Instructions to use llm-jp/llm-jp-3-7.2b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use llm-jp/llm-jp-3-7.2b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="llm-jp/llm-jp-3-7.2b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("llm-jp/llm-jp-3-7.2b") model = AutoModelForCausalLM.from_pretrained("llm-jp/llm-jp-3-7.2b") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use llm-jp/llm-jp-3-7.2b 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-3-7.2b" # 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-3-7.2b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/llm-jp/llm-jp-3-7.2b
- SGLang
How to use llm-jp/llm-jp-3-7.2b 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-3-7.2b" \ --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-3-7.2b", "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-3-7.2b" \ --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-3-7.2b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use llm-jp/llm-jp-3-7.2b with Docker Model Runner:
docker model run hf.co/llm-jp/llm-jp-3-7.2b
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README.md
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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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For models with different parameters, please refer to [LLM-jp-3 Pre-trained Models](https://huggingface.co/collections/llm-jp/llm-jp-3-pre-trained-models-672c6096472b65839d76a1fa) and [LLM-jp-3 Fine-tuned Models](https://huggingface.co/collections/llm-jp/llm-jp-3-fine-tuned-models-672c621db852a01eae939731).
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Checkpoints format: Hugging Face Transformers
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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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For LLM-jp-3 models with different parameters, please refer to [LLM-jp-3 Pre-trained Models](https://huggingface.co/collections/llm-jp/llm-jp-3-pre-trained-models-672c6096472b65839d76a1fa) and [LLM-jp-3 Fine-tuned Models](https://huggingface.co/collections/llm-jp/llm-jp-3-fine-tuned-models-672c621db852a01eae939731).
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Checkpoints format: Hugging Face Transformers
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