TheBloke's LLM work is generously supported by a grant from andreessen horowitz (a16z)
The original Vicuna 7B repository contains deltas rather than weights. Rather than merging the deltas myself, I used the model files from https://huggingface.co/helloollel/vicuna-7b.
Two model files are provided. You don't need both, choose the one you prefer.
Details of the files provided:
safetensorsformat, with improved file security, created with the latest GPTQ-for-LLaMa code.
- Command to create:
python3 llama.py vicuna-7B c4 --wbits 4 --true-sequential --act-order --groupsize 128 --save_safetensors vicuna-7B-GPTQ-4bit-128g.safetensors
These model files were created with the latest GPTQ code, and require that the latest GPTQ-for-LLaMa is used inside the UI.
Here are the commands I used to clone the Triton branch of GPTQ-for-LLaMa, clone text-generation-webui, and install GPTQ into the UI:
git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa git clone https://github.com/oobabooga/text-generation-webui mkdir -p text-generation-webui/repositories ln -s GPTQ-for-LLaMa text-generation-webui/repositories/GPTQ-for-LLaMa
Then install this model into
text-generation-webui/models and launch the UI as follows:
cd text-generation-webui python server.py --model vicuna-7B-GPTQ-4bit-128g --wbits 4 --groupsize 128 # add any other command line args you want
The above commands assume you have installed all dependencies for GPTQ-for-LLaMa and text-generation-webui. Please see their respective repositories for further information.
If you are on Windows, or cannot use the Triton branch of GPTQ for any other reason, you can instead use the CUDA branch:
git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa -b cuda cd GPTQ-for-LLaMa python setup_cuda.py install
Then link that into
text-generation-webui/repositories as described above.
For further support, and discussions on these models and AI in general, join us at:
Thanks to the chirper.ai team!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
Special thanks to: Aemon Algiz.
Patreon special mentions: Sam, theTransient, Jonathan Leane, Steven Wood, webtim, Johann-Peter Hartmann, Geoffrey Montalvo, Gabriel Tamborski, Willem Michiel, John Villwock, Derek Yates, Mesiah Bishop, Eugene Pentland, Pieter, Chadd, Stephen Murray, Daniel P. Andersen, terasurfer, Brandon Frisco, Thomas Belote, Sid, Nathan LeClaire, Magnesian, Alps Aficionado, Stanislav Ovsiannikov, Alex, Joseph William Delisle, Nikolai Manek, Michael Davis, Junyu Yang, K, J, Spencer Kim, Stefan Sabev, Olusegun Samson, transmissions 11, Michael Levine, Cory Kujawski, Rainer Wilmers, zynix, Kalila, Luke @flexchar, Ajan Kanaga, Mandus, vamX, Ai Maven, Mano Prime, Matthew Berman, subjectnull, Vitor Caleffi, Clay Pascal, biorpg, alfie_i, 阿明, Jeffrey Morgan, ya boyyy, Raymond Fosdick, knownsqashed, Olakabola, Leonard Tan, ReadyPlayerEmma, Enrico Ros, Dave, Talal Aujan, Illia Dulskyi, Sean Connelly, senxiiz, Artur Olbinski, Elle, Raven Klaugh, Fen Risland, Deep Realms, Imad Khwaja, Fred von Graf, Will Dee, usrbinkat, SuperWojo, Alexandros Triantafyllidis, Swaroop Kallakuri, Dan Guido, John Detwiler, Pedro Madruga, Iucharbius, Viktor Bowallius, Asp the Wyvern, Edmond Seymore, Trenton Dambrowitz, Space Cruiser, Spiking Neurons AB, Pyrater, LangChain4j, Tony Hughes, Kacper Wikieł, Rishabh Srivastava, David Ziegler, Luke Pendergrass, Andrey, Gabriel Puliatti, Lone Striker, Sebastain Graf, Pierre Kircher, Randy H, NimbleBox.ai, Vadim, danny, Deo Leter
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
Model type: Vicuna is an open-source chatbot trained by fine-tuning LLaMA on user-shared conversations collected from ShareGPT. It is an auto-regressive language model, based on the transformer architecture.
Model date: Vicuna was trained between March 2023 and April 2023.
Organizations developing the model: The Vicuna team with members from UC Berkeley, CMU, Stanford, and UC San Diego.
Paper or resources for more information: https://vicuna.lmsys.org/
License: Apache License 2.0
Where to send questions or comments about the model: https://github.com/lm-sys/FastChat/issues
Primary intended uses: The primary use of Vicuna is research on large language models and chatbots.
Primary intended users: The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence.
70K conversations collected from ShareGPT.com.
A preliminary evaluation of the model quality is conducted by creating a set of 80 diverse questions and utilizing GPT-4 to judge the model outputs. See https://vicuna.lmsys.org/ for more details.
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Inference API has been turned off for this model.