xTuring
nanoChatGPT
| xTuring | nanoChatGPT | |
|---|---|---|
| 31 | 3 | |
| 2,541 | 50 | |
| 0.7% | - | |
| 8.4 | 9.3 | |
| 2 months ago | 10 months ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
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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.
nanoChatGPT
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A full tutorial on turning GPT-2 into a conversational AI
Hi, Vatsa here, this is tutorial on turning GPT-2 into a conversational bot, it was a fun project, and I hope you like it it!
github -> https://github.com/VatsaDev/nanoChatGPT
- NanoChatGPT - turning nanogpt into a chat model/LLM
- NanoChatGpt, NanoGPT, Finetuned for Chatting
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.
unsloth - Finetune Llama 3, Mistral, Phi & Gemma LLMs 2-5x faster with 80% less memory
axolotl - Go ahead and axolotl questions
llm-toys - Small(7B and below) finetuned LLMs for a diverse set of useful tasks
FinGPT - FinGPT: Open-Source Financial Large Language Models! Revolutionize 🔥 We release the trained model on HuggingFace.
DB-GPT-Hub - A repository that contains models, datasets, and fine-tuning techniques for DB-GPT, with the purpose of enhancing model performance in Text-to-SQL
awesome-totally-open-chatgpt - A list of totally open alternatives to ChatGPT
Zicklein - Finetuning instruct-LLaMA on german datasets.
Meshtasticator - Discrete-event and interactive simulator for Meshtastic.
pistoBot - Create an AI that chats like you
praetor-data - Praetor is a lightweight finetuning data and prompt management tool [Moved to: https://github.com/US-Artificial-Intelligence/praetor-data]