metadata

inference: false
license: other

Chat & support: TheBloke's Discord server

Want to contribute? TheBloke's Patreon page

TheBloke's LLM work is generously supported by a grant from andreessen horowitz (a16z)

Project Baize V2 13B GPTQ

These files are GPTQ 4bit model files for Project Baize V2 13B.

It is the result of quantising to 4bit using GPTQ-for-LLaMa.

Other repositories available

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 TheBloke/Project-Baize-v2-13B-GPTQ.
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, Project-Baize-v2-13B-GPTQ.
If you see an error in the bottom right, ignore it - it's temporary.
Fill out the GPTQ parameters on 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!

Provided files

Compatible file - Baize-v2-13B-4bit-128g.no-act-order.safetensors

In the main branch - the default one - you will find Baize-v2-13B-4bit-128g.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.

Baize-v2-13B-4bit-128g.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:
```
python llama.py /workspace/ggml/TheBloke_Project-Baize-v2-13B-GGML/HF  wikitext2 --wbits 4 --true-sequential --groupsize 128  --save_safetensors /workspace/ggml/TheBloke_Project-Baize-v2-13B-GGML/gptq/Baize-v2-13B-4bit-128g.no-act-order.safetensors
```

Discord

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

TheBloke AI's Discord server

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.

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.

Original model info on Github

News

[May 23, 2023] We are releasing Baize v2! Check out the 7B and 13B model. Code coming soon!
[Apr. 27, 2023] Fastchat now supports Baize. Try the new CLI and API!
[Apr. 21, 2023] We now have a script to merge LoRA weights into standard HF model so you can use it everywhere HF is supported!

What's Baize?

Baize is an open-source chat model trained with LoRA. It uses 100k dialogs generated by letting ChatGPT chat with itself. We also use Alpaca's data to improve its performance. We have released 7B, 13B and 30B models. Please refer to the paper for more details.

Why it's called Baize?

Baize (pronounced as By-zor; Simplified Chinese 白泽, Traditional Chinese 白澤, Japanese 白沢, はくたく) is a mythical creature in Chinese folklore, who speaks human languages and knows everything. This is exactly what we expect from a chat model.

Overview

⚠️ All model weights and data are for research use ONLY. Commercial use is strictly prohibited. We accept NO responsibility or liability for any use of our data, code or weights.

This is the repo for the Baize project, which aims to build a chat model with LLaMA. This repository contains:

54K/57K/47K dialogs from Quora, StackOverFlow and MedQuAD questions
The code for collecting self-chat data
The code for training Baize
The code for chat model demo (forked from ChuanhuChatGPT)

Model Release

V1

V2

Community Models and Data

Fauno is an Italian version of Baize.
Dutch Data: Baize data translated into Dutch.

CLI and API

Now you can use Baize with Fastchat for the CLI and API provided by Fastchat!

First, install the latest version of Fastchat:

pip install git+https://github.com/huggingface/peft.git
pip install git+https://github.com/lm-sys/FastChat.git

(For v1 models only): Merge Baize's LoRA weights into LLaMA. Take 7B checkpoint as an example.

# Note you have to include "baize" in the target directory so Fastchat can recognize Baize.
python3 -m fastchat.model.apply_lora --base huggyllama/llama-7b --target ./model_weights/baize-7b --lora project-baize/baize-lora-7B

Now, run the CLI in your terminal! More options and configs can be found here.

# Optional: Add `--style rich` for better style.
python -m fastchat.serve.cli --model-path ./model_weights/baize-7b

You can use Baize with OpenAI API or Hugging Face API following the instruction here.

Demo

You can either host it on your local machine or access the online demo. The demo fetches the LLaMA model and the LoRA weights from the Hugging Face model hub, then runs a user-friendly Gradio interface for chatting.

How to Run Locally

First, make sure your Python version is 3.8, and then install the required packages using the command below:

cd demo
pip install -r requirements.txt

You can host the model on your local machine using the following command:

# We assume you have obtained access to use LLaMA. The following LLaMA weights are from a 3rd party.
base_model=huggyllama/llama-7b
lora_model=project-baize/baize-lora-7B
python app.py $base_model $lora_model

GPU VRAM Requirements

	Inference (without int8)
Baize-7B	16GB
Baize-13B	28GB
Baize-30B	67GB

If you have a GPU with smaller VRAM, you can do inference with int8, by passing the 8bit argument:

python app.py $base_model $lora_model 8bit

How to Reproduce

Setup

Install dependencies

pip install -r requirements.txt

If bitsandbytes doesn't work, install it from source. Windows users can follow these instructions.

Data Collecting

You can use our released data or collect the data from ChatGPT using the following command:

num_process=10 # The number of processes to collect data
max_total_tokens=500000 # Set maximum numbers of tokens to collect data
api_key=xxxxxxxxxxxxxxxxx # Set your openai api key
for ((i=0; i<$num_process; i++))
do
    python collect.py $api_key $max_total_tokens $i $num_process stackoverflow &
    python collect.py $api_key $max_total_tokens $i $num_process quora &
    python collect.py $api_key $max_total_tokens $i $num_process medical &
done

After collecting data, you use the following command to preprocess data:

python preprocess.py stackoverflow
python preprocess.py quora
python preprocess.py medical

Use your own data

If there's a specific dataset you want to use as seeds for ChatGPT self-chatting, you can simply modify collect.py to load your own data.

Training

The fine-tuning code is designed to run on an A100-80G GPU. The finetune.py script accepts three parameters: foundation model size (i.e., 7B, 13B, or 30B), batch size, learning rate and datasets. Note the total batch size is fixed to 64 (can be modified here) and the batch size here is the per device batch size before gradient accumulation. Set it to a smaller value if you are training on a GPU with smaller VRAM.

# For the 7B model (takes about 9 hours)
python finetune.py 7b 32 0.0002 alpaca,stackoverflow,quora

# For the 13B model (takes about 16 hours)
python finetune.py 13b 16 0.0001 alpaca,stackoverflow,quora

# For the 30B model (takes about 36 hours)
python finetune.py 30b 8 0.00005 alpaca,stackoverflow,quora

GPU VRAM Consumption

With the settings ABOVE:

	Training (with int8)
Baize-7B	26GB
Baize-13B	25GB
Baize-30B	42GB

Got a question? See this issue.

Merge LoRA into LLaMA

Now you can easily merge the trained LoRA weights into a LLaMA model so you can use it with everything that supports standard Hugging Face API!

Here's an example for merging baize-lora-7B into LLaMA-7B.

python merge_lora.py \
--base huggyllama/llama-7b \
--target ~/model_weights/baize-7b \
--lora project-baize/baize-lora-7B

Citation

@article{xu2023baize,
  title={Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on Self-Chat Data},
  author={Xu, Canwen and Guo, Daya and Duan, Nan and McAuley, Julian},
  journal={arXiv preprint arXiv:2304.01196},
  year={2023}
}