Use PEFT or Full-parameter to finetune 350+ LLMs or 90+ MLLMs. (Qwen2, GLM4v, Internlm2.5, Yi, Llama3.1, Llava-Video, Internvl2, MiniCPM-V-2.6, Deepseek, Baichuan2, Gemma2, Phi3-Vision, ...)
-
Updated
Sep 19, 2024 - Python
Use PEFT or Full-parameter to finetune 350+ LLMs or 90+ MLLMs. (Qwen2, GLM4v, Internlm2.5, Yi, Llama3.1, Llava-Video, Internvl2, MiniCPM-V-2.6, Deepseek, Baichuan2, Gemma2, Phi3-Vision, ...)
tensorflow를 사용하여 텍스트 전처리부터, Topic Models, BERT, GPT, LLM과 같은 최신 모델의 다운스트림 태스크들을 정리한 Deep Learning NLP 저장소입니다.
SiLLM simplifies the process of training and running Large Language Models (LLMs) on Apple Silicon by leveraging the MLX framework.
Notus is a collection of fine-tuned LLMs using SFT, DPO, SFT+DPO, and/or any other RLHF techniques, while always keeping a data-first approach
Step-aware Preference Optimization: Aligning Preference with Denoising Performance at Each Step
Align Anything: Training All-modality Model with Feedback
CodeUltraFeedback: aligning large language models to coding preferences
Various training, inference and validation code and results related to Open LLM's that were pretrained (full or partially) on the Dutch language.
[ACL 2024] Self-Training with Direct Preference Optimization Improves Chain-of-Thought Reasoning
Data and models for the paper "Configurable Safety Tuning of Language Models with Synthetic Preference Data"
Examples for using the SiLLM framework for training and running Large Language Models (LLMs) on Apple Silicon
Add a description, image, and links to the dpo topic page so that developers can more easily learn about it.
To associate your repository with the dpo topic, visit your repo's landing page and select "manage topics."