xTuring
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| xTuring | BELLE | |
|---|---|---|
| 31 | 1 | |
| 2,525 | 7,566 | |
| 0.9% | 1.6% | |
| 8.4 | 9.0 | |
| about 1 month ago | about 2 months ago | |
| Python | HTML | |
| Apache License 2.0 | Apache License 2.0 |
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xTuring
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I'm developing an open-source AI tool called xTuring, enabling anyone to construct a Language Model with just 5 lines of code. I'd love to hear your thoughts!
Explore the project on GitHub here.
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LLaMA 2 fine-tuning made easier and faster
If you're curious, I encourage you to: - Dive deeper with the LLaMA 2 tutorial here. - Explore the project on GitHub here. - Connect with our community on Discord here.
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RAG vs. Fine-Tuning
If you want best performance, you need to do both RAG and fine-tuning very well. There are plenty of resources on doing fine-tuning thought. I'm one of the contributors to https://github.com/stochasticai/xturing project focused on fine-tuning LLMs. You can find help in the discord channel listed on the GitHub.
- Build, customize and control your own personal LLMs via xTuring OSS
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Finetuning LLaMA 2 (the base models) ?
What tools do you use and achieved great results ? … For me i have tried xturing and SFTTrainer and they got me a semi okay results.
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Finetuning using Google Colab (Free Tier)
Code: https://github.com/stochasticai/xTuring/blob/main/examples/llama/llama_lora_int8.py Colab: https://colab.research.google.com/drive/1SQUXq1AMZPSLD4mk3A3swUIc6Y2dclme?usp=sharing
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I would like to try my hand at finetuning some models. What is the best way to start? I have some questions that I'd appreciate your help on.
We are a group of researchers out of Harvard working on open-source library called xTuring, focused on fine-tuning LLMs: https://github.com/stochasticai/xturing.
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Fine tuning on my tweets
Fine tuning I was thinking about using this (low GPU memory footprint): https://github.com/stochasticai/xturing/blob/main/examples/int4_finetuning/README.md
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Colab for finetuning llama models in 4-bit?
I can't speak for QLORA, as I haven't had a chance to get an implementation working, but I've had success with StochasticAI's Xturing. It's by far the most streamlined method of finetuning I've come across, and they offer int8 and int4 fintuning (but only for llama-7B).
- Just wanna say this.
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What are some alternatives?
quivr - Your GenAI Second Brain 🧠 A personal productivity assistant (RAG) ⚡️🤖 Chat with your docs (PDF, CSV, ...) & apps using Langchain, GPT 3.5 / 4 turbo, Private, Anthropic, VertexAI, Ollama, LLMs, Groq that you can share with users ! Local & Private alternative to OpenAI GPTs & ChatGPT powered by retrieval-augmented generation.
Chinese-LLaMA-Alpaca - 中文LLaMA&Alpaca大语言模型+本地CPU/GPU训练部署 (Chinese LLaMA & Alpaca LLMs)
axolotl - Go ahead and axolotl questions
LLaMA-LoRA-Tuner - UI tool for fine-tuning and testing your own LoRA models base on LLaMA, GPT-J and more. One-click run on Google Colab. + A Gradio ChatGPT-like Chat UI to demonstrate your language models.
FinGPT - FinGPT: Open-Source Financial Large Language Models! Revolutionize 🔥 We release the trained model on HuggingFace.
Luotuo-Chinese-LLM - 骆驼(Luotuo): Open Sourced Chinese Language Models. Developed by 陈启源 @ 华中师范大学 & 李鲁鲁 @ 商汤科技 & 冷子昂 @ 商汤科技
awesome-totally-open-chatgpt - A list of totally open alternatives to ChatGPT
ChatGLM-6B - ChatGLM-6B: An Open Bilingual Dialogue Language Model | 开源双语对话语言模型
Meshtasticator - Discrete-event and interactive simulator for Meshtastic.
lora-air-time - A Calculator that calculate the Time on Air (ToA) of LoRa packet. Based on the formula in Semtech's datasheet.
Zicklein - Finetuning instruct-LLaMA on german datasets.
LLaMA-Cult-and-More - Large Language Models for All, 🦙 Cult and More, Stay in touch !