Zero to Hero with the TRL learning link bomb 💣
TRL is a backbone of LLM post-training. Sure, there are solid alternatives like Unsloth, Axolotl, and AutoTrain, but if you need a daily driver to go from tinkering to production, TRL delivers.
The catch? No one-stop course covers the full journey. Thankfully, the community is awesome, so we’ve pieced it together!
Here are six top-notch, straight-to-the-point lessons that dive into TRL’s core features!
1. How to fine-tune Google Gemma with ChatML and Hugging Face TRL
Start with a clear notebook that focuses on SFT and data format. This blog walks through fine-tuning Google Gemma LLMs using Hugging Face’s TRL library and ChatML format. It covers setting up the environment, preparing datasets, and leveraging SFTTrainer with QLoRA for efficient training on consumer GPUs, culminating in inference tests on conversational prompts.
https://www.philschmid.de/fine-tune-google-gemma
By Phil Schmid
2. Fine-Tuning LLM to Generate Persian Product Catalogs in JSON Format
Build on the same classes, but incorporate output structure and inference. Learn how to fine-tune a Llama-2-7B model using QLoRA and PEFT to generate structured Persian product catalogs. This guide covers dataset preparation, efficient fine-tuning on consumer GPUs, and deploying the model for inference with the fast Vllm engine.
How to Fine-Tune Multimodal Models or VLMs with Hugging Face TRL
Take our SFT skills over to vision language models. Master fine-tuning Vision-Language Models (e.g., Qwen2-VL-7B) with TRL and QLoRA. This guide explains setting up datasets, defining prompts, and using SFTTrainer for multimodal tasks like generating SEO-friendly descriptions.
https://www.philschmid.de/fine-tune-multimodal-llms-with-trl
By Phil Schmid
Fine-Tuning a Vision Language Model (Qwen2-VL-7B) with the Hugging Face Ecosystem (TRL)
Build on those vision skills for more complex visual tasks. This tutorial shows how to fine-tune the Qwen2-VL-7B model for visual question answering using the ChartQA dataset. It includes data preparation, memory-efficient training with QLoRA, and exploring prompting as an alternative to fine-tuning.
https://huggingface.co/learn/cookbook/fine_tuning_vlm_trl
Fine-Tune Mistral-7b with Direct Preference Optimization
Move on to the DPOTrainer and preference data. This practical guide demonstrates fine-tuning Mistral-7b using Direct Preference Optimization (DPO) to align model outputs with human preferences. It highlights dataset preparation, training, and evaluation for improved leaderboard performance.
https://mlabonne.github.io/blog/posts/Fine_tune_Mistral_7b_with_DPO.html
Fine-Tune Llama 3 with ORPO
Discover how ORPO combines instruction tuning and preference alignment into a single process, streamlining fine-tuning on Llama 3 8B with TRL. Learn how this method improves efficiency and alignment while reducing training steps.
https://mlabonne.github.io/blog/posts/2024-04-19_Fine_tune_Llama_3_with_ORPO.html
These tutorials provide a comprehensive yet concise roadmap through TRL across various fine-tuning and alignment scenarios, making it easier to apply cutting-edge techniques to your LLM projects.
Let me know if this is useful
These tutorials provide a comprehensive but concise roadmap through TRL across the main fine-tuning and alignment classes. Let me know if you would like a dedicated course on TRL basics 🤔, and I'll get to work.