pydantic_core==2.16.3 220 | pydub==0.25.1 221 | Pygments==2.17.2 222 | pyloudnorm==0.1.1 223 | PyMCubes==0.1.4 224 | pynini==2.1.5 225 | pyparsing==3.1.1 226 | pypinyin==0.50.0 227 | pypinyin-dict==0.7.0 228 | PySocks==1.7.1 229 | pytest==8.0.2 230 | pytest-runner==6.0.1 231 | python-dateutil==2.9.0 232 | python-json-logger==2.0.7 233 | python-multipart==0.0.9 234 | pytorch-lightning==2.0.7 235 | pytz==2024.1 236 | PyYAML==6.0.1 237 | pyzmq==25.1.2 238 | qtconsole==5.5.1 239 | QtPy==2.4.1 240 | rapidfuzz==2.13.7 241 | referencing==0.33.0 242 | regex==2023.12.25 243 | requests==2.31.0 244 | resampy==0.4.2 245 | rfc3339-validator==0.1.4 246 | rfc3986-validator==0.1.1 247 | rich==13.7.1 248 | rouge-score==0.1.2 249 | rpds-py==0.18.0 250 | ruamel.yaml==0.18.6 251 | ruamel.yaml.clib==0.2.8 252 | ruff==0.3.0 253 | s3transfer==0.10.0 254 | sacrebleu==2.4.0 255 | sacremoses==0.1.1 256 | safetensors==0.4.2 257 | scikit-learn==1.4.1.post1 258 | scipy==1.12.0 259 | semantic-version==2.10.0 260 | Send2Trash==1.8.2 261 | sentence-transformers==2.5.1 262 | sentencepiece==0.2.0 263 | sentry-sdk==1.40.6 264 | setproctitle==1.3.3 265 | shellingham==1.5.4 266 | shtab==1.7.0 267 | six==1.16.0 268 | smmap==5.0.1 269 | sniffio==1.3.1 270 | snowballstemmer==2.2.0 271 | sortedcontainers==2.4.0 272 | soundfile==0.12.1 273 | soupsieve==2.5 274 | sox==1.4.1 275 | soxr==0.3.7 276 | Sphinx==7.2.6 277 | sphinxcontrib-applehelp==1.0.8 278 | sphinxcontrib-bibtex==2.6.2 279 | sphinxcontrib-devhelp==1.0.6 280 | sphinxcontrib-htmlhelp==2.0.5 281 | sphinxcontrib-jsmath==1.0.1 282 | sphinxcontrib-qthelp==1.0.7 283 | sphinxcontrib-serializinghtml==1.1.10 284 | stack-data==0.6.3 285 | starlette==0.36.3 286 | sympy==1.12 287 | tabulate==0.9.0 288 | taming-transformers==0.0.1 289 | tensorboard==2.16.2 290 | tensorboard-data-server==0.7.2 291 | tensorstore==0.1.45 292 | termcolor==2.4.0 293 | terminado==0.18.0 294 | text-unidecode==1.3 295 | textdistance==4.6.1 296 | texterrors==0.4.4 297 | threadpoolctl==3.3.0 298 | timm==0.9.16 299 | tinycss2==1.2.1 300 | tokenizers==0.15.2 301 | toml==0.10.2 302 | tomli==2.0.1 303 | tomlkit==0.12.0 304 | toolz==0.12.1 305 | torch==2.2.1 306 | torchaudio==2.2.1 307 | torchdiffeq==0.2.3 308 | torchmetrics==1.3.1 309 | torchsde==0.2.6 310 | torchvision==0.17.1 311 | tornado==6.4 312 | tqdm==4.66.2 313 | traitlets==5.14.1 314 | trampoline==0.1.2 315 | transformers==4.38.2 316 | trimesh==4.1.7 317 | triton==2.2.0 318 | trl==0.7.11 319 | typed-ast==1.5.5 320 | typer==0.9.0 321 | types-python-dateutil==2.8.19.20240106 322 | typing_extensions==4.10.0 323 | tyro==0.7.3 324 | tzdata==2024.1 325 | unsloth @ git+https://github.com/unslothai/unsloth.git@dbba69b085b9d6049b57b48b882af7e9f29df5b2 326 | uri-template==1.3.0 327 | urllib3==2.0.7 328 | uvicorn==0.27.1 329 | wandb==0.16.3 330 | wcwidth==0.2.13 331 | webcolors==1.13 332 | webdataset==0.1.62 333 | webencodings==0.5.1 334 | websocket-client==1.7.0 335 | websockets==11.0.3 336 | Werkzeug==3.0.1 337 | wget==3.2 338 | widgetsnbextension==4.0.10 339 | wrapt==1.16.0 340 | xformers @ https://download.pytorch.org/whl/cu121/xformers-0.0.22.post7-cp310-cp310-manylinux2014_x86_64.whl#sha256=44c33373976705b1f3c5729a5ed24165b21536e3d3eedc58dd60ce68d3603f89 341 | xxhash==3.4.1 342 | yarl==1.9.4 343 | youtokentome==1.0.6 344 | zarr==2.17.0 345 | zipp==3.17.0 346 | -------------------------------------------------------------------------------- /scripts/inference/infer.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | from unsloth import FastLanguageModel 3 | import torch 4 | 5 | def parse_arguments(): 6 | parser = argparse.ArgumentParser(description="Translate text to Hindi using a pretrained language model.") 