Instructions to use liujch1998/crystal-3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use liujch1998/crystal-3b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="liujch1998/crystal-3b")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("liujch1998/crystal-3b") model = AutoModelForSeq2SeqLM.from_pretrained("liujch1998/crystal-3b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use liujch1998/crystal-3b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "liujch1998/crystal-3b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "liujch1998/crystal-3b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/liujch1998/crystal-3b
- SGLang
How to use liujch1998/crystal-3b 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 "liujch1998/crystal-3b" \ --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": "liujch1998/crystal-3b", "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 "liujch1998/crystal-3b" \ --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": "liujch1998/crystal-3b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use liujch1998/crystal-3b with Docker Model Runner:
docker model run hf.co/liujch1998/crystal-3b
Download pytorch_model-00002-of-00002.bin from liujch1998/crystal-3b: direct link, hf CLI and curl.
- Browser
- Download file 905 MB
-
https://huggingface.co/liujch1998/crystal-3b/resolve/main/pytorch_model-00002-of-00002.bin
- Command line
-
hf download hf://liujch1998/crystal-3b/pytorch_model-00002-of-00002.bin
-
curl -L -o pytorch_model-00002-of-00002.bin https://huggingface.co/liujch1998/crystal-3b/resolve/main/pytorch_model-00002-of-00002.bin
905 MB
- Xet hash:
- c77a7ea4fe9309ec02c4c0c3179d356ad128ede255c32dc9c4a12e4c07de3a81
- Size of remote file:
- 905 MB
- SHA256:
- a1cc34ea5992c21a7d5e50033ac5a1c7637a2a296f5d471656cefa9e6e36827d
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