The steps to fine-tune the model in gpt-oss-recipes are as follows:
- Download Dataset: Use
load_datasetLoading a multilingual inference dataset (such as theHuggingFaceH4/Multilingual-Thinking). - Configuring LoRA Parameters: Definitions
LoraConfig(e.g.r=8cap (a poem)target_modules=["q_proj", "v_proj"]). - Loading Models: By
AutoModelForCausalLMLoad the base model and apply the LoRA configuration. - fine tune: Using TRL libraries (such as the repository's
finetune.ipynb(Example) Complete the training. - Save model: Fine-tuned models can be saved and used for specific tasks (e.g., multilingual reasoning).
This method, combined with LoRA technology, significantly reduces video memory requirements and is suitable for efficient fine-tuning of large models on limited hardware.
This answer comes from the articleCollection of scripts and tutorials for fine-tuning OpenAI GPT OSS modelsThe
May not be reproduced without permission:AI productivity tools " How to fine-tune models in gpt-oss-recipes?
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