7 | parser.add_argument("--instruction", type=str, help="Instruction for translation") 8 | parser.add_argument("--input_text", type=str, help="Input text for translation") 9 | parser.add_argument("output_file", type=str, help="Path to the output file to save translated text.") 10 | return parser.parse_args() 11 | 12 | def load_model(): 13 | model, tokenizer = FastLanguageModel.from_pretrained( 14 | model_name="lora_model", # YOUR MODEL YOU USED FOR TRAINING 15 | max_seq_length=2048, 16 | dtype=None, 17 | load_in_4bit=False, 18 | ) 19 | FastLanguageModel.for_inference(model) # Enable native 2x faster inference 20 | return model, tokenizer 21 | 22 | def translate_single_instruction(instruction, input_text, output_file, model, tokenizer): 23 | alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. 24 | 25 | ### Instruction: 26 | {} 27 | 28 | ### Input: 29 | {} 30 | 31 | ### Response: 32 | {}""".format(instruction, input_text, "") 33 | 34 | inputs = tokenizer( 35 | [alpaca_prompt], padding=True, return_tensors="pt").to("cuda") 36 | 37 | outputs = model.generate(**inputs, max_new_tokens=256, use_cache=True) 38 | decoded_outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True) 39 | 40 | with open(output_file, 'w+', encoding='utf-8') as file: 41 | print(decoded_outputs[0], file=file) 42 | 43 | if __name__ == "__main__": 44 | args = parse_arguments() 45 | model, tokenizer = load_model() 46 | translate_single_instruction(args.instruction, args.input_text, args.output_file, model, tokenizer) 47 | -------------------------------------------------------------------------------- /scripts/inference/infer_batch.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | from unsloth import FastLanguageModel 3 | import torch 4 | from tqdm import tqdm 5 | 6 | def load_model(model_name, max_seq_length=2048, dtype=None, load_in_4bit=False): 7 | model, tokenizer = FastLanguageModel.from_pretrained( 8 | model_name=model_name, 9 | max_seq_length=max_seq_length, 10 | dtype=dtype, 11 | load_in_4bit=load_in_4bit 12 | ) 13 | FastLanguageModel.for_inference(model) 14 | return model, tokenizer 15 | 16 | def process_batch(data, model, tokenizer, output_file, infer_output_file): 17 | batch_size = 1 18 | num_batches = (len(data) + batch_size - 1) // batch_size 19 | 20 | alpaca_prompt = "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Instruction:\n{}\n\n### Input:\n{}\n\n### Response:\n{}" 21 | alpaca_prompt_infer = "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Instruction:\n{}\n\n### Input:\n{}\n\n### Response:\n" 22 | 23 | with torch.no_grad(): 24 | for batch_idx in tqdm(range(num_batches)): 25 | batch_data = data[batch_idx * batch_size: (batch_idx + 1) * batch_size] 26 | data_list = [] 27 | infer_list = [] 28 | for item in batch_data: 29 | data_list.append(alpaca_prompt.format("Translate this to Hindi", item, "")) 30 | infer_list.append(alpaca_prompt_infer.format("Translate this to Hindi", item)) 31 | 32 | inputs = tokenizer( 33 | data_list, 34 | padding=True, 35 | return_tensors="pt", 36 | truncation=True 37 | ) 38 | 39 | inputs = inputs.to("cuda") 40 | 41 | outputs = model.generate(input_ids=inputs.input_ids, max_length=256, use_cache=True) 42 | 43 | decoded_outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True) 44 | 45 | with open(output_file, 'a', encoding='utf-8') as file, open(infer_output_file, 'a', encoding='utf-8') as lfile: 46 | for i, output in enumerate(decoded_outputs, start=0): 47 | print(output.replace(infer_list[i], '').replace('\n','').strip(), file=lfile) 48 | print(output, file=file) 49 | 50 | def main(): 51 | parser = argparse.ArgumentParser(description="Process text data using a pre-trained language model.") 