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Model: llm-jp/llm-jp-3-13b-instruct 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-instruct
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This repository provides 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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The development was partially supported by [GENIAC](https://www.meti.go.jp/policy/mono_info_service/geniac/index.html).
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| Model Variants |
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| :--- |
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| [llm-jp-3-1.8b](https://huggingface.co/llm-jp/llm-jp-3-1.8b) |
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| [llm-jp-3-1.8b-instruct](https://huggingface.co/llm-jp/llm-jp-3-1.8b-instruct) |
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| [llm-jp-3-3.7b](https://huggingface.co/llm-jp/llm-jp-3-3.7b) |
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| [llm-jp-3-3.7b-instruct](https://huggingface.co/llm-jp/llm-jp-3-3.7b-instruct) |
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| [llm-jp-3-13b](https://huggingface.co/llm-jp/llm-jp-3-13b) |
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| [llm-jp-3-13b-instruct](https://huggingface.co/llm-jp/llm-jp-3-13b-instruct) |
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| [llm-jp-3-172b-beta1](https://huggingface.co/llm-jp/llm-jp-3-172b-beta1) |
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| [llm-jp-3-172b-beta1-instruct](https://huggingface.co/llm-jp/llm-jp-3-172b-beta1-instruct) |
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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-instruct")
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model = AutoModelForCausalLM.from_pretrained("llm-jp/llm-jp-3-13b-instruct", 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
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|Params|Layers|Hidden size|Heads|Context length|Embedding parameters|Non-embedding parameters|
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|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
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|1.8b|24|2048|16|4096|407,896,064|1,459,718,144|
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|3.7b|28|3072|24|4096|611,844,096|3,171,068,928|
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|13b|40|5120|40|4096|1,019,740,160|12,688,184,320|
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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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### Instruction tuning
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The models have been fine-tuned on the following datasets.
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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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| |[answer-carefully-002](https://liat-aip.sakura.ne.jp/wp/answercarefully-dataset/)| A manually constructed instruction dataset focusing on LLMs' safety |
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| |ichikara-instruction-format| A small amount of instruction dataset edited from ichikara-instruction, 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_additional-ja](https://huggingface.co/datasets/kanhatakeyama/wizardlm8x22b-logical-math-coding-sft_additional-ja)| A synthetic instruction dataset. |
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| |[Synthetic-JP-EN-Coding-Dataset-567k](https://huggingface.co/datasets/Aratako/Synthetic-JP-EN-Coding-Dataset-567k)| A synthetic instruction dataset. We used sampled one.|
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|English |[FLAN](https://huggingface.co/datasets/Open-Orca/FLAN) | We used sampled one. |
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## Evaluation
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### llm-jp-eval (v1.3.1)
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We evaluated the models using 100 examples from the dev split.
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| Model name | average | EL | FA | HE | MC | MR | MT | NLI | QA | RC |
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| :--- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
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| [llm-jp-3-1.8b](https://huggingface.co/llm-jp/llm-jp-3-1.8b) | 0.3767 | 0.3725 | 0.1948 | 0.2350 | 0.2500 | 0.0900 | 0.7730 | 0.3080 | 0.4629 | 0.7040 |
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| [llm-jp-3-1.8b-instruct](https://huggingface.co/llm-jp/llm-jp-3-1.8b-instruct) | 0.4596 | 0.4280 | 0.1987 | 0.3250 | 0.3300 | 0.4200 | 0.7900 | 0.3520 | 0.4698 | 0.8224 |
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| [llm-jp-3-3.7b](https://huggingface.co/llm-jp/llm-jp-3-3.7b) | 0.4231 | 0.3812 | 0.2440 | 0.2200 | 0.1900 | 0.3600 | 0.7947 | 0.3800 | 0.4688 | 0.7694 |
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| [llm-jp-3-3.7b-instruct](https://huggingface.co/llm-jp/llm-jp-3-3.7b-instruct) | 0.5188 | 0.4191 | 0.2504 | 0.3400 | 0.5000 | 0.5800 | 0.8166 | 0.4500 | 0.4881 | 0.8247 |
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| [llm-jp-3-13b](https://huggingface.co/llm-jp/llm-jp-3-13b) | 0.5802 | 0.5570 | 0.2593 | 0.4600 | 0.7000 | 0.6300 | 0.8292 | 0.3460 | 0.5937 | 0.8469 |
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| [llm-jp-3-13b-instruct](https://huggingface.co/llm-jp/llm-jp-3-13b-instruct) | 0.6168 | 0.5408 | 0.2757 | 0.4950 | 0.9200 | 0.7100 | 0.8317 | 0.4640 | 0.4642 | 0.8500 |
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### Japanese MT Bench
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We evaluated the models using `gpt-4-0613`. Please see the [codes](https://github.com/llm-jp/llm-leaderboard/tree/main) for details.
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| Model name | average | coding | extraction | humanities | math | reasoning | roleplay | stem | writing |
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| :--- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
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| [llm-jp-3-1.8b-instruct](https://huggingface.co/llm-jp/llm-jp-3-1.8b-instruct) | 4.93 | 1.50 | 4.70 | 7.80 | 1.55 | 2.60 | 7.80 | 6.10 | 7.40 |
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| [llm-jp-3-3.7b-instruct](https://huggingface.co/llm-jp/llm-jp-3-3.7b-instruct) | 5.50 | 1.95 | 4.05 | 8.25 | 2.25 | 4.00 | 8.80 | 7.25 | 7.45 |
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| [llm-jp-3-13b-instruct](https://huggingface.co/llm-jp/llm-jp-3-13b-instruct) | 6.47 | 3.15 | 7.05 | 9.15 | 3.75 | 5.40 | 8.30 | 7.50 | 7.45 |
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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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"hidden_act": "silu",
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"hidden_size": 5120,
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"initializer_range": 0.02,
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"intermediate_size": 13824,
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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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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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"rope_theta": 10000,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.40.2",
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"use_cache": true,
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"vocab_size": 99584
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}
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generation_config.json
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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": "4.40.2"
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}
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51
special_tokens_map.json
Normal file
51
special_tokens_map.json
Normal file
@@ -0,0 +1,51 @@
|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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|
||||
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|
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||||
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|
||||
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||||
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|
||||
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||||
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||||
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|
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
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|
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|
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|
||||
}
|
||||
}
|
||||
398481
tokenizer.json
Normal file
398481
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
84
tokenizer_config.json
Normal file
84
tokenizer_config.json
Normal file
@@ -0,0 +1,84 @@
|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"2": {
|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"3": {
|
||||
"content": "<MASK|LLM-jp>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"4": {
|
||||
"content": "<PAD|LLM-jp>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"5": {
|
||||
"content": "<CLS|LLM-jp>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"6": {
|
||||
"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,
|
||||
"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