Create README.md

README.md

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+---
+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-3-3.7b-instruct3
+
+LLM-jp-3 is the series of 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/).
+
+This repository provides **llm-jp-3-3.7b-instruct3** model.
+For an overview of the LLM-jp-3 models across different parameter sizes, please refer to:
+  - [LLM-jp-3 Pre-trained Models](https://huggingface.co/collections/llm-jp/llm-jp-3-pre-trained-models-672c6096472b65839d76a1fa)
+  - [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-3-3.7b-instruct3")
+model = AutoModelForCausalLM.from_pretrained("llm-jp/llm-jp-3-3.7b-instruct3", device_map="auto", torch_dtype=torch.bfloat16)
+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:** 2.1T tokens
+
+|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
+
+### Post-training
+
+We have fine-tuned the pre-trained checkpoint with supervised fine-tuning and further aligned it with Direct Preference Optimization.
+
+#### Supervised Fine-tuning
+The datasets used for supervised fine-tuning are as follows:
+ 
+| 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. |
+|        |[AnswerCarefully (ver2.0)](https://huggingface.co/datasets/llm-jp/AnswerCarefully)| A manually constructed instruction dataset focusing on LLMs' safety. |
+|        |ichikara-instruction-format| A small subset of the ichikara-instruction dataset, edited 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-ja](https://huggingface.co/datasets/llm-jp/wizardlm8x22b-logical-math-coding-sft-ja)| A synthetic instruction dataset. |
+|        |[magpie-sft-v1.0](https://huggingface.co/datasets/llm-jp/magpie-sft-v1.0)| A synthetic instruction dataset we created. |
+|English|[Daring-Anteater](https://huggingface.co/datasets/nvidia/Daring-Anteater)| - | 
+|        |[FLAN](https://huggingface.co/datasets/llm-jp/FLAN/blob/main/README.md) | - |
+|Japanese & English|[Synthetic-JP-EN-Coding-Dataset](https://huggingface.co/datasets/llm-jp/Synthetic-JP-EN-Coding-Dataset)| A synthetic instruction dataset. |
+
+
+#### Direct Preference Optimization
+
+The datasets used for supervised fine-tuning are as follows:
+
+| Language | Dataset | Description |
+|:---|:---|:---|
+|Japanese|[aya-ja-evol-inst](https://huggingface.co/datasets/llm-jp/aya-ja-evol-inst) | A synthetic preference dataset focusing on LLMs' helpfulness. |
+|        |[ac-self-inst](https://huggingface.co/datasets/llm-jp/ac-self-inst)| A synthetic preference dataset focusing on LLMs' safety. |
+
+
+## Evaluation
+
+Detailed evaluation results are reported in this blog.
+
+
+## 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.