mixtral-offloading
xTuring
| mixtral-offloading | xTuring | |
|---|---|---|
| 3 | 31 | |
| 2,262 | 2,536 | |
| - | 0.5% | |
| 8.6 | 8.4 | |
| about 2 months ago | 2 months ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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mixtral-offloading
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DBRX: A New Open LLM
Waiting for Mixed Quantization with MQQ and MoE Offloading [1]. With that I was able to run Mistral 8x7B on my 10 GB VRAM rtx3080... This should work for DBRX and should shave off a ton of VRAM requirement.
1. https://github.com/dvmazur/mixtral-offloading?tab=readme-ov-...
- Mixtral in Colab
- Run Mixtral-8x7B models in Colab or consumer desktops
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.
What are some alternatives?
lightning-mlflow-hf - Use QLoRA to tune LLM in PyTorch-Lightning w/ Huggingface + MLflow
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.
dbrx - Code examples and resources for DBRX, a large language model developed by Databricks
axolotl - Go ahead and axolotl questions
FinGPT - FinGPT: Open-Source Financial Large Language Models! Revolutionize 🔥 We release the trained model on HuggingFace.
awesome-totally-open-chatgpt - A list of totally open alternatives to ChatGPT
Meshtasticator - Discrete-event and interactive simulator for Meshtastic.
Zicklein - Finetuning instruct-LLaMA on german datasets.
safetensors_util - Utility for Safetensors Files
Open-Assistant - OpenAssistant is a chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically to do so.
azure-search-openai-demo - A sample app for the Retrieval-Augmented Generation pattern running in Azure, using Azure AI Search for retrieval and Azure OpenAI large language models to power ChatGPT-style and Q&A experiences.
BELLE - BELLE: Be Everyone's Large Language model Engine（开源中文对话大模型）