This repo contains 4bit GPTQ format quantised models of junelee's wizard-vicuna 13B.
It is the result of quantising to 4bit using GPTQ-for-LLaMa.
- 4bit GPTQ models for GPU inference.
- 4bit and 5bit GGML models for CPU inference.
- float16 HF format model for GPU inference.
How to easily download and use this model in text-generation-webui
Open the text-generation-webui UI as normal.
- Click the Model tab.
- Under Download custom model or LoRA, enter
- Click Download.
- Wait until it says it's finished downloading.
- Click the Refresh icon next to Model in the top left.
- In the Model drop-down: choose the model you just downloaded,
- If you see an error in the bottom right, ignore it - it's temporary.
- Fill out the
GPTQ parameterson the right:
Bits = 4,
Groupsize = 128,
model_type = Llama
- Click Save settings for this model in the top right.
- Click Reload the Model in the top right.
- Once it says it's loaded, click the Text Generation tab and enter a prompt!
Compatible file - wizard-vicuna-13B-GPTQ-4bit.compat.no-act-order.safetensors
main branch - the default one - you will find
This will work with all versions of GPTQ-for-LLaMa. It has maximum compatibility
It was created without the
--act-order parameter. It may have slightly lower inference quality compared to the other file, but is guaranteed to work on all versions of GPTQ-for-LLaMa and text-generation-webui.
- Works with all versions of GPTQ-for-LLaMa code, both Triton and CUDA branches
- Works with text-generation-webui one-click-installers
- Parameters: Groupsize = 128g. No act-order.
- Command used to create the GPTQ:
CUDA_VISIBLE_DEVICES=0 python3 llama.py wizard-vicuna-13B-HF c4 --wbits 4 --true-sequential --groupsize 128 --save_safetensors wizard-vicuna-13B-GPTQ-4bit.compat.no-act-order.safetensors
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Thanks, and how to contribute.
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.
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Patreon special mentions: Aemon Algiz, Dmitriy Samsonov, Nathan LeClaire, Trenton Dambrowitz, Mano Prime, David Flickinger, vamX, Nikolai Manek, senxiiz, Khalefa Al-Ahmad, Illia Dulskyi, Jonathan Leane, Talal Aujan, V. Lukas, Joseph William Delisle, Pyrater, Oscar Rangel, Lone Striker, Luke Pendergrass, Eugene Pentland, Sebastain Graf, Johann-Peter Hartman.
Thank you to all my generous patrons and donaters!
Original WizardVicuna-13B model card
Github page: https://github.com/melodysdreamj/WizardVicunaLM
Wizard's dataset + ChatGPT's conversation extension + Vicuna's tuning method
I am a big fan of the ideas behind WizardLM and VicunaLM. I particularly like the idea of WizardLM handling the dataset itself more deeply and broadly, as well as VicunaLM overcoming the limitations of single-turn conversations by introducing multi-round conversations. As a result, I combined these two ideas to create WizardVicunaLM. This project is highly experimental and designed for proof of concept, not for actual usage.
Approximately 7% performance improvement over VicunaLM
The questions presented here are not from rigorous tests, but rather, I asked a few questions and requested GPT-4 to score them. The models compared were ChatGPT 3.5, WizardVicunaLM, VicunaLM, and WizardLM, in that order.
We adopted the approach of WizardLM, which is to extend a single problem more in-depth. However, instead of using individual instructions, we expanded it using Vicuna's conversation format and applied Vicuna's fine-tuning techniques.
Turning a single command into a rich conversation is what we've done here.
After creating the training data, I later trained it according to the Vicuna v1.1 training method.
First, we explore and expand various areas in the same topic using the 7K conversations created by WizardLM. However, we made it in a continuous conversation format instead of the instruction format. That is, it starts with WizardLM's instruction, and then expands into various areas in one conversation using ChatGPT 3.5.
After that, we applied the following model using Vicuna's fine-tuning format.
Trained with 8 A100 GPUs for 35 hours.
You can see the dataset we used for training and the 13b model in the huggingface.
If we extend the conversation to gpt4 32K, we can expect a dramatic improvement, as we can generate 8x more, more accurate and richer conversations.
The model is licensed under the LLaMA model, and the dataset is licensed under the terms of OpenAI because it uses ChatGPT. Everything else is free.
JUNE LEE - He is active in Songdo Artificial Intelligence Study and GDG Songdo.
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Inference API has been turned off for this model.