Update README.md

README.md

@@ -25,34 +25,7 @@ inference: false
 
 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-3-44---
-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-3-150m-instruct2
-
-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-3-150m-instruct2** model.
+This repository provides **llm-jp-3-440m-instruct2** 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).
@@ -176,138 +149,6 @@ The models released here are in the early stages of our research and development
 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.0m-instruct2** 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-3-150m-instruct2")
-model = AutoModelForCausalLM.from_pretrained("llm-jp/llm-jp-3-150m-instruct2", 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.
-
-#### 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. |
-
-
-## 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)