copy tokyotech-llm/Swallow-MS-7b-instruct-v0.1 (revision=8b17f1c87697fb354952fa0d1018568e50bdff56)

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README.md

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+---
+language:
+  - en
+  - ja
+library_name: transformers
+pipeline_tag: text-generation
+model_type: mistral
+license: apache-2.0
+---
+
+# Swallow-MS-7b-v0.1
+
+Our Swallow-MS-7b-v0.1 model has undergone continual pre-training from the Mistral-7B-v0.1, primarily with the addition of Japanese language data. 
+
+# Model Release Updates
+
+We are excited to share the release schedule for our latest models:
+- **April 26, 2024**: Released the [Swallow-MS-7b-instruct-v0.1](https://huggingface.co/tokyotech-llm/Swallow-MS-7b-instruct-v0.1)
+- **March 11, 2024**: Released the [Swallow-MS-7b-v0.1](https://huggingface.co/tokyotech-llm/Swallow-MS-7b-v0.1)
+![logo](./logo.png)
+
+This repository provides large language models developed by [TokyoTech-LLM](https://tokyotech-llm.github.io/).
+
+## Model Details
+
+* **Model type**: Please refer to Mistral technical report for details on the model architecture. 
+* **Language(s)**: Japanese English
+* **Tokenizer**: This model employs a tokenizer that features a broadened vocabulary based on Japanese data. This allows for a more efficient representation of text using fewer tokens, leading to a notably faster inference process.
+* **Contact**: swallow[at]nlp.c.titech.ac.jp 
+
+## Instruct Model Performance
+
+### MT-Bench JA
+
+#### Turn-Wise Performance
+
+We report overall (i.e., average over scores of the first and second turns), first, and second turn scores.
+
+##### Overall
+
+|Model|Average|Writing|Roleplay|Reasoning|Math|Coding|Extraction|STEM|Humanities|
+|---|---|---|---|---|---|---|---|---|---|
+| Swallow-MS-7b-instruct-v0.1 |0.3411|0.3770|0.4290|0.3454|0.1040|0.2400|0.3677|0.3907|0.4750|
+
+##### First Turn
+
+|Model|Average|Writing|Roleplay|Reasoning|Math|Coding|Extraction|STEM|Humanities|
+|---|---|---|---|---|---|---|---|---|---|
+| Swallow-MS-7b-instruct-v0.1 |0.3699|0.4880|0.4260|0.3900|0.1080|0.2364|0.3780|0.4500|0.4800|
+
+##### Second Turn
+
+|Model|Average|Writing|Roleplay|Reasoning|Math|Coding|Extraction|STEM|Humanities|
+|---|---|---|---|---|---|---|---|---|---|
+| Swallow-MS-7b-instruct-v0.1 |0.3130|0.2624|0.4320|0.2996|0.1000|0.2430|0.3564|0.3291|0.4700|
+
+#### Comparison to the past model
+
+We only provide the overall score in this section.
+
+|Model|Average|Writing|Roleplay|Reasoning|Math|Coding|Extraction|STEM|Humanities|
+|---|---|---|---|---|---|---|---|---|---|
+| Swallow-MS-7b-instruct-v0.1 |0.3411|0.3770|0.4290|0.3454|0.1040|0.2400|0.3677|0.3907|0.4750|
+| ELYZA-japanese-Llama-2-7b-fast-instruct |0.2827|0.3289|0.3907|0.2424|0.1480|0.1584|0.3511|0.3053|0.3365|
+| calm2-7b-chat |0.3204|0.4657|0.4898|0.1837|0.1005|0.1414|0.3927|0.3601|0.4293|
+| calm2-7b-chat-dpo-experimental |0.3493|0.5312|0.5237|0.1857|0.1000|0.1813|0.3355|0.4320|0.5051|
+| RakutenAI-7B-instruct |0.2994|0.3623|0.3711|0.3333|0.1763|0.1581|0.4215|0.2824|0.2901|
+| RakutenAI-7B-chat |0.3667|0.4229|0.4644|0.3990|0.2161|0.2390|0.3416|0.3904|0.4601|
+
+
+## Evaluation Benchmarks
+
+### MT-Bench JA
+
+We used [Japanese MT-Bench](https://wandb.ai/wandb-japan/llm-leaderboard/artifacts/dataset/mtbench_ja_question) to assess the instruction-following capabilities of models.
+We utilized the following settings:
+
+- Implemantation: FastChat [Zheng+, 2023] (commit #e86e70d0)
+- Question: [Nejumi LLM-Leaderboard NEO, mtbench_ja_question_v3](https://wandb.ai/wandb-japan/llm-leaderboard/artifacts/dataset/mtbench_ja_question/v3)
+- Reference Answer: [Nejumi LLM-Leaderboard NEO, mtbench_ja_referenceanswer_v1](https://wandb.ai/wandb-japan/llm-leaderboard/artifacts/dataset/mtbench_ja_referenceanswer/v1)
+- Prompt for Judge: [Nejumi LLM-Lederboard NEO, mtbench_ja_prompt_v1](https://wandb.ai/wandb-japan/llm-leaderboard/artifacts/dataset/mtbench_ja_prompt/v1)
+- Judge: `gpt-4-1106-preview`
+- Scoring: Absolute scale normalized to a 0-1 range, averaged over five runs.
+
+
+## Usage
+
+First install additional dependencies in [requirements.txt](./requirements.txt):
+
+```sh
+pip install -r requirements.txt
+```
+
+### Instruction format Ver0.1
+This format must be adhered to strictly, as deviations may result in less optimal outputs from the model.
+
+The template used to construct a prompt for the Instruct model is specified as follows:
+
+```
+<s>[INST] <<SYS>>\n{SYSTEM_PROMPT}\n<</SYS>>\n\n{USER_MESSAGE_1} [/INST] {BOT_MESSAGE_1} </s>[INST] {USER_MESSAGE_2}[/INST] 
+```
+
+
