Update 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
 library_name: peft
+pipeline_tag: text-generation
+inference: false
 ---
-## Training procedure
+# llm-jp-13b-instruct-lora-jaster-dolly-oasst-v1.0
 
-### Framework versions
+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-jaster-v1.0](https://huggingface.co/llm-jp/llm-jp-13b-instruct-full-jaster-v1.0) |
+| [llm-jp-13b-instruct-full-jaster-dolly-oasst-v1.0](https://huggingface.co/llm-jp/llm-jp-13b-instruct-full-jaster-dolly-oasst-v1.0) |
+| [llm-jp-13b-instruct-full-dolly-oasst-v1.0](https://huggingface.co/llm-jp/llm-jp-13b-instruct-full-dolly-oasst-v1.0) |
+| [llm-jp-13b-instruct-lora-jaster-v1.0](https://huggingface.co/llm-jp/llm-jp-13b-instruct-lora-jaster-v1.0) | 
+| [llm-jp-13b-instruct-lora-jaster-dolly-oasst-v1.0](https://huggingface.co/llm-jp/llm-jp-13b-instruct-lora-jaster-dolly-oasst-v1.0) | 
+| [llm-jp-13b-instruct-lora-dolly-oasst-v1.0](https://huggingface.co/llm-jp/llm-jp-13b-instruct-lora-dolly-oasst-v1.0) | 
 
-- PEFT 0.5.0
+
+|  | 
+| :--- |
+|**Pre-trained models**|
+| [llm-jp-13b-v1.0](https://huggingface.co/llm-jp/llm-jp-13b-v1.0) | 
+| [llm-jp-1.3b-v1.0](https://huggingface.co/llm-jp/llm-jp-1.3b-v1.0) | 
+Checkpoints format: `transformers` (Megatron-DeepSpeed format available [here](https://huggingface.co/llm-jp/llm-jp-13b-v1.0-mdsfmt))
+
+
+## Required Libraries and Their Versions
+
+- torch>=2.0.0
+- transformers>=4.34.0
+- tokenizers>=0.14.0
+- peft==0.5.0
+
+## Usage
+
+```python
+import torch
+from peft import PeftModel, PeftConfig
+from transformers import AutoTokenizer, AutoModelForCausalLM
+peft_model_name = "llm-jp/llm-jp-13b-instruct-lora-jaster-dolly-oasst-v1.0"
+tokenizer = AutoTokenizer.from_pretrained(peft_model_name)
+config = PeftConfig.from_pretrained(peft_model_name)
+model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, torch_dtype=torch.float16)
+model = PeftModel.from_pretrained(model, peft_model_name)
+text = "自然言語処理とは何か"
+text = text + "### 回答："
+tokenized_input = tokenizer(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,
+    )[0]
+print(tokenizer.decode(output))
+```
+
+
+## Model Details
+
+- **Model type:** Transformer-based Language Model
+- **Total seen tokens:** 270B+
+
+|Model|Params|Layers|Hidden size|Heads|Context length|
+|:---:|:---:|:---:|:---:|:---:|:---:|
+|13b model|13b|40|5120|40|2048|
+|1.3b model|1.3b|24|2048|16|2048|
+
+
+## Training
+
+- **Pre-training:**
+  - **Hardware:** 96 A100 40GB GPUs ([mdx cluster](https://mdx.jp/en/))
+  - **Software:** Megatron-DeepSpeed
+
+- **Instruction tuning:**
+  - **Hardware:** 8 A100 40GB GPUs ([mdx cluster](https://mdx.jp/en/))
+  - **Software:** [TRL](https://github.com/huggingface/trl), [PEFT](https://github.com/huggingface/peft), 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 vocab entries were converted from [`llm-jp-tokenizer v2.1 (50k)`](https://github.com/llm-jp/llm-jp-tokenizer/releases/tag/v2.1).
+Please refer to [README.md](https://github.com/llm-jp/llm-jp-tokenizer) of `llm-ja-tokenizer` for the details of vocab constuction steps.
+- **Model:** Hugging Face Fast Tokenizer using Unigram byte-fallback model which requires `tokenizers>=0.14.0`
+- **Training algorithm:** SentencePiece Unigram byte-fallback
+- **Training data:** A subset of the datasets for model pre-training
+- **Vocabulary size:** 50,570 (mixed vocabulary of Japanese, English, and source code)
+
+
+## Datasets
+
+### Pre-training
+
+The models have been pre-trained on approximately 287.5B tokens, sourced from a blend of the following datasets.
+
+| Language | Dataset | Tokens|
+|:---:|:---:|:---:|
+|Japanese|[Wikipedia](https://huggingface.co/datasets/wikipedia)|1.5B
+||[mC4](https://huggingface.co/datasets/mc4)|136B
+|English|[Wikipedia](https://huggingface.co/datasets/wikipedia)|5B
+||[The Pile](https://huggingface.co/datasets/EleutherAI/pile)|135B
+|Codes|[The Stack](https://huggingface.co/datasets/bigcode/the-stack)|10B
+
+Pretraining was done by 10-hold shards that consists approx. 27-28B tokens. We further finalized the pretraining with additional cleaned 27B tokens data.
+
+### Instruction tuning
+
+The models have been fine-tuned on the following datasets.
+ 
+| Language | Dataset | description |
+|:---|:---:|:---:|
+|Japanese|[jaster](https://github.com/llm-jp/llm-jp-eval)| An automatically transformed data from the existing Japanese NLP datasets |
+||[databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k)| A translated one by DeepL in LLM-jp |
+||[OpenAssistant Conversations Dataset](https://huggingface.co/datasets/OpenAssistant/oasst1)| A translated one by DeepL in LLM-jp |
+
+
+## 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) for the evaluation.
+
+## 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, Hirokazu Kiyomaru, Hiroshi Matsuda, Shota Sasaki, Shuhei Kurita,　Taishi Nakamura, Takumi Okamoto.