---
license: apache-2.0
language:
  - en
  - ja
programming_language:
  - C
  - C++
  - C#
  - Go
  - Java
  - JavaScript
  - Lua
  - PHP
  - Python
  - Ruby
  - Rust
  - Scala
  - TypeScript
library_name: transformers
pipeline_tag: text-generation
inference: false
datasets:
- databricks/databricks-dolly-15k
- llm-jp/databricks-dolly-15k-ja
- llm-jp/oasst1-21k-en
- llm-jp/oasst1-21k-ja
- llm-jp/oasst2-33k-en
- llm-jp/oasst2-33k-ja
---
# llm-jp-13b-instruct-full-ac_001-dolly-ichikara_004_001_single-oasst-oasst2-v2.0

This repository provides large language models developed by [LLM-jp](https://llm-jp.nii.ac.jp/), a collaborative project launched in Japan.

| Model Variant | 
| :--- |
|**Instruction models**|
| [llm-jp-13b-instruct-full-dolly-ichikara_004_001_single-oasst-oasst2-v2.0](https://huggingface.co./llm-jp/llm-jp-13b-instruct-full-dolly-ichikara_004_001_single-oasst-oasst2-v2.0) |
| [llm-jp-13b-instruct-full-ac_001-dolly-ichikara_004_001_single-oasst-oasst2-v2.0](https://huggingface.co./llm-jp/llm-jp-13b-instruct-full-ac_001-dolly-ichikara_004_001_single-oasst-oasst2-v2.0) |
| [llm-jp-13b-instruct-full-ac_001_16x-dolly-ichikara_004_001_single-oasst-oasst2-v2.0](https://huggingface.co./llm-jp/llm-jp-13b-instruct-full-ac_001_16x-dolly-ichikara_004_001_single-oasst-oasst2-v2.0) |


|  | 
| :--- |
|**Pre-trained models**|
| [llm-jp-13b-v2.0](https://huggingface.co./llm-jp/llm-jp-13b-v2.0) | 

Checkpoints format: Hugging Face Transformers


## Required Libraries and Their Versions

- torch>=2.3.0
- transformers>=4.40.1
- tokenizers>=0.19.1
- accelerate>=0.29.3
- flash-attn>=2.5.8

## Usage

```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("llm-jp/llm-jp-13b-instruct-full-ac_001-dolly-ichikara_004_001_single-oasst-oasst2-v2.0")
model = AutoModelForCausalLM.from_pretrained("llm-jp/llm-jp-13b-instruct-full-ac_001-dolly-ichikara_004_001_single-oasst-oasst2-v2.0", device_map="auto", torch_dtype=torch.float16)
chat = [
    {"role": "system", "content": "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。"},
    {"role": "user", "content": "自然言語処理とは何か"},
]
tokenized_input = tokenizer.apply_chat_template(chat, add_generation_prompt=True, tokenize=True, return_tensors="pt").to(model.device)
with torch.no_grad():
    output = model.generate(
        tokenized_input,
        max_new_tokens=100,
        do_sample=True,
        top_p=0.95,
        temperature=0.7,
        repetition_penalty=1.05,
    )[0]
print(tokenizer.decode(output))
```


## Model Details

- **Model type:** Transformer-based Language Model
- **Total seen tokens:** 256B

|Model|Params|Layers|Hidden size|Heads|Context length|
|:---:|:---:|:---:|:---:|:---:|:---:|
|13b model|13b|40|5120|40|4096|


## Training

- **Pre-training:**
  - **Hardware:** 128 A100 40GB GPUs ([mdx cluster](https://mdx.jp/en/))
  - **Software:** Megatron-LM

- **Instruction tuning:**
  - **Hardware:** 8 A100 40GB GPUs ([mdx cluster](https://mdx.jp/en/))
  - **Software:** [TRL](https://github.com/huggingface/trl) and [DeepSpeed](https://github.com/microsoft/DeepSpeed)

## Tokenizer

The tokenizer of this model is based on [huggingface/tokenizers](https://github.com/huggingface/tokenizers) Unigram byte-fallback model.
The vocabulary entries were converted from [`llm-jp-tokenizer v2.2 (100k: code20K_en40K_ja60K.ver2.2)`](https://github.com/llm-jp/llm-jp-tokenizer/releases/tag/v2.2).
Please refer to [README.md](https://github.com/llm-jp/llm-jp-tokenizer) of `llm-ja-tokenizer` for details on the vocabulary construction procedure (the pure SentencePiece training does not reproduce our vocabulary).

