llm-jp
/

llm-jp-3-172b-instruct3

+---
+license: other
+license_name: llm-jp-3-172b-instruct3-tou
+license_link: https://huggingface.co/llm-jp/llm-jp-3-172b-instruct3/raw/main/LICENSE_ja
+language:
+- en
+- ja
+programming_language:
+  - C
+  - C++
+  - C#
+  - Go
+  - Java
+  - JavaScript
+  - Lua
+  - PHP
+  - Python
+  - Ruby
+  - Rust
+  - Scala
+  - TypeScript
+pipeline_tag: text-generation
+library_name: transformers
+inference: false
+---
+# llm-jp-3-172b-instruct3
+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/).
+The development was partially supported by [GENIAC](https://www.meti.go.jp/policy/mono_info_service/geniac/index.html).
+| Model Variants |
+| :--- |
+| [llm-jp-3-1.8b](https://huggingface.co/llm-jp/llm-jp-3-1.8b) |
+| [llm-jp-3-1.8b-instruct](https://huggingface.co/llm-jp/llm-jp-3-1.8b-instruct) |
+| [llm-jp-3-3.7b](https://huggingface.co/llm-jp/llm-jp-3-3.7b) |
+| [llm-jp-3-3.7b-instruct](https://huggingface.co/llm-jp/llm-jp-3-3.7b-instruct) |
+| [llm-jp-3-13b](https://huggingface.co/llm-jp/llm-jp-3-13b) |
+| [llm-jp-3-13b-instruct](https://huggingface.co/llm-jp/llm-jp-3-13b-instruct) |
+| [llm-jp-3-172b-beta1](https://huggingface.co/llm-jp/llm-jp-3-172b-beta1) |
+| [llm-jp-3-172b-beta1-instruct](https://huggingface.co/llm-jp/llm-jp-3-172b-beta1-instruct) |
+| [llm-jp-3-172b-beta2](https://huggingface.co/llm-jp/llm-jp-3-172b-beta2) |
+| [llm-jp-3-172b-beta2-instruct2](https://huggingface.co/llm-jp/llm-jp-3-172b-beta2-instruct2) |
+| [llm-jp-3-172b](https://huggingface.co/llm-jp/llm-jp-3-172b) |
+| [llm-jp-3-172b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-172b-instruct3) |
+Checkpoints format: Hugging Face Transformers
+## Required Libraries and Their Versions
+- torch>=2.3.0
+- transformers>=4.40.1
+- tokenizers>=0.19.1
+- accelerate>=0.29.3
+- flash-attn>=2.5.8
+## Usage
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+tokenizer = AutoTokenizer.from_pretrained("llm-jp/llm-jp-3-172b-instruct3")
+model = AutoModelForCausalLM.from_pretrained("llm-jp/llm-jp-3-172b-instruct3", device_map="auto", torch_dtype=torch.bfloat16)
+chat = [
+    {"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。"},
+    {"role": "user", "content": "自然言語処理とは何か"},
+]
+tokenized_input = tokenizer.apply_chat_template(chat, add_generation_prompt=True, tokenize=True, return_tensors="pt").to(model.device)
+with torch.no_grad():
+    output = model.generate(
+        tokenized_input,
+        max_new_tokens=100,
+        do_sample=True,
+        top_p=0.95,
+        temperature=0.7,
+        repetition_penalty=1.05,
+    )[0]
+print(tokenizer.decode(output))
+```
+## Model Details
+- **Model type:** Transformer-based Language Model
+- **Total seen tokens:**:
+  - llm-jp-3-1.8b: 2.1T
+  - llm-jp-3-3.7b: 2.1T
+  - llm-jp-3-13b: 2.1T
+  - llm-jp-3-172b-beta1: 0.7T
+  - llm-jp-3-172b-beta2: 1.4T
+  - llm-jp-3-172b: 2.1T
+|Params|Layers|Hidden size|Heads|Context length|Embedding parameters|Non-embedding parameters|
+|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
+|1.8b|24|2048|16|4096|407,498,752|1,459,718,144|
+|3.7b|28|3072|24|4096|611,248,128|3,171,068,928|
+|13b|40|5120|40|4096|1,018,746,880|12,688,184,320|
+|172b|96|12288|96|4096|2,444,992,512|169,947,181,056|
+## Tokenizer
+The tokenizer of this model is based on [huggingface/tokenizers](https://github.com/huggingface/tokenizers) Unigram byte-fallback model.
+The vocabulary entries were converted from [`llm-jp-tokenizer v3.0`](https://github.com/llm-jp/llm-jp-tokenizer/releases/tag/v3.0b2).
+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).
+## Datasets
+### Pre-training
+The models have been pre-trained using a blend of the following datasets.
+| Language | Dataset | Tokens|
+|:---|:---|---:|
+|Japanese|[Wikipedia](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|2.6B
+||[Common Crawl](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|762.8B
+||[WARP/PDF](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|237.3B
+||[WARP/HTML](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|2.7B
+||[Kaken](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|1.8B
+|English|[Wikipedia](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|4.7B
+||[Dolma/CC-head](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|608.5B
+||[Dolma/C4](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|181.6B
+||[Dolma/Reddit](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|83.1B
+||[Dolma/PeS2o](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|62.9B
+||[Dolma/Gutenberg](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|5.5B
+||[Dolma/Wiki](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|3.9B
+|Code|[The Stack](https://huggingface.co/datasets/bigcode/the-stack)|114.1B
+|Chinese|[Wikipedia](https://huggingface.co/datasets/bigcode/the-stack)|0.8B
+|Korean|[Wikipedia](https://huggingface.co/datasets/bigcode/the-stack)|0.3B
+### Post-training
+We have fine-tuned the pre-trained checkpoint with supervised fine-tuning and further aligned it with Direct Preference Optimization.
