Instructions to use llm-jp/llm-jp-1.3b-v1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use llm-jp/llm-jp-1.3b-v1.0 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="llm-jp/llm-jp-1.3b-v1.0")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("llm-jp/llm-jp-1.3b-v1.0")
model = AutoModelForCausalLM.from_pretrained("llm-jp/llm-jp-1.3b-v1.0")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use llm-jp/llm-jp-1.3b-v1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "llm-jp/llm-jp-1.3b-v1.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "llm-jp/llm-jp-1.3b-v1.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/llm-jp/llm-jp-1.3b-v1.0

SGLang

How to use llm-jp/llm-jp-1.3b-v1.0 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "llm-jp/llm-jp-1.3b-v1.0" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "llm-jp/llm-jp-1.3b-v1.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "llm-jp/llm-jp-1.3b-v1.0" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "llm-jp/llm-jp-1.3b-v1.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use llm-jp/llm-jp-1.3b-v1.0 with Docker Model Runner:
```
docker model run hf.co/llm-jp/llm-jp-1.3b-v1.0
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

llm-jp-1.3b-v1.0

This repository provides large language models developed by LLM-jp, a collaborative project launched in Japan.

Model Variant
Instruction models
llm-jp-13b-instruct-full-jaster-v1.0
llm-jp-13b-instruct-full-jaster-dolly-oasst-v1.0
llm-jp-13b-instruct-full-dolly-oasst-v1.0
llm-jp-13b-instruct-lora-jaster-v1.0
llm-jp-13b-instruct-lora-jaster-dolly-oasst-v1.0
llm-jp-13b-instruct-lora-dolly-oasst-v1.0


Pre-trained models
llm-jp-13b-v1.0
llm-jp-1.3b-v1.0
Checkpoints format: Hugging Face Transformers (Megatron-DeepSpeed format models are available here)

Required Libraries and Their Versions

torch>=2.0.0
transformers>=4.34.0
tokenizers>=0.14.0
accelerate==0.23.0

Usage

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("llm-jp/llm-jp-1.3b-v1.0")
model = AutoModelForCausalLM.from_pretrained("llm-jp/llm-jp-1.3b-v1.0", device_map="auto", torch_dtype=torch.float16)
text = "自然言語処理とは何か"
tokenized_input = tokenizer.encode(text, add_special_tokens=False, return_tensors="pt").to(model.device)
with torch.no_grad():
    output = model.generate(
        tokenized_input,
        max_new_tokens=20,
        do_sample=True,
        top_p=0.90,
        temperature=0.7,
    )[0]
print(tokenizer.decode(output))

Model Details

Model type: Transformer-based Language Model
Total seen tokens: 300B

Model	Params	Layers	Hidden size	Heads	Context length
13b model	13b	40	5120	40	2048
1.3b model	1.3b	24	2048	16	2048

Training

Pre-training:
- Hardware: 96 A100 40GB GPUs (mdx cluster)
- Software: Megatron-DeepSpeed
Instruction tuning:
- Hardware: 8 A100 40GB GPUs (mdx cluster)
- Software: TRL, PEFT, and DeepSpeed

Tokenizer

The tokenizer of this model is based on huggingface/tokenizers Unigram byte-fallback model. The vocabulary entries were converted from llm-jp-tokenizer v2.1 (50k). Please refer to README.md of llm-ja-tokenizer for details on the vocabulary construction procedure.

Model: Hugging Face Fast Tokenizer using Unigram byte-fallback model which requires tokenizers>=0.14.0
Training algorithm: SentencePiece Unigram byte-fallback
Training data: A subset of the datasets for model pre-training
Vocabulary size: 50,570 (mixed vocabulary of Japanese, English, and source code)

Datasets

Pre-training

The models have been pre-trained using a blend of the following datasets.

Language	Dataset	Tokens
Japanese	Wikipedia	1.5B
	mC4	136B
English	Wikipedia	5B
	The Pile	135B
Codes	The Stack	10B

The pre-training was continuously conducted using a total of 10 folds of non-overlapping data, each consisting of approximately 27-28B tokens. We finalized the pre-training with additional (potentially) high-quality 27B tokens data obtained from the identical source datasets listed above used for the 10-fold data.

Instruction tuning

The models have been fine-tuned on the following datasets.

Language	Dataset	description
Japanese	jaster	An automatically transformed data from the existing Japanese NLP datasets
	databricks-dolly-15k	A translated one by DeepL in LLM-jp
	OpenAssistant Conversations Dataset	A translated one by DeepL in LLM-jp

Evaluation

You can view the evaluation results of several LLMs on this leaderboard. We used llm-jp-eval for the evaluation.

Risks and Limitations

The models released here are still in the early stages of our research and development and have not been tuned to ensure outputs align with human intent and safety considerations.