+Please be aware that ``<s>`` and ``</s>`` are special tokens used for the beginning of string (BOS) and end of string (EOS), respectively, while [INST] and [/INST] are considered regular strings.
+
+For the "{SYSTEM_PROMPT}" part, We recommend using "あなたは誠実で優秀な日本人のアシスタントです。"
+
+For the "{USER_MESSAGE_1}" part, We recommend using {instruction}\n{input}
+
+In other words, We recommend the following:
+
+```
+<s>[INST] <<SYS>>\nあなたは誠実で優秀な日本人のアシスタントです。\n<</SYS>>\n\n{instruction1}\n{input1} [/INST] {BOT_MESSAGE_1}</s>[INST] \n\n{instruction2}\n{input2} [/INST] 
+```
+
+### Use the instruct model Ver0.1
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_name = "tokyotech-llm/Swallow-MS-7b-instruct-v0.1"
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+device = "cuda"
+
+messages = [
+    {"role": "system", "content": "あなたは誠実で優秀な日本人のアシスタントです。"},
+    {"role": "user", "content": "東京工業大学の主なキャンパスについて教えてください"}
+]
+
+encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
+
+model_inputs = encodeds.to(device)
+model.to(device)
+
+generated_ids = model.generate(model_inputs, max_new_tokens=128, do_sample=True)
+decoded = tokenizer.batch_decode(generated_ids)
+print(decoded[0])
+```
+
+## Training Datasets
+
+### Instruction Tuning Ver0.1
+
+The following datasets were used for the instruction tuning. 
+
+- [OpenAssistant Conversations Dataset](https://huggingface.co/datasets/llm-jp/oasst1-21k-ja) was used, where human utterances are included but the responses are not used. Instead, the responses were generated using the [Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/datasets/llm-jp/oasst1-21k-jahttps://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) model.
+- [OpenAssistant Conversations Dataset 21k Ja](https://huggingface.co/datasets/llm-jp/oasst1-21k-ja)
+- [OpenAssistant Conversations Dataset 21k En](https://huggingface.co/datasets/llm-jp/oasst1-21k-en)
+- [Databricks Dolly 15k Ja](https://huggingface.co/datasets/llm-jp/databricks-dolly-15k-ja)
+- [Databricks Dolly 15k En](https://huggingface.co/datasets/databricks/databricks-dolly-15k)
+
+Please note that some of the data had issues with quality or format, so not all of it was used.
+
+## 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.
+
+## Acknowledgements
+
+We thank Mistral AI for releasing Mistral 7B v0.1 under an open license for others to build on.
+
+Our project is supported by the [ABCI Large-scale Language Model Building Support Program](https://abci.ai/en/link/llm_support_program.html) of the National Institute of Advanced Industrial Science and Technology. 
+
+## License
+
+apache-2.0
+
+## Authors
+
+Here are the team members:
+- From [Okazaki Laboratory](https://www.nlp.c.titech.ac.jp/index.en.html), the following members:
+  - [Naoaki Okazaki](https://www.chokkan.org/index.ja.html)
+  - [Sakae Mizuki](https://s-mizuki-nlp.github.io/)
+  - [Hiroki Iida](https://meshidenn.github.io/)
+  - [Mengsay Loem](https://loem-ms.github.io/)
+  - [Shota Hirai](https://huggingface.co/Kotemo428)
+  - [Kakeru Hattori](https://aya-se.vercel.app/)
+  - [Masanari Ohi](https://twitter.com/stjohn2007)
+- From [YOKOTA Laboratory](https://www.rio.gsic.titech.ac.jp/en/index.html), the following members:
+  - [Rio Yokota](https://twitter.com/rioyokota)
+  - [Kazuki Fujii](https://twitter.com/okoge_kaz)
+  - [Taishi Nakamura](https://twitter.com/Setuna7777_2)
+  - [Takumi Okamoto](https://www.linkedin.com/in/takumi-okamoto)
+  - [Ishida Shigeki](https://www.wantedly.com/id/reborn27)

config.json

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+{
+  "_name_or_path": "tokyotech-llm/Swallow-MS-7b-instruct-v0.1",
+  "architectures": [
+    "MistralForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 4096,
+  "model_type": "mistral",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 10000.0,
+  "sliding_window": 4096,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.39.1",
+  "use_cache": false,
+  "vocab_size": 42800
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.36.2"
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requirements.txt

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+torch
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+protobuf

special_tokens_map.json

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+{
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+}

tokenizer.model

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6239e68e91d052426ceac4b17aadca42c34f5b2067caee6b67d42105742c1f16
+size 903208

tokenizer_config.json

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+      "rstrip": false,
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