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---
inference: false
---
# 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.
This model is 4bit GPTQ Version.
The size is smaller and the execution speed is faster, but the inference performance may be a little worse.
You need autoGPTQ library to use this model.
### sample code
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.
```
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]))
```
### Other 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 tuning after this.
* **Japanese benchmark**
- *We used [Stability-AI/lm-evaluation-harness + gptq patch](https://github.com/webbigdata-jp/lm-evaluation-harness) for evaluation.*
- *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 |
| :-- | :-- | :-- | :-- | :-- | :-- |
| weblab-10b-instruction-sft | 78.78 | 74.35 | 65.65 | 96.06 | 79.04 |
| weblab-10b | 66.38 | 65.86 | 54.19 | 84.49 | 60.98 |
| *weblab-10b-instruction-sft-GPTQ* | - | 74.53 | 41.70 | - | 72.69 |
|