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

# stockmark/stockmark-100b

Stockmark-100b is a 100 billion parameter LLM pretrained from scratch based on Japanese and English corpus of about 910 billion tokens. This model is developed by [Stockmark Inc.](https://stockmark.co.jp/)

Instruction tuned model:
- [stockmark-100b-instruct-v0.1](https://huggingface.co/stockmark/stockmark-100b-instruct-v0.1)

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 AutoTokenizer
from peft import AutoPeftModelForCausalLM

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

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

### 応答:
"""

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

input_ids = tokenizer("生成AIとは？", 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 (pretraining)

Stockmark-100b was trained using a total of about 910B tokens of Japanese and English text corpus. 

The detail of Japanese data is summarized in the below table. The stockmark web corpus consists of web pages related to business, which are collected by Stockmark Inc.

| corpus | tokens after preprocessing |
|:---:|:---:|
| Stockmark Web Corpus (This dataset will not be released) | 8.8 billion |
| Patent | 37.5 billion |
| Wikipedia |1.5 billion |
| mC4 | 52.6 billion |
| CommonCrawl (snapshot: 2020-50 ~ 2024-10) | 203.7 billion|

English data is sampled from [RedPajama-Data](https://github.com/togethercomputer/RedPajama-Data/tree/rp_v1).

## Training

- GPU: 48 nodes of a3 (8*H100) instances
- Training duration: about 7 weeks
- Container: [Pytorch NGC Container](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch)
- Library: [Megatron-LM](https://github.com/NVIDIA/Megatron-LM)

## 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 excluded 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/)