- **Model:** Hugging Face Fast Tokenizer using Unigram byte-fallback model which requires `tokenizers>=0.14.0`
- **Training algorithm:** Marging Code/English/Japanese vocabularies constructed with SentencePiece Unigram byte-fallback and reestimating scores with the EM-algorithm.
- **Training data:** A subset of the datasets for model pre-training
- **Vocabulary size:** 96,867 (mixed vocabulary of Japanese, English, and source code)
  - The acutal size of vocabulary in the pretrained model is 97,024 due to round-up to multiples of 256.


## Datasets

### Pre-training

The models have been pre-trained using a blend of the following datasets.

| Language | Dataset | Tokens|
|:---|:---|---:|
|Japanese|[Wikipedia](https://huggingface.co./datasets/wikipedia)|1.4B
||[Common Crawl](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v2)|130.7B
|English|[Wikipedia](https://huggingface.co./datasets/wikipedia)|4.7B
||[The Pile](https://huggingface.co./datasets/EleutherAI/pile)|110.3B
|Codes|[The Stack](https://huggingface.co./datasets/bigcode/the-stack)|8.7B

### Instruction tuning

The models have been fine-tuned on the following datasets.
 
| Language | Dataset | description |
|:---|:---|:---|
|Japanese|[ichikara-instruction-004-001](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/)| A manually constructed Japanese instruction dataset |
|        |[answer-carefully-001](https://liat-aip.sakura.ne.jp/wp/answercarefully-dataset/)| A manually constructed Japanese instruction dataset focusing on LLMs' safety |
|        |[databricks-dolly-15k-ja](https://huggingface.co./datasets/llm-jp/databricks-dolly-15k-ja)| [databricks-dolly-15k](https://huggingface.co./datasets/databricks/databricks-dolly-15k) translated into Japanese using DeepL  |
|        |[oasst1-21k-ja](https://huggingface.co./datasets/llm-jp/oasst1-21k-ja)| A subset of [oasst1](https://huggingface.co./datasets/OpenAssistant/oasst1) translated into Japanese using DeepL |
|        |[oasst2-33k-ja](https://huggingface.co./datasets/llm-jp/oasst2-33k-ja)| A subset of [oasst2](https://huggingface.co./datasets/OpenAssistant/oasst2) translated into Japanese using DeepL |
|English |[databricks-dolly-15k](https://huggingface.co./datasets/databricks/databricks-dolly-15k) | - | 
|        |[oasst1-21k-en](https://huggingface.co./datasets/llm-jp/oasst1-21k-en)| A subset of [oasst1](https://huggingface.co./datasets/OpenAssistant/oasst1) |
|        |[oasst2-33k-en](https://huggingface.co./datasets/llm-jp/oasst2-33k-en)| A subset of [oasst2](https://huggingface.co./datasets/OpenAssistant/oasst2) |

## Evaluation

You can view the evaluation results of several LLMs on this [leaderboard](http://wandb.me/llm-jp-leaderboard). We used [llm-jp-eval](https://github.com/llm-jp/llm-jp-eval) (v1.3.0) for the evaluation.

Besides, we used LLM-as-a-judge frameworks, [Japanese Vicuna QA Benchmark](https://github.com/ku-nlp/ja-vicuna-qa-benchmark/) and [Japanese MT Bench](https://github.com/Stability-AI/FastChat/tree/jp-stable/fastchat/llm_judge), for evaluation.
For details, please refer to [our technical blog](https://llm-jp.nii.ac.jp/blog/2024/04/30/v2.0-release.html) (in Japanese).

## Risks and Limitations

The models released here are still in the early stages of our research and development and have not been tuned to ensure outputs align with human intent and safety considerations.


## Send Questions to

llm-jp(at)nii.ac.jp


## License

[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)


## Model Card Authors

*The names are listed in alphabetical order.*

Namgi Han, Tatsuya Hiraoka, Hirokazu Kiyomaru, Takashi Kodama, and Hiroshi Matsuda.