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Model: llm-jp/llm-jp-3-13b-instruct2 Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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language:
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- en
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- ja
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programming_language:
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- C
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- C++
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- C#
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- Go
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- Java
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- JavaScript
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- Lua
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- PHP
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- Python
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- Ruby
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- Rust
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- Scala
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- TypeScript
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pipeline_tag: text-generation
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library_name: transformers
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inference: false
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---
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# llm-jp-3-13b-instruct2
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LLM-jp-3 is the series of large language models developed by the [Research and Development Center for Large Language Models](https://llmc.nii.ac.jp/) at the [National Institute of Informatics](https://www.nii.ac.jp/en/).
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This repository provides **llm-jp-3-13b-instruct2** model.
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For an overview of the LLM-jp-3 models across different parameter sizes, please refer to:
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- [LLM-jp-3 Pre-trained Models](https://huggingface.co/collections/llm-jp/llm-jp-3-pre-trained-models-672c6096472b65839d76a1fa)
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- [LLM-jp-3 Fine-tuned Models](https://huggingface.co/collections/llm-jp/llm-jp-3-fine-tuned-models-672c621db852a01eae939731).
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Checkpoints format: Hugging Face Transformers
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## Required Libraries and Their Versions
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- torch>=2.3.0
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- transformers>=4.40.1
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- tokenizers>=0.19.1
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- accelerate>=0.29.3
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- flash-attn>=2.5.8
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## Usage
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```python
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("llm-jp/llm-jp-3-13b-instruct2")
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model = AutoModelForCausalLM.from_pretrained("llm-jp/llm-jp-3-13b-instruct2", device_map="auto", torch_dtype=torch.bfloat16)
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chat = [
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{"role": "system", "content": "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。"},
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{"role": "user", "content": "自然言語処理とは何か"},
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]
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tokenized_input = tokenizer.apply_chat_template(chat, add_generation_prompt=True, tokenize=True, return_tensors="pt").to(model.device)
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with torch.no_grad():
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output = model.generate(
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tokenized_input,
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max_new_tokens=100,
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do_sample=True,
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top_p=0.95,
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temperature=0.7,
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repetition_penalty=1.05,
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)[0]
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print(tokenizer.decode(output))
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```
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## Model Details
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- **Model type:** Transformer-based Language Model
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- **Total seen tokens:** 2.1T tokens
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|Params|Layers|Hidden size|Heads|Context length|Embedding parameters|Non-embedding parameters|
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|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
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|150M|12|512|8|4096|101,874,688|50,344,448|
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|440M|16|1024|8|4096|203,749,376|243,303,424|
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|980M|20|1536|8|4096|305,624,064|684,258,816|
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|1.8b|24|2048|16|4096|407,498,752|1,459,718,144|
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|3.7b|28|3072|24|4096|611,248,128|3,171,068,928|
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|7.2b|32|4096|32|4096|814,997,504|6,476,271,616|
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|13b|40|5120|40|4096|1,018,746,880|12,688,184,320|
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|172b|96|12288|96|4096|2,444,992,512|169,947,181,056|
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## Tokenizer
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The tokenizer of this model is based on [huggingface/tokenizers](https://github.com/huggingface/tokenizers) Unigram byte-fallback model.
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The vocabulary entries were converted from [`llm-jp-tokenizer v3.0`](https://github.com/llm-jp/llm-jp-tokenizer/releases/tag/v3.0b2).
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Please refer to [README.md](https://github.com/llm-jp/llm-jp-tokenizer) of `llm-jp-tokenizer` for details on the vocabulary construction procedure (the pure SentencePiece training does not reproduce our vocabulary).
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## Datasets
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### Pre-training
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The models have been pre-trained using a blend of the following datasets.
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| Language | Dataset | Tokens|
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|:---|:---|---:|
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|Japanese|[Wikipedia](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|2.6B
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||[Common Crawl](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|762.8B
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||[WARP/PDF](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|237.3B
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||[WARP/HTML](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|2.7B
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||[Kaken](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|1.8B
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|English|[Wikipedia](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|4.7B
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||[Dolma/CC-head](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|608.5B
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||[Dolma/C4](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|181.6B
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||[Dolma/Reddit](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|83.1B
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||[Dolma/PeS2o](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|62.9B
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||[Dolma/Gutenberg](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|5.5B
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||[Dolma/Wiki](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|3.9B
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|Code|[The Stack](https://huggingface.co/datasets/bigcode/the-stack)|114.1B
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|Chinese|[Wikipedia](https://huggingface.co/datasets/bigcode/the-stack)|0.8B
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|Korean|[Wikipedia](https://huggingface.co/datasets/bigcode/the-stack)|0.3B
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### Post-training
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We have fine-tuned the pre-trained checkpoint with supervised fine-tuning.
