llm-jp
/

llm-jp-3-7.2b

+---
+license: apache-2.0
+language:
+- en
+- ja
+programming_language:
+  - C
+  - C++
+  - C#
+  - Go
+  - Java
+  - JavaScript
+  - Lua
+  - PHP
+  - Python
+  - Ruby
+  - Rust
+  - Scala
+  - TypeScript
+pipeline_tag: text-generation
+library_name: transformers
+inference: false
+---
+# llm-jp-3-7.2b
+This repository provides large language models developed by the [Research and Development Center for Large Language Models](https://llmc.nii.ac.jp/) at the [National Institute of Informatics](https://www.nii.ac.jp/en/).
+For models with different parameters, please refer to [LLM-jp-3 Pre-trained Models](https://huggingface.co/collections/llm-jp/llm-jp-3-pre-trained-models-672c6096472b65839d76a1fa) and [LLM-jp-3 Fine-tuned Models](https://huggingface.co/collections/llm-jp/llm-jp-3-fine-tuned-models-672c621db852a01eae939731).
+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-3-7.2b")
+model = AutoModelForCausalLM.from_pretrained("llm-jp/llm-jp-3-7.2b", device_map="auto", torch_dtype=torch.bfloat16)
+text = "自然言語処理とは何か"
+tokenized_input = tokenizer.encode(text, add_special_tokens=False, 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:** 2.1T
+|Params|Layers|Hidden size|Heads|Context length|Embedding parameters|Non-embedding parameters|
+|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
+|150M|12|512|8|4096|101,874,688|50,344,448|
+|440M|16|1024|8|4096|203,749,376|243,303,424|
+|980M|20|1536|8|4096|305,624,064|684,258,816|
+|1.8b|24|2048|16|4096|407,498,752|1,459,718,144|
+|3.7b|28|3072|24|4096|611,248,128|3,171,068,928|
+|7.2b|32|4096|32|4096|814,997,504|6,476,271,616|
+|13b|40|5120|40|4096|1,018,746,880|12,688,184,320|
+|172b|96|12288|96|4096|2,444,992,512|169,947,181,056|
+## 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 v3.0`](https://github.com/llm-jp/llm-jp-tokenizer/releases/tag/v3.0b2).
+Please refer to [README.md](https://github.com/llm-jp/llm-jp-tokenizer) of `llm-jp-tokenizer` for details on the vocabulary construction procedure (the pure SentencePiece training does not reproduce our vocabulary).
+## Datasets
+### Pre-training
+The models have been pre-trained using a blend of the following datasets.
+| Language | Dataset | Tokens|
+|:---|:---|---:|
+|Japanese|[Wikipedia](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|2.6B
+||[Common Crawl](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|762.8B
+||[WARP/PDF](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|237.3B
+||[WARP/HTML](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|2.7B
+||[Kaken](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|1.8B
+|English|[Wikipedia](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|4.7B
+||[Dolma/CC-head](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|608.5B
+||[Dolma/C4](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|181.6B
+||[Dolma/Reddit](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|83.1B
+||[Dolma/PeS2o](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|62.9B
+||[Dolma/Gutenberg](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|5.5B
+||[Dolma/Wiki](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|3.9B
+|Code|[The Stack](https://huggingface.co/datasets/bigcode/the-stack)|114.1B
+|Chinese|[Wikipedia](https://huggingface.co/datasets/bigcode/the-stack)|0.8B
+|Korean|[Wikipedia](https://huggingface.co/datasets/bigcode/the-stack)|0.3B
+### Instruction tuning
+The models have been fine-tuned on the following datasets.
+| Language | Dataset | description |
+|:---|:---|:---|
+|Japanese|[ichikara-instruction-004-002](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 instruction dataset |
+|        |[answer-carefully-002](https://liat-aip.sakura.ne.jp/wp/answercarefully-dataset/)| A manually constructed instruction dataset focusing on LLMs' safety |
+|        |ichikara-instruction-format| A small amount of instruction dataset edited from ichikara-instruction, with some constraints on the output format. |
+|        |[AutoMultiTurnByCalm3-22B](https://huggingface.co/datasets/kanhatakeyama/AutoMultiTurnByCalm3-22B)| A synthetic instruction dataset. |
+|        |[ramdom-to-fixed-multiturn-Calm3](https://huggingface.co/datasets/kanhatakeyama/ramdom-to-fixed-multiturn-Calm3)| A synthetic instruction dataset. |
+|        |[wizardlm8x22b-logical-math-coding-sft_additional-ja](https://huggingface.co/datasets/kanhatakeyama/wizardlm8x22b-logical-math-coding-sft_additional-ja)| A synthetic instruction dataset. |
+|        |[Synthetic-JP-EN-Coding-Dataset-567k](https://huggingface.co/datasets/Aratako/Synthetic-JP-EN-Coding-Dataset-567k)| A synthetic instruction dataset. We used sampled one.|
+|English |[FLAN](https://huggingface.co/datasets/Open-Orca/FLAN) | We used sampled one. |
+## Evaluation
+### llm-jp-eval (v1.3.1)
+We evaluated the models using 100 examples from the dev split.
