llm-jp/llm-jp-3-3.7b-instruct3
llm-jp-3-3.7b-instruct3
LLM-jp-3 is the series of large language models developed by the Research and Development Center for Large Language Models at the National Institute of Informatics.
This repository provides llm-jp-3-3.7b-instruct3 model. For an overview of the LLM-jp-3 models across different parameter sizes, please refer to:
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-3-3.7b-instruct3")
model = AutoModelForCausalLM.from_pretrained("llm-jp/llm-jp-3-3.7b-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: 2.1T tokens
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.
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:
Direct Preference Optimization
The datasets used for supervised fine-tuning are as follows:
Evaluation
Detailed evaluation results are reported in this blog.
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.