From f97beca23a1c79df38769b8dd40c9b19d4e5ef5c Mon Sep 17 00:00:00 2001 From: hoshi-hiyouga Date: Fri, 26 Jul 2024 11:29:09 +0800 Subject: [PATCH] Update README.md --- README.md | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 61f482fc..8e41d832 100644 --- a/README.md +++ b/README.md @@ -47,7 +47,7 @@ Choose your path: ## Features - **Various models**: LLaMA, LLaVA, Mistral, Mixtral-MoE, Qwen, Yi, Gemma, Baichuan, ChatGLM, Phi, etc. -- **Integrated methods**: (Continuous) pre-training, (multimodal) supervised fine-tuning, reward modeling, PPO (The details of TRL PPO can refer to [this blog](https://newfacade.github.io/notes-on-reinforcement-learning/17-ppo-trl.html).), DPO, KTO, ORPO, etc. +- **Integrated methods**: (Continuous) pre-training, (multimodal) supervised fine-tuning, reward modeling, PPO, DPO, KTO, ORPO, etc. - **Scalable resources**: 16-bit full-tuning, freeze-tuning, LoRA and 2/3/4/5/6/8-bit QLoRA via AQLM/AWQ/GPTQ/LLM.int8/HQQ/EETQ. - **Advanced algorithms**: GaLore, BAdam, DoRA, LongLoRA, LLaMA Pro, Mixture-of-Depths, LoRA+, LoftQ, PiSSA and Agent tuning. @@ -201,6 +201,9 @@ You also can add a custom chat template to [template.py](src/llamafactory/data/t | ORPO Training | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | | SimPO Training | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | +> [!TIP] +> The implementation details of PPO can be found in [this blog](https://newfacade.github.io/notes-on-reinforcement-learning/17-ppo-trl.html). + ## Provided Datasets
Pre-training datasets