Instructions to use IFM/Crystal with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IFM/Crystal with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="IFM/Crystal", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("IFM/Crystal", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use IFM/Crystal with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "IFM/Crystal" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "IFM/Crystal", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/IFM/Crystal
- SGLang
How to use IFM/Crystal 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 "IFM/Crystal" \ --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": "IFM/Crystal", "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 "IFM/Crystal" \ --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": "IFM/Crystal", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use IFM/Crystal with Docker Model Runner:
docker model run hf.co/IFM/Crystal
Download pytorch_model-00001-of-00004.bin from IFM/Crystal: direct link, hf CLI and curl.
- Browser
- Download file 7.81 GB
-
https://huggingface.co/IFM/Crystal/resolve/CrystalCoder_phase2_checkpoint_214387/pytorch_model-00001-of-00004.bin
- Command line
-
hf download hf://IFM/Crystal@CrystalCoder_phase2_checkpoint_214387/pytorch_model-00001-of-00004.bin
-
curl -L -o pytorch_model-00001-of-00004.bin https://huggingface.co/IFM/Crystal/resolve/CrystalCoder_phase2_checkpoint_214387/pytorch_model-00001-of-00004.bin
7.81 GB
- Xet hash:
- 183a1d65d7d3cce144598162ecfc571b12b853fdd7b3072fc431d446712f5121
- Size of remote file:
- 7.81 GB
- SHA256:
- ca36dfec3827f45660d2a24e30d29a51d8bb72dd4d8ff878f246636acf16aba9
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