copy tokyotech-llm/Swallow-MS-7b-instruct-v0.1 (revision=8b17f1c87697fb354952fa0d1018568e50bdff56)
Browse files- .gitattributes +1 -0
- README.md +187 -0
- config.json +26 -0
- generation_config.json +6 -0
- logo.png +3 -0
- model-00001-of-00003.safetensors +3 -0
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- model-00003-of-00003.safetensors +3 -0
- model.safetensors.index.json +298 -0
- requirements.txt +5 -0
- special_tokens_map.json +5 -0
- tokenizer.json +0 -0
- tokenizer.model +3 -0
- tokenizer_config.json +43 -0
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README.md
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---
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language:
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- en
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- ja
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library_name: transformers
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pipeline_tag: text-generation
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model_type: mistral
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license: apache-2.0
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---
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# Swallow-MS-7b-v0.1
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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.
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# Model Release Updates
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We are excited to share the release schedule for our latest models:
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- **April 26, 2024**: Released the [Swallow-MS-7b-instruct-v0.1](https://huggingface.co/tokyotech-llm/Swallow-MS-7b-instruct-v0.1)
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- **March 11, 2024**: Released the [Swallow-MS-7b-v0.1](https://huggingface.co/tokyotech-llm/Swallow-MS-7b-v0.1)
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+
![logo](./logo.png)
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This repository provides large language models developed by [TokyoTech-LLM](https://tokyotech-llm.github.io/).
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## Model Details
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* **Model type**: Please refer to Mistral technical report for details on the model architecture.
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* **Language(s)**: Japanese English
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* **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.
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* **Contact**: swallow[at]nlp.c.titech.ac.jp
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## Instruct Model Performance
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### MT-Bench JA
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#### Turn-Wise Performance
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We report overall (i.e., average over scores of the first and second turns), first, and second turn scores.
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##### Overall
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|Model|Average|Writing|Roleplay|Reasoning|Math|Coding|Extraction|STEM|Humanities|
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|---|---|---|---|---|---|---|---|---|---|
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| Swallow-MS-7b-instruct-v0.1 |0.3411|0.3770|0.4290|0.3454|0.1040|0.2400|0.3677|0.3907|0.4750|
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##### First Turn
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|Model|Average|Writing|Roleplay|Reasoning|Math|Coding|Extraction|STEM|Humanities|
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|---|---|---|---|---|---|---|---|---|---|
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| Swallow-MS-7b-instruct-v0.1 |0.3699|0.4880|0.4260|0.3900|0.1080|0.2364|0.3780|0.4500|0.4800|
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##### Second Turn
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|Model|Average|Writing|Roleplay|Reasoning|Math|Coding|Extraction|STEM|Humanities|
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|---|---|---|---|---|---|---|---|---|---|
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| Swallow-MS-7b-instruct-v0.1 |0.3130|0.2624|0.4320|0.2996|0.1000|0.2430|0.3564|0.3291|0.4700|
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|
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#### Comparison to the past model
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|
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We only provide the overall score in this section.
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|Model|Average|Writing|Roleplay|Reasoning|Math|Coding|Extraction|STEM|Humanities|
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|---|---|---|---|---|---|---|---|---|---|
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| Swallow-MS-7b-instruct-v0.1 |0.3411|0.3770|0.4290|0.3454|0.1040|0.2400|0.3677|0.3907|0.4750|
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| 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|
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| calm2-7b-chat |0.3204|0.4657|0.4898|0.1837|0.1005|0.1414|0.3927|0.3601|0.4293|
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| calm2-7b-chat-dpo-experimental |0.3493|0.5312|0.5237|0.1857|0.1000|0.1813|0.3355|0.4320|0.5051|
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| RakutenAI-7B-instruct |0.2994|0.3623|0.3711|0.3333|0.1763|0.1581|0.4215|0.2824|0.2901|
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| RakutenAI-7B-chat |0.3667|0.4229|0.4644|0.3990|0.2161|0.2390|0.3416|0.3904|0.4601|
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|
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## Evaluation Benchmarks
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### MT-Bench JA
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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.
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We utilized the following settings:
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- Implemantation: FastChat [Zheng+, 2023] (commit #e86e70d0)
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- Question: [Nejumi LLM-Leaderboard NEO, mtbench_ja_question_v3](https://wandb.ai/wandb-japan/llm-leaderboard/artifacts/dataset/mtbench_ja_question/v3)
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- Reference Answer: [Nejumi LLM-Leaderboard NEO, mtbench_ja_referenceanswer_v1](https://wandb.ai/wandb-japan/llm-leaderboard/artifacts/dataset/mtbench_ja_referenceanswer/v1)
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- Prompt for Judge: [Nejumi LLM-Lederboard NEO, mtbench_ja_prompt_v1](https://wandb.ai/wandb-japan/llm-leaderboard/artifacts/dataset/mtbench_ja_prompt/v1)
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- Judge: `gpt-4-1106-preview`
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- Scoring: Absolute scale normalized to a 0-1 range, averaged over five runs.
|
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|
85 |
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|
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## Usage
|
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First install additional dependencies in [requirements.txt](./requirements.txt):
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|
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```sh
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pip install -r requirements.txt
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```
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|
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### Instruction format Ver0.1
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This format must be adhered to strictly, as deviations may result in less optimal outputs from the model.
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The template used to construct a prompt for the Instruct model is specified as follows:
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```
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<s>[INST] <<SYS>>\n{SYSTEM_PROMPT}\n<</SYS>>\n\n{USER_MESSAGE_1} [/INST] {BOT_MESSAGE_1} </s>[INST] {USER_MESSAGE_2}[/INST]
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```
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|
103 |
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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.