+| Model name | average | EL | FA | HE | MC | MR | MT | NLI | QA | RC |
+| :--- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
+| [llm-jp-3-1.8b](https://huggingface.co/llm-jp/llm-jp-3-1.8b) | 0.3767 | 0.3725 | 0.1948 | 0.2350 | 0.2500 | 0.0900 | 0.7730 | 0.3080 | 0.4629 | 0.7040 |
+| [llm-jp-3-1.8b-instruct](https://huggingface.co/llm-jp/llm-jp-3-1.8b-instruct) | 0.4596 | 0.4280 | 0.1987 | 0.3250 | 0.3300 | 0.4200 | 0.7900 | 0.3520 | 0.4698 | 0.8224 |
+| [llm-jp-3-3.7b](https://huggingface.co/llm-jp/llm-jp-3-3.7b) | 0.4231 | 0.3812 | 0.2440 | 0.2200 | 0.1900 | 0.3600 | 0.7947 | 0.3800 | 0.4688 | 0.7694 |
+| [llm-jp-3-3.7b-instruct](https://huggingface.co/llm-jp/llm-jp-3-3.7b-instruct) | 0.5188 | 0.4191 | 0.2504 | 0.3400 | 0.5000 | 0.5800 | 0.8166 | 0.4500 | 0.4881 | 0.8247 |
+| [llm-jp-3-7.2b](https://huggingface.co/llm-jp/llm-jp-3-7.2b) | 0.5057 | 0.4062 | 0.2678 | 0.3450 | 0.5800 | 0.4300 | 0.8083 | 0.3480 | 0.5528 | 0.8136 |
+| [llm-jp-3-7.2b-instruct](https://huggingface.co/llm-jp/llm-jp-3-7.2b-instruct) | 0.5888 | 0.4282 | 0.2659 | 0.4350 | 0.8900 | 0.5800 | 0.8250 | 0.4860 | 0.5565 | 0.8330 |
+| [llm-jp-3-13b](https://huggingface.co/llm-jp/llm-jp-3-13b) | 0.5802 | 0.5570 | 0.2593 | 0.4600 | 0.7000 | 0.6300 | 0.8292 | 0.3460 | 0.5937 | 0.8469 |
+| [llm-jp-3-13b-instruct](https://huggingface.co/llm-jp/llm-jp-3-13b-instruct) | 0.6168 | 0.5408 | 0.2757 | 0.4950 | 0.9200 | 0.7100 | 0.8317 | 0.4640 | 0.4642 | 0.8500 |
+### Japanese MT Bench
+We evaluated the models using `gpt-4-0613`. Please see the [codes](https://github.com/llm-jp/llm-leaderboard/tree/main) for details.
+| Model name | average | coding | extraction | humanities | math | reasoning | roleplay | stem | writing |
+| :--- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
+| [llm-jp-3-1.8b-instruct](https://huggingface.co/llm-jp/llm-jp-3-1.8b-instruct) | 4.93 | 1.50 | 4.70 | 7.80 | 1.55 | 2.60 | 7.80 | 6.10 | 7.40 |
+| [llm-jp-3-3.7b-instruct](https://huggingface.co/llm-jp/llm-jp-3-3.7b-instruct) | 5.50 | 1.95 | 4.05 | 8.25 | 2.25 | 4.00 | 8.80 | 7.25 | 7.45 |
+| [llm-jp-3-7.2b-instruct](https://huggingface.co/llm-jp/llm-jp-3-7.2b-instruct) | 5.70 | 2.95 | 5.60 | 7.95 | 2.80 | 3.90 | 8.40 | 6.15 | 7.85 |
+| [llm-jp-3-13b-instruct](https://huggingface.co/llm-jp/llm-jp-3-13b-instruct) | 6.47 | 3.15 | 7.05 | 9.15 | 3.75 | 5.40 | 8.30 | 7.50 | 7.45 |
+## Risks and Limitations
+The models released here are 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.*
+Hirokazu Kiyomaru and Takashi Kodama.