{"data":{"slug":"daoyuanli2816-can-i-finetune-this","name":"can-i-finetune-this","tagline":"Estimate if a Hugging Face model can fine-tune locally on GPU","github_url":"https://github.com/DaoyuanLi2816/can-i-finetune-this","owner":"DaoyuanLi2816","repo":"can-i-finetune-this","owner_avatar_url":"https://avatars.githubusercontent.com/u/94409450?v=4","primary_language":"Python","stars":792,"forks":107,"topics":["bitsandbytes","fine-tuning","gpu","hugging-face","llm","lora","memory-estimation","peft","pytorch","qlora","transformers","vram"],"archived":false,"github_pushed_at":"2026-07-23T05:11:25+00:00","maintenance_label":"Steady","stars_delta_30d":0,"url":"https://www.graphcanon.com/tools/daoyuanli2816-can-i-finetune-this","markdown_url":"https://www.graphcanon.com/tools/daoyuanli2816-can-i-finetune-this.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/daoyuanli2816-can-i-finetune-this","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=daoyuanli2816-can-i-finetune-this","description":"Estimate whether a Hugging Face model fits and fine-tunes on your local GPU.","homepage_url":"https://pypi.org/project/canifinetune/","license":"MIT","open_issues":0,"watchers":88,"ai_summary":"Provides tools to estimate the feasibility of fine-tuning large language models from Hugging Face on local GPUs considering VRAM and other resource constraints.","readme_excerpt":"## Install\n\n`canifinetune` runs in two layers:\n\n| Layer | Install | What you get |\n| --- | --- | --- |\n| Core (estimate / recommend / recipe / report) | `pip install canifinetune` | All CLI commands. No PyTorch required. |\n| Training (bench / real fine-tuning) | `pip install canifinetune[train]` | Adds `torch`, `transformers`, `peft`, `bitsandbytes`, `trl`, `datasets`. |\n| Reporting extras | `pip install canifinetune[report]` | Pandas/tabulate for prettier tables. |\n| Development | `pip install canifinetune[dev]` | pytest, ruff, mypy. |\n\nIf you use `uv`:\n\n```bash\nuv venv\nuv pip install -e \".[dev,report]\"","github_created_at":"2026-05-16T17:14:13+00:00","created_at":"2026-07-11T11:40:17.01523+00:00","updated_at":"2026-08-24T06:01:11.80027+00:00","categories":[{"slug":"llm-frameworks","name":"LLM Frameworks","url":"https://www.graphcanon.com/categories/llm-frameworks","markdown_url":"https://www.graphcanon.com/categories/llm-frameworks.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/llm-frameworks"},{"slug":"model-training","name":"Model Training","url":"https://www.graphcanon.com/categories/model-training","markdown_url":"https://www.graphcanon.com/categories/model-training.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/model-training"}],"tags":[{"slug":"bitsandbytes","name":"bitsandbytes"},{"slug":"fine-tuning","name":"fine-tuning"},{"slug":"gpu","name":"gpu"},{"slug":"hugging-face","name":"hugging-face"},{"slug":"llm","name":"llm"},{"slug":"lora","name":"lora"},{"slug":"memory-estimation","name":"memory-estimation"},{"slug":"peft","name":"peft"}],"trust":{"provenance":{"is_fork":false,"github_id":1240872088,"owner_type":"User","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-24T06:01:10.986Z","maintenance":{"label":"Steady","score":60,"methodology":"github_public_v1","releases_90d":3,"days_since_push":32,"last_release_at":"2026-07-23T05:02:57Z","stars_delta_30d":0,"open_issues_delta_30d":0},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T11:40:22.504Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-08-24T06:01:11.444Z"},"has_cli":{"value":true,"source":"pyproject.toml:[project.scripts]","observed_at":"2026-08-24T06:01:11.444Z"},"languages":{"value":["python"],"source":"github.language+pyproject.toml","observed_at":"2026-08-24T06:01:11.444Z"},"license_spdx":{"value":"MIT","source":"github.license","observed_at":"2026-08-24T06:01:11.444Z"}},"decision_facts":{"hosting":null,"pricing":{"model":"freemium","summary":"Free for use with no limitations on functionality due to it being open-source under the MIT license."},"requirements":{"notes":["Python environment is required.","Support for models from Hugging Face ecosystem."],"min_ram_gb":null,"requires_docker":false},"constraints":{"min_ram_gb":null,"pricing_model":"freemium","requires_docker":false},"when_to_use":["You have specific Hugging Face models to evaluate for fine-tuning locally without exceeding your GPU's memory limits, and you are considering using bitsandbytes or similar optimization techniques.","Your project includes fine-tuning LLMs with PyTorch where memory-estimation is critical before initiating the training process."],"when_not_to_use":["You require support for frameworks other than Hugging Face models and PyTorch, as this tool focuses on these technologies.","If your machine learning tasks do not involve fine-tuning local LLMs but rather use pre-trained models in inference mode only or work mainly with CPUs."],"source":"enrich:decision_facts","observed_at":"2026-07-14T20:31:46.742Z"},"constraint_facets":{"min_ram_gb":null,"pricing_model":"freemium","requires_docker":false},"decision_summary":[{"label":"Pricing","value":"freemium - Free for use with no limitations on functionality due to it being open-source under the MIT license."},{"label":"Requirements","value":"Python environment is required.; Support for models from Hugging Face ecosystem."},{"label":"Adopt for","value":"can-i-finetune-this assists in estimating if fine-tuning a Hugging Face model is feasible given the VRAM and other resource constraints of your local GPU."},{"label":"License detail","value":"This tool is released under the MIT License, allowing free usage for both personal and commercial projects."}]}}