Update README.md

README.md

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 ---
 license: cc-by-nc-4.0
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+
+# tsubaki-10b-instruction-sft
+
+# Overview
+This repository provides a Japanese-centric multilingual GPT-NeoX model of 10 billion parameters.
+
+* **Library**
+    
+    The model was trained using code based on [EleutherAI/gpt-neox](https://github.com/EleutherAI/gpt-neox).
+
+* **Model architecture**
+
+    A 36-layer, 4864-hidden-size transformer-based language model.
+
+* **Pre-training**
+
+    The model was trained on around **600B** tokens from a mixture of the following corpora
+
+    - [Japanese C4](https://huggingface.co/datasets/mc4)
+    - [The Pile](https://huggingface.co/datasets/EleutherAI/pile)
+
+* **Instruction-supervised-finetuning**
+
+    The model was finetuned on a subset records from a mixture of the following dataset
+
+    - [Alpaca (English)](https://github.com/gururise/AlpacaDataCleaned/blob/main/alpaca_data_cleaned.json)
+    - [Alpaca (Japanese translation)](https://github.com/shi3z/alpaca_ja/blob/main/alpaca_cleaned_ja.json)
+    - [Flan 2021](https://huggingface.co/datasets/conceptofmind/flan2021_submix_original)
+    - [Flan CoT](https://huggingface.co/datasets/conceptofmind/cot_submix_original)
+    - [Flan Dialog](https://huggingface.co/datasets/conceptofmind/dialog_submix_original)
+
+* **Model Series**
+
+    | Variant | Link |
+    | :-- | :--|
+    | tsubaki-10b-instruction-sft | https://huggingface.co/Kojima777/tsubaki-10b-instruction-sft |
+    | tsubaki-10b | https://huggingface.co/Kojima777/tsubaki-10b |
+
+* **Authors**
+    
+    Takeshi Kojima
+
+---
+
+# Benchmarking
+
+* **Japanese benchmark**
+
+    - *The 4-task average accuracy is based on results of JCommonsenseQA, JNLI, MARC-ja, and JSQuAD.*
+   
+    | Model | Average | JCommonsenseQA | JNLI | MARC-ja | JSQuAD |
+    | :-- | :-- | :-- | :-- | :-- | :-- |
+    | tsubaki-10b-instruction-sft | 79.04 | 74.35 | 65.65 | 96.06 | 80.09 |
+    | tsubaki-10b | 67.27 | 65.86 | 54.19 | 84.49 | 64.54 |
+
+---
+
+# How to use the model
+
+~~~~python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("Kojima777/tsubaki-10b-instruction-sft", use_fast=False)
+model = AutoModelForCausalLM.from_pretrained("Kojima777/tsubaki-10b-instruction-sft")
+
+if torch.cuda.is_available():
+    model = model.to("cuda")
+
+text = "大規模言語モデルについて説明してください。"
+text = f'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。\n\n### 指示:\n{text}\n\n### 応答:'
+token_ids = tokenizer.encode(text, add_special_tokens=False, return_tensors="pt")
+
+with torch.no_grad():
+    output_ids = model.generate(
+        token_ids.to(model.device),
+        max_new_tokens=100,
+        do_sample=True,
+        temperature=0.6,
+        top_p=0.9,
+        pad_token_id=tokenizer.pad_token_id,
+        bos_token_id=tokenizer.bos_token_id,
+        eos_token_id=tokenizer.eos_token_id
+    )
+
+output = tokenizer.decode(output_ids.tolist()[0])
+print(output)
+
+~~~~
+---
+
+# Licenese
+[cc-by-nc-4.0](https://creativecommons.org/licenses/by-nc/4.0/)