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#### Supervised Fine-tuning
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The datasets used for supervised fine-tuning are as follows:
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| Language | Dataset | Description |
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|:---|:---|:---|
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|Japanese|[ichikara-instruction-004-002](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/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-%e5%85%ac%e9%96%8b/)| A manually constructed instruction dataset. |
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| |[AnswerCarefully (ver2.0)](https://huggingface.co/datasets/llm-jp/AnswerCarefully)| A manually constructed instruction dataset focusing on LLMs' safety. |
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| |ichikara-instruction-format| A small subset of the ichikara-instruction dataset, edited with some constraints on the output format. |
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| |[AutoMultiTurnByCalm3-22B](https://huggingface.co/datasets/kanhatakeyama/AutoMultiTurnByCalm3-22B)| A synthetic instruction dataset. |
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| |[ramdom-to-fixed-multiturn-Calm3](https://huggingface.co/datasets/kanhatakeyama/ramdom-to-fixed-multiturn-Calm3)| A synthetic instruction dataset. |
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| |[wizardlm8x22b-logical-math-coding-sft-ja](https://huggingface.co/datasets/llm-jp/wizardlm8x22b-logical-math-coding-sft-ja)| A synthetic instruction dataset. |
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| |[magpie-sft-v1.0](https://huggingface.co/datasets/llm-jp/magpie-sft-v1.0)| A synthetic instruction dataset we created. |
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|English|[Daring-Anteater](https://huggingface.co/datasets/nvidia/Daring-Anteater)| - |
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| |[FLAN](https://huggingface.co/datasets/llm-jp/FLAN/blob/main/README.md) | - |
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|Japanese & English|[Synthetic-JP-EN-Coding-Dataset](https://huggingface.co/datasets/llm-jp/Synthetic-JP-EN-Coding-Dataset)| A synthetic instruction dataset. |
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## Evaluation
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Detailed evaluation results are reported in this [blog](https://llm-jp.nii.ac.jp/blog/2025/02/05/instruct3.html).
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## Risks and Limitations
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The models released here are 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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## Send Questions to
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llm-jp(at)nii.ac.jp
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## License
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[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
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## Model Card Authors
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*The names are listed in alphabetical order.*
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Hirokazu Kiyomaru and Takashi Kodama.
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config.json
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{
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"_name_or_path": "llm-jp/llm-jp-3-13b",
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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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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"head_dim": 128,
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"max_position_embeddings": 4096,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 40,
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"num_hidden_layers": 40,
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"num_key_value_heads": 40,
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"pretraining_tp": 1,
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"rope_scaling": null,
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"transformers_version": "4.47.0",
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"use_cache": true,
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"vocab_size": 99584
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}
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51
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85
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Normal file
85
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Normal file
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|
||||
"content": "<SEP|LLM-jp>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"7": {
|
||||
"content": "<EOD|LLM-jp>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"bos_token": "<s>",
|
||||
"chat_template": "{{bos_token}}{% for message in messages %}{% if message['role'] == 'user' %}{{ '\\n\\n### 指示:\\n' + message['content'] }}{% elif message['role'] == 'system' %}{{ '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。' }}{% elif message['role'] == 'assistant' %}{{ '\\n\\n### 応答:\\n' + message['content'] + eos_token }}{% endif %}{% if loop.last and add_generation_prompt %}{{ '\\n\\n### 応答:\\n' }}{% endif %}{% endfor %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"cls_token": "<CLS|LLM-jp>",
|
||||
"eod_token": "</s>",
|
||||
"eos_token": "</s>",
|
||||
"extra_ids": 0,
|
||||
"extra_special_tokens": {},
|
||||
"mask_token": "<MASK|LLM-jp>",
|
||||
"model_max_length": 1000000000000000019884624838656,
|
||||
"pad_token": "<PAD|LLM-jp>",
|
||||
"sep_token": "<SEP|LLM-jp>",
|
||||
"sp_model_kwargs": {},
|
||||
"tokenizer_class": "PreTrainedTokenizerFast",
|
||||
"unk_token": "<unk>"
|
||||
}
|
||||
Reference in New Issue
Block a user