hugging-face-model-trainer

What it does

This skill enables the training or fine-tuning of language models using Transformer Reinforcement Learning (TRL). It leverages Hugging Face Jobs to provide a scalable infrastructure for tasks such as Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and GRPO (Generalized Response Policy Optimization) alongside reward model training. Users can efficiently train custom AI models without managing the underlying hardware.

When to use it

• Fine-tuning a large language model for a specific task: For example, adapting a base LLM to generate marketing copy or summarize legal documents.
• Training a reward model: To evaluate and rank different responses from an LLM based on desired criteria like helpfulness or safety.
• Implementing Reinforcement Learning from Human Feedback (RLHF): Specifically using DPO or GRPO techniques to align language models with human preferences.
• Scaling training jobs: When the model size or dataset is too large for local hardware, and distributed training is required.

Key capabilities

• Supports Supervised Fine-Tuning (SFT)
• Enables Direct Preference Optimization (DPO)
• Facilitates Generalized Response Policy Optimization (GRPO)
• Includes reward model training functionality
• Utilizes Hugging Face Jobs infrastructure for scalable training

Example prompts

• "Train a DPO model using this dataset and the base Llama-2 7B model."
• "Fine-tune this language model with SFT, optimizing for text summarization."
• "Create a reward model to evaluate responses based on helpfulness and conciseness."

Tips & gotchas

• Familiarity with Transformer Reinforcement Learning (TRL) concepts is recommended.
• Ensure you have access to and understand the Hugging Face Jobs infrastructure for proper job submission and monitoring.