Finetuning Open Source Large Language Models

Material for the O'Reilly course

If possible, work with uv. If not, you need to install at least the following packages:

Name		Name	Last commit message	Last commit date
Latest commit History 7 Commits
10-prepare-dataset-finetune.ipynb		10-prepare-dataset-finetune.ipynb
10000_All_Beauty.json.xz		10000_All_Beauty.json.xz
11-bert-finetune-classification.ipynb		11-bert-finetune-classification.ipynb
12-alternative-zeroshot.ipynb		12-alternative-zeroshot.ipynb
2000_All_Beauty.json.xz		2000_All_Beauty.json.xz
21-sbert-finetune.ipynb		21-sbert-finetune.ipynb
22-create-sbert-data-qwen-reranker.ipynb		22-create-sbert-data-qwen-reranker.ipynb
31-phi1-full-finetune.ipynb		31-phi1-full-finetune.ipynb
31-qwen2-05-full-finetune.ipynb		31-qwen2-05-full-finetune.ipynb
31b-qwen2-05-bare.ipynb		31b-qwen2-05-bare.ipynb
31b-qwen2-05.ipynb		31b-qwen2-05.ipynb
32-llama32-1-huggingface.ipynb		32-llama32-1-huggingface.ipynb
32b-llama32-1.ipynb		32b-llama32-1.ipynb
33-smolvm-1-huggingface.ipynb		33-smolvm-1-huggingface.ipynb
33b-smolvm-1.ipynb		33b-smolvm-1.ipynb
34-phi3-unsloth.ipynb		34-phi3-unsloth.ipynb
34b-phi3.ipynb		34b-phi3.ipynb
README.md		README.md
ai-abstract-dataset.jsonl.xz		ai-abstract-dataset.jsonl.xz
ai-abstracts.jsonl.xz		ai-abstracts.jsonl.xz
pyproject.toml		pyproject.toml
requirements.txt		requirements.txt

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