52 | parser.add_argument("--model_name", type=str, required=True, help="Name of the pre-trained language model") 53 | parser.add_argument("--input_file", type=str, required=True, help="Path to the input text file") 54 | parser.add_argument("--output_file", type=str, required=True, help="Path to the output text file") 55 | parser.add_argument("--infer_output_file", type=str, required=True, help="Path to the inference output text file") 56 | args = parser.parse_args() 57 | 58 | model, tokenizer = load_model(args.model_name) 59 | 60 | with open(args.input_file, encoding='utf-8') as file: 61 | data = file.readlines() 62 | 63 | process_batch(data, model, tokenizer, args.output_file, args.infer_output_file) 64 | 65 | if __name__ == "__main__": 66 | main() 67 | -------------------------------------------------------------------------------- /scripts/inference/infer_gemma.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | from unsloth import FastLanguageModel 3 | import torch 4 | from tqdm import tqdm 5 | 6 | def load_model(model_name, max_seq_length=2048, dtype=None, load_in_4bit=False, token=None): 7 | model, tokenizer = FastLanguageModel.from_pretrained( 8 | model_name=model_name, 9 | max_seq_length=max_seq_length, 10 | dtype=dtype, 11 | load_in_4bit=load_in_4bit, 12 | token=token 13 | ) 14 | FastLanguageModel.for_inference(model) 15 | return model, tokenizer 16 | 17 | def translate_text(data, model, tokenizer, output_file, infer_output_file, test_string): 18 | template = "Instruction:\n{instruction}\n\nResponse:\n{response}" 19 | template_infer = "Instruction:\n{instruction}\n\nResponse:\n" 20 | 21 | with torch.no_grad(): 22 | for batch_idx in tqdm(range(num_batches)): 23 | batch_data = data[batch_idx * batch_size: (batch_idx + 1) * batch_size] 24 | data_list = [] 25 | infer_list = [] 26 | for item in batch_data: 27 | instruction = f"{test_string}\n\nTranslate the below sentence from English to Hindi in devanagari script.\n{item}" 28 | data_list.append(template.format(instruction=instruction, response="")) 29 | infer_list.append(template_infer.format(instruction=instruction)) 30 | 31 | inputs = tokenizer( 32 | data_list, 33 | padding=True, 34 | return_tensors="pt", 35 | truncation=True 36 | ) 37 | 38 | inputs = inputs.to("cuda") 39 | 40 | outputs = model.generate(input_ids=inputs.input_ids, max_length=256, use_cache=True) 41 | 42 | decoded_outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True) 43 | 44 | with open(output_file, 'a', encoding='utf-8') as file, open(infer_output_file, 'a', encoding='utf-8') as lfile: 45 | for i, output in enumerate(decoded_outputs, start=0): 46 | print(output.replace(infer_list[i], '').replace('\n', '').strip(), file=lfile) 47 | print(output, file=file) 48 | 49 | def main(args): 50 | model, tokenizer = load_model(args.model_name, token=args.token) 51 | 52 | with open(args.input_file, encoding='utf-8') as file: 53 | data = file.readlines() 54 | 55 | batch_size = 1 56 | num_batches = (len(data) + batch_size - 1) // batch_size 57 | 58 | translate_text(data, model, tokenizer, args.output_file, args.infer_output_file, args.test_string) 59 | 60 | if __name__ == "__main__": 61 | parser = argparse.ArgumentParser(description="Translate text from English to Hindi using a pre-trained language model.") 62 | parser.add_argument("--model_name", type=str, required=True, help="Name of the pre-trained language model") 63 | parser.add_argument("--token", type=str, required=True, help="Token for Hugging Face Hub login") 64 | parser.add_argument("--input_file", type=str, required=True, help="Path to the input text file") 65 | parser.add_argument("--output_file", type=str, required=True, help="Path to the output text file") 66 | parser.add_argument("--infer_output_file", type=str, required=True, help="Path to the inference output text file") 67 | parser.add_argument("--test_string", type=str, required=True, help="String for testing") 68 | args = parser.parse_args() 69 | main(args) 70 | -------------------------------------------------------------------------------- /scripts/inference/infer_on_file.