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: transformers
+pipeline_tag: text-generation
+inference: false
 ---
+# llm-jp-13b-instruct-lora-dolly_en-dolly_ja-ichikara_003_001-oasst_en-oasst_ja-v1.1
+
+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_en-dolly_ja-ichikara_003_001-oasst_en-oasst_ja-v1.1](https://huggingface.co/llm-jp/llm-jp-13b-instruct-full-dolly_en-dolly_ja-ichikara_003_001-oasst_en-oasst_ja-v1.1) |
+| [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-dolly_en-dolly_ja-ichikara_003_001-oasst_en-oasst_ja-v1.1](https://huggingface.co/llm-jp/llm-jp-13b-instruct-lora-dolly_en-dolly_ja-ichikara_003_001-oasst_en-oasst_ja-v1.1) |
+| [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) | 
+
+
+|  | 
+| :--- |
+|**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: Hugging Face Transformers (Megatron-DeepSpeed format models are 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
+- accelerate==0.23.0
+
+## Usage
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+tokenizer = AutoTokenizer.from_pretrained("llm-jp/llm-jp-13b-instruct-full-jaster-dolly-oasst-v1.0")
+model = AutoModelForCausalLM.from_pretrained("llm-jp/llm-jp-13b-instruct-full-jaster-dolly-oasst-v1.0", device_map="auto", torch_dtype=torch.float16)
+text = "自然言語処理とは何か"
+text = text + "### 回答："
+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,
+    )[0]
+print(tokenizer.decode(output))
+```
+
+
+## Model Details
+
+- **Model type:** Transformer-based Language Model
+- **Total seen tokens:** 300B
+
+|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 vocabulary 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 details on the vocabulary construction procedure.
+- **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 using 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
+
+The pre-training was continuously conducted using a total of 10 folds of non-overlapping data, each consisting of approximately 27-28B tokens.
+We finalized the pre-training with additional (potentially) high-quality 27B tokens data obtained from the identical source datasets listed above used for the 10-fold 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 |
+|English|[databricks-dolly-15k-en](https://huggingface.co/datasets/llm-jp/databricks-dolly-15k-en)| Identical to [databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) |
+|Japanese|[databricks-dolly-15k-ja](https://huggingface.co/datasets/llm-jp/databricks-dolly-15k-ja)| A translated one by DeepL in LLM-jp |
+|English|[oasst1-21k-en](https://huggingface.co/datasets/llm-jp/oasst1-21k-en)| English subset of [oasst1 dataset](https://huggingface.co/datasets/OpenAssistant/oasst1) |
+|Japanese|[oasst1-21k-ja](https://huggingface.co/datasets/llm-jp/oasst1-21k-ja)| A translated one by DeepL in LLM-jp |
+|Japanese|[ichikara_003_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/)| ichikara-instruction dataset (ver.003-001)
+
+
+## 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.*
+
+Hirokazu Kiyomaru, Hiroshi Matsuda, Jun Suzuki, Namgi Han, Saku Sugawara, Shota Sasaki, Shuhei Kurita, Taishi Nakamura, Takashi Kodama, Takumi Okamoto.