+#### Supervised Fine-tuning
+The datasets used for supervised fine-tuning are as follows:
+| Language | Dataset | Description |
+|:---|:---|:---|
+|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 Japanese instruction dataset. |
+|        |[answer-carefully-002](https://liat-aip.sakura.ne.jp/wp/answercarefully-dataset/)| A manually constructed instruction dataset focusing on LLMs' safety. |
+|        |ichikara-instruction-format| A small subset of the ichikara-instruction dataset, edited with some constraints on the output format. |
+|        |[AutoMultiTurnByCalm3-22B](https://huggingface.co/datasets/kanhatakeyama/AutoMultiTurnByCalm3-22B)| A synthetic instruction dataset. |
+|        |[ramdom-to-fixed-multiturn-Calm3](https://huggingface.co/datasets/kanhatakeyama/ramdom-to-fixed-multiturn-Calm3)| A synthetic instruction dataset. |
+|        |[wizardlm8x22b-logical-math-coding-sft-ja](https://huggingface.co/datasets/kanhatakeyama/wizardlm8x22b-logical-math-coding-sft-ja)| A synthetic instruction dataset. We used a sampled subset. |
+|        |[wizardlm8x22b-logical-math-coding-sft_additional-ja](https://huggingface.co/datasets/kanhatakeyama/wizardlm8x22b-logical-math-coding-sft_additional-ja)| A synthetic instruction dataset. We used a sampled subset. |
+|        |[magpie-sft-v1.0](https://huggingface.co/datasets/llm-jp/magpie-sft-v1.0)| A synthetic instruction dataset we created. |
+|English|[Daring-Anteater](https://huggingface.co/datasets/nvidia/Daring-Anteater)| - |
+|        |[FLAN](https://huggingface.co/datasets/Open-Orca/FLAN) | We used a sampled subset. |
+|Japanese & English|[Synthetic-JP-EN-Coding-Dataset-567k](https://huggingface.co/datasets/Aratako/Synthetic-JP-EN-Coding-Dataset-567k)| A synthetic instruction dataset. We used a sampled subset. |
+#### Direct Preference Optimization
+We used synthetic preference data to improve both the helpfulness and harmlessness of the LLM. The datasets will be made available soon.
+## Evaluation
+### llm-jp-eval (v1.4.1)
+We evaluated the models using 100 examples from the dev split. Note that we skipped the CG (Code Generation) task.
+| Model name | average | EL | FA | HE | MC | MR | MT | NLI | QA | RC | SUM |
+| :--- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
+| [llm-jp-3-172b-beta1](https://huggingface.co/llm-jp/llm-jp-3-172b-beta1) | 0.5174 | 0.4460 | 0.2556 | 0.3700 | 0.6400 | 0.6100 | 0.8265 | 0.5600 | 0.5720 | 0.8505 | 0.0434 |
+| [llm-jp-3-172b-beta1-instruct](https://huggingface.co/llm-jp/llm-jp-3-172b-beta1-instruct) | 0.5700 | 0.4306 | 0.2292 | 0.4350 | 0.8433 | 0.6200 | 0.8228 | 0.6820 | 0.5873 | 0.8964 | 0.1529 |
+| [llm-jp-3-172b-beta2](https://huggingface.co/llm-jp/llm-jp-3-172b-beta2) | 0.5422 | 0.3337 | 0.2725 | 0.4700 | 0.7767 | 0.6900 | 0.8283 | 0.5960 | 0.6133 | 0.8380 | 0.0037 |
+| [llm-jp-3-172b-beta2-instruct2](https://huggingface.co/llm-jp/llm-jp-3-172b-beta2-instruct2) | 0.6022 | 0.5470 | 0.2665 | 0.5100 | 0.8600 | 0.7000 | 0.8392 | 0.6800 | 0.6346 | 0.8770 | 0.1076 |
+| [llm-jp-3-172b](https://huggingface.co/llm-jp/llm-jp-3-172b) | 0.5431 | 0.4077 | 0.2662 | 0.5150 | 0.7633 | 0.6700 | 0.8227 | 0.5740 | 0.5686 | 0.8289 | 0.0148 |
+| [llm-jp-3-172b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-172b-instruct3) | 0.6130 | 0.5173 | 0.2711 | 0.5700 | 0.8733 | 0.7300 | 0.8437 | 0.7280 | 0.6012 | 0.8829 | 0.1121 |
+### Japanese MT Bench
+We evaluated the models using `gpt-4-0613`. Please see the [codes](https://github.com/wandb/llm-leaderboard/tree/g-leaderboard) for details.
+| Model name | average | coding | extraction | humanities | math | reasoning | roleplay | stem | writing |
+| :--- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
+| [llm-jp-3-172b-beta1-instruct](https://huggingface.co/llm-jp/llm-jp-3-172b-beta1-instruct) | 5.14 | 2.90 | 5.30 | 8.80 | 2.15 | 2.45 | 6.95 | 7.45 | 5.15 |
+| [llm-jp-3-172b-beta2-instruct2](https://huggingface.co/llm-jp/llm-jp-3-172b-beta2-instruct2) | 6.72 | 4.10 | 6.90 | 7.60 | 4.00 | 6.35 | 8.70 | 7.95 | 8.15 |
+| [llm-jp-3-172b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-beta2-instruct3) | 7.57 | 4.85 | 8.55 | 9.56 | 3.75 | 7.6 | 8.1 | 8.95 | 9.2 |
+## Risks and Limitations
+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.
+## Send Questions to
+llm-jp(at)nii.ac.jp
+## License
+See the [LICENSE](LICENSE_ja) file.
+## Model Card Authors
+*The names are listed in alphabetical order.*
+Hirokazu Kiyomaru and Takashi Kodama.