Instructions to use elyza/ELYZA-japanese-Llama-2-13b-fast with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use elyza/ELYZA-japanese-Llama-2-13b-fast with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="elyza/ELYZA-japanese-Llama-2-13b-fast")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("elyza/ELYZA-japanese-Llama-2-13b-fast") model = AutoModelForCausalLM.from_pretrained("elyza/ELYZA-japanese-Llama-2-13b-fast", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use elyza/ELYZA-japanese-Llama-2-13b-fast with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "elyza/ELYZA-japanese-Llama-2-13b-fast" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "elyza/ELYZA-japanese-Llama-2-13b-fast", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/elyza/ELYZA-japanese-Llama-2-13b-fast
- SGLang
How to use elyza/ELYZA-japanese-Llama-2-13b-fast 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 "elyza/ELYZA-japanese-Llama-2-13b-fast" \ --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": "elyza/ELYZA-japanese-Llama-2-13b-fast", "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 "elyza/ELYZA-japanese-Llama-2-13b-fast" \ --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": "elyza/ELYZA-japanese-Llama-2-13b-fast", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use elyza/ELYZA-japanese-Llama-2-13b-fast with Docker Model Runner:
docker model run hf.co/elyza/ELYZA-japanese-Llama-2-13b-fast
Download pytorch_model-00001-of-00003.bin from elyza/ELYZA-japanese-Llama-2-13b-fast: direct link, hf CLI and curl.
- Browser
- Download file 9.97 GB
-
https://huggingface.co/elyza/ELYZA-japanese-Llama-2-13b-fast/resolve/main/pytorch_model-00001-of-00003.bin
- Command line
-
hf download hf://elyza/ELYZA-japanese-Llama-2-13b-fast/pytorch_model-00001-of-00003.bin
-
curl -L -o pytorch_model-00001-of-00003.bin https://huggingface.co/elyza/ELYZA-japanese-Llama-2-13b-fast/resolve/main/pytorch_model-00001-of-00003.bin
9.97 GB
- Xet hash:
- 084e05c3cc6881fc472bb318d9d753de673078ec36a93406bff3adc2641d99a4
- Size of remote file:
- 9.97 GB
- SHA256:
- 7c0cb4888658cee78935eb4e1af6de07cc47a8059eda45bf058ace32ea0f8681
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