README.md

---
language:
  - en
tags:
  - causal-lm
  - llama
inference: false
---
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# Wizard-Vicuna-13B-GPTQ

This repo contains 4bit GPTQ format quantised models of  [junelee's wizard-vicuna 13B](https://huggingface.co/junelee/wizard-vicuna-13b).

It is the result of quantising to 4bit using [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa).

## Repositories available

* [4bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/wizard-vicuna-13B-GPTQ).
* [4bit and 5bit GGML models for CPU inference](https://huggingface.co/TheBloke/wizard-vicuna-13B-GGML).
* [float16 HF format model for GPU inference](https://huggingface.co/TheBloke/wizard-vicuna-13B-HF).

## How to easily download and use this model in text-generation-webui

Open the text-generation-webui UI as normal.

1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/wizard-vicuna-13B-GPTQ`.
3. Click **Download**.
4. Wait until it says it's finished downloading.
5. Click the **Refresh** icon next to **Model** in the top left.
6. In the **Model drop-down**: choose the model you just downloaded, `wizard-vicuna-13B-GPTQ`.
7. If you see an error in the bottom right, ignore it - it's temporary.
8. Fill out the `GPTQ parameters` on the right: `Bits = 4`, `Groupsize = 128`, `model_type = Llama`
9. Click **Save settings for this model** in the top right.
10. Click **Reload the Model** in the top right.
11. Once it says it's loaded, click the **Text Generation tab** and enter a prompt!

## Provided files

**Compatible file - wizard-vicuna-13B-GPTQ-4bit.compat.no-act-order.safetensors**

In the `main` branch - the default one - you will find `wizard-vicuna-13B-GPTQ-4bit.compat.no-act-order.safetensors`

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.

* `wizard-vicuna-13B-GPTQ-4bit.compat.no-act-order.safetensors`
  * 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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## Discord

For further support, and discussions on these models and AI in general, join us at:

[TheBloke AI's Discord server](https://discord.gg/theblokeai)

## Thanks, and how to contribute.

Thanks to the [chirper.ai](https://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.

* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI

**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.

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# Original WizardVicuna-13B model card

Github page: https://github.com/melodysdreamj/WizardVicunaLM

# 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.


## Benchmark
### Approximately 7% performance improvement over VicunaLM
![](https://user-images.githubusercontent.com/21379657/236088663-3fa212c9-0112-4d44-9b01-f16ea093cb67.png)


### Detail

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.

|     | gpt3.5 | wizard-vicuna-13b | vicuna-13b | wizard-7b | link     |
|-----|--------|-------------------|------------|-----------|----------|
| Q1  | 95     | 90                | 85         | 88        | [link](https://sharegpt.com/c/YdhIlby) |
| Q2  | 95     | 97                | 90         | 89        | [link](https://sharegpt.com/c/YOqOV4g) |
| Q3  | 85     | 90                | 80         | 65        | [link](https://sharegpt.com/c/uDmrcL9) |
| Q4  | 90     | 85                | 80         | 75        | [link](https://sharegpt.com/c/XBbK5MZ) |
| Q5  | 90     | 85                | 80         | 75        | [link](https://sharegpt.com/c/AQ5tgQX) |
| Q6  | 92     | 85                | 87         | 88        | [link](https://sharegpt.com/c/eVYwfIr) |
| Q7  | 95     | 90                | 85         | 92        | [link](https://sharegpt.com/c/Kqyeub4) |
| Q8  | 90     | 85                | 75         | 70        | [link](https://sharegpt.com/c/M0gIjMF) |
| Q9  | 92     | 85                | 70         | 60        | [link](https://sharegpt.com/c/fOvMtQt) |
| Q10 | 90     | 80                | 75         | 85        | [link](https://sharegpt.com/c/YYiCaUz) |
| Q11 | 90     | 85                | 75         | 65        | [link](https://sharegpt.com/c/HMkKKGU) |
| Q12 | 85     | 90                | 80         | 88        | [link](https://sharegpt.com/c/XbW6jgB) |
| Q13 | 90     | 95                | 88         | 85        | [link](https://sharegpt.com/c/JXZb7y6) |
| Q14 | 94     | 89                | 90         | 91        | [link](https://sharegpt.com/c/cTXH4IS) |
| Q15 | 90     | 85                | 88         | 87        | [link](https://sharegpt.com/c/GZiM0Yt) |
|     | 91     | 88                | 82         | 80        |          |


## Principle

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](https://sharegpt.com/c/6cmxqq0).

After creating the training data, I later trained it according to the Vicuna v1.1 [training method](https://github.com/lm-sys/FastChat/blob/main/scripts/train_vicuna_13b.sh).


## Detailed 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.

## Training Process

Trained with 8 A100 GPUs for 35 hours.

## Weights
You can see the [dataset](https://huggingface.co/datasets/junelee/wizard_vicuna_70k) we used for training and the [13b model](https://huggingface.co/junelee/wizard-vicuna-13b) in the huggingface.

## Conclusion
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.

## License
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.

## Author

[JUNE LEE](https://github.com/melodysdreamj) - He is active in Songdo Artificial Intelligence Study and GDG Songdo.