---
license: cc-by-nc-4.0
inference: false
language:
  - ja
---
# weblab-10b-instruction-sft-GPTQ 

Original model [weblab-10b-instruction-sft](https://huggingface.co/matsuo-lab/weblab-10b-instruction-sft) which is a Japanese-centric multilingual GPT-NeoX model of 10 billion parameters created by matsuo-lab
 Takeshi Kojima.

This model is a quantized(miniaturized) version of the original model(21.42GB).

There are currently two well-known quantization version of original model.  
(1)GPTQ version(This model. 6.3 GB)  
The size is smaller and the execution speed is faster, but the inference performance may be a little worse than original model.  
At least one GPU is currently required due to a limitation of the Accelerate library.  
So this model cannot be run with the huggingface space free version.  
You need autoGPTQ library to use this model.  

(2)llama.cpp version(gguf)([matsuolab-weblab-10b-instruction-sft-gguf](https://huggingface.co/mmnga/matsuolab-weblab-10b-instruction-sft-gguf) 6.03GB)  
created by mmnga.  
You can use gguf model with llama.cpp at cpu only machine.  
But maybe gguf model little bit slower then GPTQ especialy long text.

# How to run.

## Local PC

You can use [text-generation-webui](https://github.com/oobabooga/text-generation-webui) to run this model fast(about 16 tokens/s on my RTX 3060) on your local PC.

![text-generation-webui-sample](./text-generation-webui-sample.png "text-generation-webui")

The explanation of [how to install text-generation-webui in  Japanese is here.](https://webbigdata.jp/post-19926/).

### colab with GUI

You can try this model interactively in the free version of Colab.  
[weblab-10b-instruction-sft-GPTQ-text-generation-webui-colab](https://github.com/webbigdata-jp/python_sample/blob/main/weblab_10b_instruction_sft_GPTQ_text_generation_webui_colab.ipynb)  

![text-generation-webui-sample](./text-generation-webui-colab-sample.png "text-generation-webui-colab")

### colab simple sample code

Currently, models may behave differently on local PC and Colab. On Colab, the model may not respond if you include instructional prompts.  
[Colab Sample script](https://github.com/webbigdata-jp/python_sample/blob/main/weblab_10b_instruction_sft_GPTQ_sample.ipynb)  

If you get an error (something not found or something is not defined) in the script below, please refer to the official documentation and Colab samples and specify a specific version.

```
pip install auto-gptq
```

```
from transformers import AutoTokenizer
from auto_gptq import AutoGPTQForCausalLM

quantized_model_dir = "dahara1/weblab-10b-instruction-sft-GPTQ"
model_basename = "gptq_model-4bit-128g"

tokenizer = AutoTokenizer.from_pretrained(quantized_model_dir)

model = AutoGPTQForCausalLM.from_quantized(
        quantized_model_dir,
        model_basename=model_basename,
        use_safetensors=True,
        device="cuda:0")


prompt_text = "スタジオジブリの作品を５つ教えてください"
prompt_template = f'以下は、タスクを説明する指示です。要求を適切に満たす応答を書きなさい。\n\n### 指示:\n{prompt_text}\n\n### 応答:'

tokens = tokenizer(prompt_template, return_tensors="pt").to("cuda:0").input_ids
output = model.generate(input_ids=tokens, max_new_tokens=100, do_sample=True, temperature=0.8)
print(tokenizer.decode(output[0]))
```

### How to make finetune data(LoRA)

There is a LoRA finetune code sample in finetune_sample directory.  

### Other AutoGPTQ documents
https://github.com/PanQiWei/AutoGPTQ/blob/main/docs/tutorial/01-Quick-Start.md

### Benchmark

The results below are preliminary. The blank part is under measurement.  
Also, the score may change as a result of more tuning.

* **Japanese benchmark**

    - *We used [Stability-AI/lm-evaluation-harness + gakada's AutoGPTQ PR](https://github.com/webbigdata-jp/lm-evaluation-harness) for evaluation. ([Stability-AI/lm-evaluation-harness](https://github.com/Stability-AI/lm-evaluation-harness/tree/jp-stable) + [gakada's AutoGPTQ PR](https://github.com/EleutherAI/lm-evaluation-harness/pull/519))*
    - *The 4-task average accuracy is based on results of JCommonsenseQA-1.1, JNLI-1.1, MARC-ja-1.1, and JSQuAD-1.1.*
    - *model loading is performed with gptq_use_triton=True, and evaluation is performed with template version 0.3 using the few-shot in-context learning.*
    - *The number of few-shots is 3,3,3,2.*
   
    | Model | Average | JCommonsenseQA | JNLI | MARC-ja | JSQuAD | model |   
    | :-- | :-- | :-- | :-- | :-- | :-- | :-- |
    | weblab-10b | 66.38 | 65.86 | 54.19 | 84.49 | 60.98 | [original model](https://huggingface.co/matsuo-lab/weblab-10b) |
    | weblab-10b-instruction-sft | 78.78 | 74.35 | 65.65 | 96.06 | 79.04 | [original instruction model](https://huggingface.co/matsuo-lab/weblab-10b-instruction-sft) |
    | *weblab-10b-instruction-sft-GPTQ first tuning* | 69.72 | 74.53 | 41.70 | 89.95 | 72.69 | deleted |
    | *weblab-10b-instruction-sft-GPTQ second tuning* | 74.59 | 74.08 | 60.72 | 91.85 | 71.70 | deleted |
    | *weblab-10b-instruction-sft-GPTQ third tuning* | 77.62 | 73.19 | 69.26 | 95.91 | 72.10 | current model. replaced on August 26th |
    | *weblab-10b-instruction-sft-GPTQ 4th tuning* | - | - | 14.5 | 85.46 |  | abandoned |


  
## about this work
- **This Quantization work was done by :** [webbigdata](https://webbigdata.jp/).
- [related documentation like fine-turning in Japanesse is here.](https://webbigdata.jp/post-20104/)