---
library_name: transformers
license: mit
language:
- ja
- en
---

# stockmark/stockmark-100b-instruct-v0.1

Stockmark-100b-instruct-v0.1 is an instruction tuned version of [stockmark-100b](https://huggingface.co/stockmark/stockmark-100b), a 100 billion parameter LLM developed by [Stockmark Inc.](https://stockmark.co.jp/) 

## How to use

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

prompt_template = """### 指示:
{instruction}

### 応答:
"""

tokenizer = AutoTokenizer.from_pretrained("stockmark/stockmark-100b-instruct-v0.1")
model = AutoModelForCausalLM.from_pretrained("stockmark/stockmark-100b-instruct-v0.1", device_map="auto", torch_dtype=torch.bfloat16)

instruction = "生成AIとは？"
prompt = prompt_template.format(instruction=instruction)
input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(model.device)
with torch.inference_mode():
    tokens = model.generate(
        input_ids,
        max_new_tokens = 256,
        do_sample = True,
        temperature = 0.7,
        top_p = 0.95,
        repetition_penalty = 1.08
    )
    
output = tokenizer.decode(tokens[0], skip_special_tokens=True)
print(output)
```

## Dataset (fine-tuning)
- Ichikara instruction [[Web Page](https://liat-aip.sakura.ne.jp/wp/llm%E3%81%AE%E3%81%9F%E3%82%81%E3%81%AE%E6%97%A5%E6%9C%AC%E8%AA%9E%E3%82%A4%E3%83%B3%E3%82%B9%E3%83%88%E3%83%A9%E3%82%AF%E3%82%B7%E3%83%A7%E3%83%B3%E3%83%87%E3%83%BC%E3%82%BF%E4%BD%9C%E6%88%90/llm%E3%81%AE%E3%81%9F%E3%82%81%E3%81%AE%E6%97%A5%E6%9C%AC%E8%AA%9E%E3%82%A4%E3%83%B3%E3%82%B9%E3%83%88%E3%83%A9%E3%82%AF%E3%82%B7%E3%83%A7%E3%83%B3%E3%83%87%E3%83%BC%E3%82%BF-%E5%85%AC%E9%96%8B/)], [[Ppaer](https://www.anlp.jp/proceedings/annual_meeting/2024/pdf_dir/A6-3.pdf)]

## Performance

**Stockmark Business Questions**

Dataset: https://huggingface.co/datasets/stockmark/business-questions

| model | accuracy |
|:---:|:---:|
|stockmark-100b-instruct| 0.90 |
|stockmark-13b-instruct| 0.80 |
|GPT-3.5-turbo[^1]| 0.42 |

[^1]: 0613

**Japanese Vicuna QA Benchmark**

We exclud categories that require calculation and coding, and use remaining 60 questions for evaluation.

GitHub: https://github.com/ku-nlp/ja-vicuna-qa-benchmark

| model | average score |
|:---:|:---:|
|stockmark-100b-instruct| 5.97 |
|tokyotech-llm/Swallow-70b-instruct-hf| 5.59 |
|GPT-3.5 (text-davinci-003)| 5.08 |

**Inference speed**

| model | time [s] for genrating 100 characters in Japanese |
|:---:|:---:|
|stockmark-100b-instruct| 1.86 |
| gpt-3.5-turbo | 2.15 |
| gpt-4-turbo | 5.48 |
|tokyotech-llm/Swallow-70b-instruct-hf| 2.22 |

For local LLMs, we measured the inference time using AWS Inferentia2.

## License
[MIT](https://opensource.org/licenses/MIT)

## Developed by
[Stockmark Inc.](https://stockmark.co.jp/)