license: apache-2.0
language:
- en
- ja
programming_language:
- C
- C++
- C#
- Go
- Java
- JavaScript
- Lua
- PHP
- Python
- Ruby
- Rust
- Scala
- TypeScript
library_name: transformers
pipeline_tag: text-generation
inference: false
llm-jp-3-172b-alpha1-instruct
This repository provides large language models developed by the Research and Development Center for Large Language Models at the National Institute of Informatics.
The development was partially supported by GENIAC.
| Model Variants |
|---|
| llm-jp-3-172b-alpha1 |
| llm-jp-3-172b-alpha1-instruct |
| llm-jp-3-172b-alpha2 |
| llm-jp-3-172b-alpha2-instruct |
| llm-jp-3-172b-beta1 |
| llm-jp-3-172b-beta1-instruct |
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
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("llm-jp/llm-jp-3-172b-alpha1-instruct")
model = AutoModelForCausalLM.from_pretrained("llm-jp/llm-jp-3-172b-alpha1-instruct", 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::
- alpha1: 0.7T
- alpha2: 1.4T
- beta1: 0.7T
| Params | Layers | Hidden size | Heads | Context length |
|---|---|---|---|---|
| 172b | 96 | 12288 | 96 | 4096 |
Tokenizer
The tokenizer of this model is based on huggingface/tokenizers Unigram byte-fallback model.
The vocabulary entries were converted from llm-jp-tokenizer v3.0.
Please refer to README.md 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 | 2.6B |
| Common Crawl | 762.8B | |
| WARP/PDF | 282.1B | |
| WARP/HTML | 2.7B | |
| Kaken | 1.8B | |
| English | Wikipedia | 4.7B |
| Dolma/CC-head | 608.5B | |
| Dolma/C4 | 181.6B | |
| Dolma/Reddit | 83.1B | |
| Dolma/PeS2o | 62.9B | |
| Dolma/Gutenberg | 5.5B | |
| Dolma/Wiki | 3.9B | |
| Code | The Stack | 114.1B |
| Chinese | Wikipedia | 0.8B |
| Korean | Wikipedia | 0.3B |
Instruction tuning
The models have been fine-tuned on the following datasets.
| Language | Dataset | description |
|---|---|---|
| Japanese | ichikara-instruction-004-002 | A manually constructed Japanese instruction dataset |
| answer-carefully-001 | A manually constructed Japanese instruction dataset focusing on LLMs' safety | |
| databricks-dolly-15k-ja | databricks-dolly-15k translated into Japanese using DeepL | |
| oasst1-21k-ja | A subset of oasst1 translated into Japanese using DeepL | |
| oasst2-33k-ja | A subset of oasst2 translated into Japanese using DeepL | |
| aya-dataset-ja | A Japanese subset of aya_dataset | |
| ichikara-instruction-format | A small amount of instruction dataset edited from ichikara-instruction, with some constraints on the output format. | |
| English | databricks-dolly-15k | - |
| oasst1-21k-en | A subset of oasst1 | |
| oasst2-33k-en | A subset of oasst2 | |
| Daring-Anteater | - | |
| FLAN | We used sampled one. |
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
Model Card Authors
The names are listed in alphabetical order.
Hirokazu Kiyomaru and Takashi Kodama.