README.md

---
license: other
inference: false
---

# OpenAssistant LLaMA 30B SFT 7 GPTQ

This in a repo of GGML format models for [OpenAssistant's LLaMA 30B SFT 7](https://huggingface.co/OpenAssistant/oasst-sft-7-llama-30b-xor).

It is the result of merging the XORs from the above repo with the original Llama 30B weights, and then quantising to 4bit and 5bit GGML for CPU inference using [llama.cpp](https://github.com/ggerganov/llama.cpp).

This is epoch 7 of OpenAssistant's training of their Llama 30B model.

## Repositories available

* [4bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/OpenAssistant-SFT-7-Llama-30B-GPTQ).
* [4bit and 5bit GGML models for CPU inference](https://huggingface.co/TheBloke/OpenAssistant-SFT-7-Llama-30B-GGML).
* [Unquantised 16bit model in HF format](https://huggingface.co/TheBloke/OpenAssistant-SFT-7-Llama-30B-HF).

## PROMPT TEMPLATE

This model requires the following prompt template:

```
<|prompter|> prompt goes here
<|assistant|>:
```
## How to easily download and use this model in text-generation-webui

Load text-generation-webui as you normally do.

1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter this repo name: `TheBloke/stable-vicuna-13B-GPTQ`.
3. Click **Download**.
4. Wait until it says it's finished downloading.
5. As this is a GPTQ model, fill in the `GPTQ parameters` on the right: `Bits = 4`, `Groupsize = 128`, `model_type = Llama`
6. Now click the **Refresh** icon next to **Model** in the top left.
7. In the **Model drop-down**: choose this model: `stable-vicuna-13B-GPTQ`.
8. Click **Reload the Model** in the top right.
9. Once it says it's loaded, click the **Text Generation tab** and enter a prompt!

## Provided files

I have uploaded two versions of the GPTQ.

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

In the `main` branch - the default one - you will find `stable-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.

* `stable-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 stable-vicuna-13B-HF c4 --wbits 4 --true-sequential --groupsize 128 --save_safetensors stable-vicuna-13B-GPTQ-4bit.no-act-order.safetensors
    ```

**Latest file - stable-vicuna-13B-GPTQ-4bit.latest.act-order.safetensors**

Created for more recent versions of GPTQ-for-LLaMa, and uses the `--act-order` flag for maximum theoretical performance.

To access this file, please switch to the `latest` branch fo this repo and download from there.

* `stable-vicuna-13B-GPTQ-4bit.latest.act-order.safetensors`
  * Only works with recent GPTQ-for-LLaMa code
  * **Does not** work with text-generation-webui one-click-installers
  * Parameters: Groupsize = 128g. **act-order**.
  * Offers highest quality quantisation, but requires recent GPTQ-for-LLaMa code
  * Command used to create the GPTQ:
    ```
    CUDA_VISIBLE_DEVICES=0 python3 llama.py stable-vicuna-13B-HF c4 --wbits 4 --true-sequential --act-order --groupsize 128 --save_safetensors stable-vicuna-13B-GPTQ-4bit.act-order.safetensors
    ```
 
## Manual instructions for `text-generation-webui`

File `stable-vicuna-13B-GPTQ-4bit.compat.no-act-order.safetensors` can be loaded the same as any other GPTQ file, without requiring any updates to [oobaboogas text-generation-webui](https://github.com/oobabooga/text-generation-webui).

[Instructions on using GPTQ 4bit files in text-generation-webui are here](https://github.com/oobabooga/text-generation-webui/wiki/GPTQ-models-\(4-bit-mode\)).

The other `safetensors` model file was created using `--act-order` to give the maximum possible quantisation quality, but this means it requires that the latest GPTQ-for-LLaMa is used inside the UI.

If you want to use the act-order `safetensors` files and need to update the Triton branch of GPTQ-for-LLaMa, 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:
```
# Clone text-generation-webui, if you don't already have it
git clone https://github.com/oobabooga/text-generation-webui
# Make a repositories directory
mkdir text-generation-webui/repositories
cd text-generation-webui/repositories
# Clone the latest GPTQ-for-LLaMa code inside text-generation-webui
git clone https://github.com/qwopqwop200/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 stable-vicuna-13B-GPTQ --wbits 4 --groupsize 128 --model_type Llama # 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 can't update GPTQ-for-LLaMa or don't want to, you can use `stable-vicuna-13B-GPTQ-4bit.no-act-order.safetensors` as mentioned above, which should work without any upgrades to text-generation-webui.

# Original model card

```
llama-30b-sft-7:
  dtype: fp16
  log_dir: "llama_log_30b"
  learning_rate: 1e-5
  model_name: /home/ubuntu/Open-Assistant/model/model_training/.saved/llama-30b-super-pretrain/checkpoint-3500
  #model_name: OpenAssistant/llama-30b-super-pretrain
  output_dir: llama_model_30b
  deepspeed_config: configs/zero3_config_sft.json
  weight_decay: 0.0
  residual_dropout: 0.0
  max_length: 2048
  use_flash_attention: true
  warmup_steps: 20
  gradient_checkpointing: true
  gradient_accumulation_steps: 12
  per_device_train_batch_size: 2
  per_device_eval_batch_size: 3
  eval_steps: 101
  save_steps: 485
  num_train_epochs: 4
  save_total_limit: 3
  use_custom_sampler: true
  sort_by_length: false
  #save_strategy: steps
  save_strategy: epoch
  datasets:
    - oasst_export:
        lang: "bg,ca,cs,da,de,en,es,fr,hr,hu,it,nl,pl,pt,ro,ru,sl,sr,sv,uk"
        input_file_path: 2023-04-12_oasst_release_ready_synth.jsonl.gz
        val_split: 0.05
    - vicuna:
        val_split: 0.05
        max_val_set: 800
        fraction: 1.0
    - dolly15k:
        val_split: 0.05
        max_val_set: 300
    - grade_school_math_instructions:
        val_split: 0.05
    - code_alpaca:
        val_split: 0.05
        max_val_set: 250
```

- **OASST dataset paper:** https://arxiv.org/abs/2304.07327