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | from unsloth import FastLanguageModel 3 | import torch 4 | 5 | 6 | def parse_arguments(): 7 | parser = argparse.ArgumentParser(description="Translate text to Hindi using a pretrained language model.") 8 | parser.add_argument("input_file", type=str, help="Path to the input file containing text to translate.") 9 | parser.add_argument("output_file", type=str, help="Path to the output file to save translated text.") 10 | return parser.parse_args() 11 | 12 | 13 | def load_model(): 14 | model, tokenizer = FastLanguageModel.from_pretrained( 15 | model_name="lora_model", 16 | max_seq_length=2048, 17 | dtype=None, 18 | load_in_4bit=False, 19 | ) 20 | FastLanguageModel.for_inference(model) 21 | return model, tokenizer 22 | 23 | 24 | def generate_translations(input_file, output_file, model, tokenizer): 25 | with open(input_file, encoding='utf-8') as file: 26 | data = file.readlines() 27 | 28 | data_list = [] 29 | for item in data: 30 | data_list.append(alpaca_prompt.format("Translate this to Hindi", item, "")) 31 | 32 | inputs = tokenizer( 33 | data_list, padding=True, return_tensors="pt").to("cuda") 34 | 35 | outputs = model.generate(**inputs, max_new_tokens=256, use_cache=True) 36 | decoded_outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True) 37 | 38 | with open(output_file, 'w+', encoding='utf-8') as file: 39 | for i, output in enumerate(decoded_outputs, start=0): 40 | print(f"{output}", file=file) 41 | 42 | 43 | if __name__ == "__main__": 44 | args = parse_arguments() 45 | model, tokenizer = load_model() 46 | generate_translations(args.input_file, args.output_file, model, tokenizer) 47 | -------------------------------------------------------------------------------- /scripts/metrics/calculate_bleu.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import sacrebleu 3 | 4 | def calculate_bleu(ref_file, pred_file): 5 | ref = [] 6 | with open(ref_file, 'r') as file: 7 | ref = file.readlines() 8 | ref = [item.strip() for item in ref] 9 | 10 | hi_pred = [] 11 | with open(pred_file, 'r') as file: 12 | hi_pred = file.readlines() 13 | hi_pred = [item.strip() for item in hi_pred] 14 | 15 | bleu_score = sacrebleu.corpus_bleu(hi_pred, [ref]) 16 | return bleu_score.score 17 | 18 | def main(args): 19 | bleu_score = calculate_bleu(args.ref_file, args.pred_file) 20 | print("BLEU Score:", bleu_score) 21 | 22 | if __name__ == "__main__": 23 | parser = argparse.ArgumentParser(description="Calculate BLEU score.") 24 | parser.add_argument("--ref_file", type=str, required=True, help="Path to the reference file") 25 | parser.add_argument("--pred_file", type=str, required=True, help="Path to the prediction file") 26 | args = parser.parse_args() 27 | main(args) 28 | -------------------------------------------------------------------------------- /scripts/train/train_hindi_2b_v0.01.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | from unsloth import FastLanguageModel 3 | import torch 4 | from huggingface_hub import login 5 | from datasets import load_dataset, concatenate_datasets 6 | from trl import SFTTrainer 7 | from transformers import TrainingArguments 8 | 9 | def load_model(model_name, max_seq_length=2048, dtype=None, load_in_4bit=False): 10 | login(token=args.token) 11 | 12 | model, tokenizer = FastLanguageModel.from_pretrained( 13 | model_name=model_name, 14 | max_seq_length=max_seq_length, 15 | dtype=dtype, 16 | load_in_4bit=load_in_4bit, 17 | ) 18 | 19 | model = FastLanguageModel.get_peft_model( 20 | model, 21 | r=16, 22 | target_modules=["q_proj", "k_proj", "v_proj", "o_proj", 23 | "gate_proj", "up_proj", "down_proj",], 24 | lora_alpha=16, 25 | lora_dropout=0, 26 | bias="none", 27 | use_gradient_checkpointing=True, 28 | random_state=3407, 29 | use_rslora=False, 30 | loftq_config=None, 31 | ) 32 | 33 | return model, tokenizer 34 | 35 | def format_prompts(examples): 36 | alpaca_prompt = """Below is an instruction that describes a task, paired with a optional input that provides further context. Write a response that appropriately completes the request. 