---
license: other
---
# Koala: A Dialogue Model for Academic Research
This repo contains the weights of the Koala 7B model produced at Berkeley. It is the result of combining the diffs from https://huggingface.co/young-geng/koala with the original Llama 7B model.

This version has then been quantized to 4-bit using [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa).

## Other Koala repos

I have also made these other Koala repose available:
* [GPTQ quantized 4bit 13B model in HF format](https://huggingface.co/TheBloke/koala-13B-GPTQ-4bit-128g)
* [Unquantized 13B model in HF format](https://huggingface.co/TheBloke/koala-13B-HF)
* [Unquantized 7B model in HF format](https://huggingface.co/TheBloke/koala-7B-HF)
* [Unquantized 7B model in GGML format for llama.cpp](https://huggingface.co/TheBloke/koala-7b-ggml-unquantized)

## Quantization method

This GPTQ model was quantized using [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa) with the following command:
```
python3 llama.py /content/koala-7B-HF c4 --wbits 4 --true-sequential --act-order --groupsize 128 --save /content/koala-7B-4bit-128g.pt
```

I created this model using the latest Triton branch of GPTQ-for-LLaMa but I believe it can be run with the CUDA branch also.

## Provided files

I have provided both a `pt` and `safetensors` file. Either should work.

If both are present in the model directory for text-generation-webui I am not sure which it picks, so if you need one or the other specifically I'd recommend just downloading the one you need.

The `olderFormat` file was created with the aim of then converting it to GGML for use with llama.cpp.  At present this file does not work.

## How to run with text-generation-webui

The model files provided will not load as-is with [oobaboogas text-generation-webui](https://github.com/oobabooga/text-generation-webui).

They require the latest version of the GPTQ code.

Here are the commands I used to clone 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 run text-generation-webui as follows:
```
cd text-generation-webui
python server.py --model koala-7B-GPTQ-4bit-128g --wbits 4 --groupsize 128 --model_type Llama
```

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 cannot use the Triton branch for any reason, I believe it should also work to use the CUDA branch instead:
```
git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa -b cuda
```
Then link that into `text-generation-webui/repositories` as described above.

## How the Koala delta weights were merged

The Koala delta weights were originally merged using the following commands, producing [koala-7B-HF](https://huggingface.co/TheBloke/koala-7B-HF):
```
git clone https://github.com/young-geng/EasyLM

git clone https://huggingface.co/nyanko7/LLaMA-7B

git clone https://huggingface.co/young-geng/koala koala_diffs

cd EasyLM

PYTHON_PATH="${PWD}:$PYTHONPATH" python \
-m EasyLM.models.llama.convert_torch_to_easylm \
--checkpoint_dir=/content/LLaMA-7B \
--output_file=/content/llama-7B-LM \
--streaming=True

PYTHON_PATH="${PWD}:$PYTHONPATH" python \
-m EasyLM.scripts.diff_checkpoint --recover_diff=True \
--load_base_checkpoint='params::/content/llama-7B-LM' \
--load_target_checkpoint='params::/content/koala_diffs/koala_7b_diff_v2' \
--output_file=/content/koala_7b.diff.weights \
--streaming=True

PYTHON_PATH="${PWD}:$PYTHONPATH" python \
-m EasyLM.models.llama.convert_easylm_to_hf --model_size=7b \
--output_dir=/content/koala-7B-HF \
--load_checkpoint='params::/content/koala_7b.diff.weights' \
--tokenizer_path=/content/LLaMA-7B/tokenizer.model
```

## Further info

Check out the following links to learn more about the Berkeley Koala model.
* [Blog post](https://bair.berkeley.edu/blog/2023/04/03/koala/)
* [Online demo](https://koala.lmsys.org/)
* [EasyLM: training and serving framework on GitHub](https://github.com/young-geng/EasyLM)
* [Documentation for running Koala locally](https://github.com/young-geng/EasyLM/blob/main/docs/koala.md)

## License
The model weights are intended for academic research only, subject to the
[model License of LLaMA](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md),
[Terms of Use of the data generated by OpenAI](https://openai.com/policies/terms-of-use),
and [Privacy Practices of ShareGPT](https://chrome.google.com/webstore/detail/sharegpt-share-your-chatg/daiacboceoaocpibfodeljbdfacokfjb).
Any other usage of the model weights, including but not limited to commercial usage, is strictly prohibited.