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Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024)
✨✨Latest Advances on Multimodal Large Language Models
《开源大模型食用指南》针对中国宝宝量身打造的基于Linux环境快速微调(全参数/Lora)、部署国内外开源大模型(LLM)/多模态大模型(MLLM)教程
Retrieval and Retrieval-augmented LLMs
Langchain-Chatchat(原Langchain-ChatGLM)基于 Langchain 与 ChatGLM, Qwen 与 Llama 等语言模型的 RAG 与 Agent 应用 | Langchain-Chatchat (formerly langchain-ChatGLM), local knowledge based LLM (like ChatGLM, Qwen and…
使用peft库,对chatGLM-6B/chatGLM2-6B实现4bit的QLoRA高效微调,并做lora model和base model的merge及4bit的量化(quantize)。
AutoChain: Build lightweight, extensible, and testable LLM Agents
Tuning LLMs with no tears💦; Sample Design Engineering (SDE) for more efficient downstream-tuning.
MedicalGPT: Training Your Own Medical GPT Model with ChatGPT Training Pipeline. 训练医疗大模型,实现了包括增量预训练(PT)、有监督微调(SFT)、RLHF、DPO、ORPO、GRPO。
ChatGLM2-6B: An Open Bilingual Chat LLM | 开源双语对话语言模型
中文nlp解决方案(大模型、数据、模型、训练、推理)
🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.
The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
中文LLaMA&Alpaca大语言模型+本地CPU/GPU训练部署 (Chinese LLaMA & Alpaca LLMs)
BELLE: Be Everyone's Large Language model Engine(开源中文对话大模型)
Train transformer language models with reinforcement learning.
⭐️ NLP Algorithms with transformers lib. Supporting Text-Classification, Text-Generation, Information-Extraction, Text-Matching, RLHF, SFT etc.
Easy-to-use,Modular and Extendible package of deep-learning based CTR models .
Chinese version of CLIP which achieves Chinese cross-modal retrieval and representation generation.
This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".
经济学人(含音频)、纽约客、卫报、连线、大西洋月刊等英语杂志免费下载,支持epub、mobi、pdf格式, 每周更新
Tutorials on how to implement a few key architectures for image classification using PyTorch and TorchVision.
Revisiting Pre-trained Models for Chinese Natural Language Processing (MacBERT)
强化学习中文教程(蘑菇书🍄),在线阅读地址:https://datawhalechina.github.io/easy-rl/