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

# Stockmark-2-100B-Instruct-beta

![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/607ef1c3e758c3c5a2959eab/AbyPvKu-FBY6RDYhGi1KX.jpeg)

## Model description

**Stockmark-2-100B-Instruct-beta** is a 100-billion-parameter large language model built from scratch, with a particular focus on Japanese. It was pre-trained on approximately 1.5 trillion tokens of data, consisting of 60% English, 30% Japanese, and 10% code. Following pretraining, the model underwent post-training with synthetic data in Japanese to enhance its ability to follow instructions. This synthetic data was generated using Qwen2.5-32B-Instruct.

As a beta release, Stockmark-2-100b-Instruct-beta is still undergoing improvements and evaluations. Feedback and insights from users will help refine future versions.

See [our blog](https://stockmark-tech.hatenablog.com/entry/2025/03/06/114203) for the detail.

This project is supported by [GENIAC](https://www.meti.go.jp/policy/mono_info_service/geniac/index.html).

## How to use

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

tokenizer = AutoTokenizer.from_pretrained("stockmark/Stockmark-2-100B-Instruct-beta")
model = AutoModelForCausalLM.from_pretrained(
    "stockmark/Stockmark-2-100B-Instruct-beta", device_map="auto", torch_dtype=torch.bfloat16
)

instruction = "自然言語処理とは？"
input_ids = tokenizer.apply_chat_template(
    [{"role": "user", "content": instruction}], add_generation_prompt=True, return_tensors="pt"
).to(model.device)

with torch.inference_mode():
    tokens = model.generate(
        input_ids,
        max_new_tokens = 512,
        do_sample = True,
        temperature = 0.7,
        top_p = 0.95,
        repetition_penalty = 1.05
    )
    
output = tokenizer.decode(tokens[0], skip_special_tokens=True)
print(output)
```

## License

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

## Developed by

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

## Author

Takahiro Omi