37 | 38 | ### Instruction: 39 | {} 40 | 41 | ### Input: 42 | {} 43 | 44 | ### Response: 45 | {}""" 46 | 47 | texts = [] 48 | for instruction, input, output in zip(examples["instruction"], examples["input"], examples["output"]): 49 | text = alpaca_prompt.format(instruction, input, output) + tokenizer.eos_token 50 | texts.append(text) 51 | return {"text": texts} 52 | 53 | def load_datasets(): 54 | dataset1 = load_dataset("yahma/alpaca-cleaned", split="train") 55 | dataset1 = dataset1.map(format_prompts, batched=True) 56 | 57 | dataset2 = load_dataset("BhabhaAI/indic-instruct-data-v0.1-filtered", "dolly", split="hi") 58 | dataset2 = dataset2.rename_column("context", "input") 59 | dataset2 = dataset2.rename_column("response", "output") 60 | dataset2 = dataset2.map(format_prompts, batched=True) 61 | 62 | dataset3 = load_dataset("BhabhaAI/indic-instruct-data-v0.1-filtered", "dolly", split="en") 63 | dataset3 = dataset3.rename_column("context", "input") 64 | dataset3 = dataset3.rename_column("response", "output") 65 | dataset3 = dataset3.map(format_prompts, batched=True) 66 | 67 | dataset4 = load_dataset("BhabhaAI/indic-instruct-data-v0.1-filtered", "nmt-seed", split="hi") 68 | dataset4 = dataset4.rename_column("input_text", "input") 69 | dataset4 = dataset4.rename_column("output_text", "output") 70 | dataset4 = dataset4.map(add_instruction, batched=True) 71 | 72 | concatenated_dataset = concatenate_datasets([dataset1, dataset2, dataset3, dataset4]) 73 | return concatenated_dataset 74 | 75 | def add_instruction(example): 76 | example["instruction"] = "Translate this sentence to Hindi" 77 | return example 78 | 79 | def filter_example(example): 80 | return len(example['text']) <= 2048 81 | 82 | def main(args): 83 | model, tokenizer = load_model(args.model_name) 84 | 85 | concatenated_dataset = load_datasets() 86 | filtered_dataset = concatenated_dataset.filter(filter_example) 87 | 88 | train_size = 0.9 89 | train_dataset = filtered_dataset.train_test_split(test_size=1 - train_size)["train"] 90 | validation_dataset = filtered_dataset.train_test_split(test_size=1 - train_size)["test"] 91 | 92 | trainer = SFTTrainer( 93 | model=model, 94 | tokenizer=tokenizer, 95 | train_dataset=train_dataset, 96 | dataset_text_field="text", 97 | max_seq_length=args.max_seq_length, 98 | dataset_num_proc=2, 99 | packing=True, 100 | args=TrainingArguments( 101 | per_device_train_batch_size=1, 102 | gradient_accumulation_steps=4, 103 | warmup_steps=50, 104 | num_train_epochs=2, 105 | learning_rate=2e-4, 106 | fp16=not torch.cuda.is_bf16_supported(), 107 | bf16=torch.cuda.is_bf16_supported(), 108 | logging_steps=1, 109 | optim="paged_adamw_32bit", 110 | weight_decay=0.01, 111 | lr_scheduler_type="linear", 112 | seed=3407, 113 | output_dir="outputs", 114 | ), 115 | ) 116 | 117 | trainer_stats = trainer.train() 118 | model.save_pretrained("lora_model") 119 | 120 | if __name__ == "__main__": 121 | parser = argparse.ArgumentParser(description="Train a model for instructional text translation.") 122 | parser.add_argument("--model_name", type=str, required=True, help="Name of the pre-trained language model") 123 | parser.add_argument("--token", type=str, required=True, help="Token for Hugging Face Hub login") 124 | parser.add_argument("--max_seq_length", type=int, default=2048, help="Maximum sequence length") 125 | args = parser.parse_args() 126 | main(args) 127 | --------------------------------------------------------------------------------