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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。"
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For the "{USER_MESSAGE_1}" part, We recommend using {instruction}\n{input}
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In other words, We recommend the following:
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```
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<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]
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```
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### Use the instruct model Ver0.1
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```python
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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model_name = "tokyotech-llm/Swallow-MS-7b-instruct-v0.1"
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model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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device = "cuda"
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messages = [
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{"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。"},
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{"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"}
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]
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encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
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model_inputs = encodeds.to(device)
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model.to(device)
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generated_ids = model.generate(model_inputs, max_new_tokens=128, do_sample=True)
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decoded = tokenizer.batch_decode(generated_ids)
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print(decoded[0])
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```
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## Training Datasets
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### Instruction Tuning Ver0.1
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146 |
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The following datasets were used for the instruction tuning.
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- [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.
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- [OpenAssistant Conversations Dataset 21k Ja](https://huggingface.co/datasets/llm-jp/oasst1-21k-ja)
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- [OpenAssistant Conversations Dataset 21k En](https://huggingface.co/datasets/llm-jp/oasst1-21k-en)
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- [Databricks Dolly 15k Ja](https://huggingface.co/datasets/llm-jp/databricks-dolly-15k-ja)
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- [Databricks Dolly 15k En](https://huggingface.co/datasets/databricks/databricks-dolly-15k)
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Please note that some of the data had issues with quality or format, so not all of it was used.
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|
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## Risks and Limitations
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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.
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160 |
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|
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## Acknowledgements
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162 |
+
|
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We thank Mistral AI for releasing Mistral 7B v0.1 under an open license for others to build on.
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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.
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|
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## License
|
168 |
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|
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apache-2.0
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## Authors
|
172 |
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|
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Here are the team members:
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- From [Okazaki Laboratory](https://www.nlp.c.titech.ac.jp/index.en.html), the following members:
|
175 |
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- [Naoaki Okazaki](https://www.chokkan.org/index.ja.html)
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176 |
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- [Sakae Mizuki](https://s-mizuki-nlp.github.io/)
|
177 |
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- [Hiroki Iida](https://meshidenn.github.io/)
|
178 |
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- [Mengsay Loem](https://loem-ms.github.io/)
|
179 |
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- [Shota Hirai](https://huggingface.co/Kotemo428)
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180 |
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- [Kakeru Hattori](https://aya-se.vercel.app/)
|
181 |
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- [Masanari Ohi](https://twitter.com/stjohn2007)
|
182 |
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- From [YOKOTA Laboratory](https://www.rio.gsic.titech.ac.jp/en/index.html), the following members:
|
183 |
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- [Rio Yokota](https://twitter.com/rioyokota)
|
184 |
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- [Kazuki Fujii](https://twitter.com/okoge_kaz)
|
185 |
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- [Taishi Nakamura](https://twitter.com/Setuna7777_2)
|
186 |
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- [Takumi Okamoto](https://www.linkedin.com/in/takumi-okamoto)
|
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- [Ishida Shigeki](https://www.wantedly.com/id/reborn27)
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config.json
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{
|
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"_name_or_path": "tokyotech-llm/Swallow-MS-7b-instruct-v0.1",
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"architectures": [
|
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"MistralForCausalLM"
|
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],
|
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"attention_dropout": 0.0,
|
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"bos_token_id": 1,
|
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"eos_token_id": 2,
|
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"hidden_act": "silu",
|
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"hidden_size": 4096,
|
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"initializer_range": 0.02,
|
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"intermediate_size": 14336,
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"max_position_embeddings": 4096,
|
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"model_type": "mistral",
|
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"num_attention_heads": 32,
|
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"num_hidden_layers": 32,
|
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"num_key_value_heads": 8,
|
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"rms_norm_eps": 1e-05,
|
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"rope_theta": 10000.0,
|
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"sliding_window": 4096,
|
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"tie_word_embeddings": false,
|
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"torch_dtype": "bfloat16",
|
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"transformers_version": "4.39.1",
|
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"use_cache": false,
|
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"vocab_size": 42800
|
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}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"transformers_version": "4.36.2"
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}
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logo.png
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30 |
+
"chat_template": "{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% elif false == true and not '<<SYS>>' in messages[0]['content'] %}{% set loop_messages = messages %}{% set system_message = 'あなたは誠実で優秀な日本人のアシスタントです。' %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{{ bos_token }}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if loop.index0 == 0 and system_message != false %}{% set content = '<<SYS>>\\n' + system_message + '\\n<</SYS>>\\n\\n' + message['content'] %}{% else %}{% set content = message['content'] %}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + content.strip() + ' [/INST] ' }}{% elif message['role'] == 'system' %}{{ '<<SYS>>\\n' + content.strip() + '\\n<</SYS>>\\n\\n' }}{% elif message['role'] == 'assistant' %}{{ '' + content.strip() + '' + eos_token }}{% endif %}{% endfor %}",
|
31 |
+
"additional_special_tokens": [],
|
32 |
+
"bos_token": "<s>",
|
33 |
+
"clean_up_tokenization_spaces": false,
|
34 |
+
"eos_token": "</s>",
|
35 |
+
"legacy": true,
|
36 |
+
"model_max_length": 1000000000000000019884624838656,
|
37 |
+
"pad_token": null,
|
38 |
+
"sp_model_kwargs": {},
|
39 |
+
"spaces_between_special_tokens": false,
|
40 |
+
"tokenizer_class": "LlamaTokenizer",
|
41 |
+
"unk_token": "<unk>",
|
42 |
+
"use_default_system_prompt": false
|
43 |
+
}
|