{"id":23377509,"url":"https://github.com/opencodeiiita/finetuning_llama","last_synced_at":"2025-08-23T23:32:17.418Z","repository":{"id":268282213,"uuid":"902953793","full_name":"opencodeiiita/Finetuning_Llama","owner":"opencodeiiita","description":"Fine-Tuning LLaMA for Indian Laws","archived":false,"fork":false,"pushed_at":"2024-12-15T18:51:37.000Z","size":6,"stargazers_count":0,"open_issues_count":2,"forks_count":4,"subscribers_count":1,"default_branch":"main","last_synced_at":"2024-12-15T19:33:58.440Z","etag":null,"topics":["llm","opencode24","python","pytorch","tensorboard","transformers"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/opencodeiiita.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-12-13T16:01:58.000Z","updated_at":"2024-12-15T18:51:41.000Z","dependencies_parsed_at":"2024-12-15T19:44:02.505Z","dependency_job_id":null,"html_url":"https://github.com/opencodeiiita/Finetuning_Llama","commit_stats":null,"previous_names":["opencodeiiita/finetuning_llama"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/opencodeiiita%2FFinetuning_Llama","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/opencodeiiita%2FFinetuning_Llama/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/opencodeiiita%2FFinetuning_Llama/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/opencodeiiita%2FFinetuning_Llama/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/opencodeiiita","download_url":"https://codeload.github.com/opencodeiiita/Finetuning_Llama/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":230750976,"owners_count":18274975,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["llm","opencode24","python","pytorch","tensorboard","transformers"],"created_at":"2024-12-21T18:14:54.769Z","updated_at":"2024-12-21T18:14:56.918Z","avatar_url":"https://github.com/opencodeiiita.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Finetuning_Llama\r\nFine-tuning large language models like LLaMA has transformed the way we adapt pre-trained models for specialized tasks. This repository focuses on parameter-efficient fine-tuning techniques such as LoRA and QLoRA to adapt the LLaMA2-7B model to Indian legal text datasets.\u003cbr\u003e\r\n\r\n## Problem Statement\r\n\r\nYou are tasked with fine-tuning the LLaMA2-7B model on a dataset related to Indian laws to make it capable of generating context-aware legal insights. The challenge is to leverage advanced fine-tuning techniques like LoRA/QLoRA to optimize the training process while keeping computational requirements minimal. Demonstrate your skills in model tuning and deployment! \u003cbr\u003e\r\n\r\n## Instructions\r\n\r\n- Refer to articles, research papers, and official documentation for guidance on techniques and best practices.\r\n\r\n- Do not alter any pre-written code or comments.\r\n\r\n- Write code only in the provided space and document your steps with comments for better understanding.\r\n\r\n- Use Google Colab or similar GPU-enabled environments for training and testing the model. \u003cbr\u003e\r\n\r\n\r\n\r\n\r\n- Help\r\n\r\n- For any queries or support, feel free to reach out via email at iib2023013@iiita.ac.in or iit2023153@iiita.ac.in or join the discussion on the project’s Discord server. \u003cbr\u003e\r\n\r\n## Contributions\r\n\r\n- Contributions are welcome! Follow these steps:\r\n\r\n- Fork this repository and clone it to your local device.\r\n\r\n- Work on individual tasks in a separate branch.\r\n\r\n- Push your updates to the forked repo and create a Pull Request (PR).\r\n\r\n- Your PR will be reviewed, and upon approval, merged into the main repository.\r\n\r\n## Resources\r\n\r\n- Dataset: Indian Law Dataset (https://huggingface.co/datasets/jizzu/llama2_indian_law_v2)\r\n\r\n- Parameter-Efficient Fine-Tuning: LoRA Paper (https://arxiv.org/pdf/1902.00751)\r\n\r\n- Hugging Face Transformers Documentation: Link(https://huggingface.co/docs/transformers/index) \u003cbr\u003e\r\n\r\n\r\n\r\n  \r\n\r\n## Happy Fine-Tuning!\r\n\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fopencodeiiita%2Ffinetuning_llama","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fopencodeiiita%2Ffinetuning_llama","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fopencodeiiita%2Ffinetuning_llama/lists"}