The repository provides examples of fine-tuning based on the Hugging Face TRL library and LoRA technology in the following steps:
- Download Dataset: Use
load_datasetLoad multilingual inference datasets such asHuggingFaceH4/Multilingual-ThinkingThe - Configuring LoRA Parameters: Definitions
LoraConfigSettingsrcap (a poem)lora_alphaetc. and specify the target module (e.g.q_projcap (a poem)v_proj). - Loading Models: By
AutoModelForCausalLM.from_pretrainedLoad the pre-trained model and apply the LoRA configuration. - Implementation fine-tuning: refer to the repository in the
finetune.ipynb, using the TRL library for fine-tuning. - Save model: Save the model after fine-tuning is complete for specific tasks (e.g., multilingual reasoning).
This process is applied to optimize the performance of a model on a specific dataset.
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 use gpt-oss-recipes repository for model fine-tuning?
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