Text Generation
Transformers
PyTorch
English
crystalcoder
llm
code
custom_code
Eval Results (legacy)
Instructions to use IFM/CrystalChat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IFM/CrystalChat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="IFM/CrystalChat", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("IFM/CrystalChat", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use IFM/CrystalChat with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "IFM/CrystalChat" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "IFM/CrystalChat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/IFM/CrystalChat
- SGLang
How to use IFM/CrystalChat 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/CrystalChat" \ --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/CrystalChat", "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/CrystalChat" \ --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/CrystalChat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use IFM/CrystalChat with Docker Model Runner:
docker model run hf.co/IFM/CrystalChat
Update README.md
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README.md
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pipeline_tag: text-generation
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library_name: transformers
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widget:
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- example_title: Write python function and explain
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text: >-
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<s> <|sys_start|> You are an AI assistant. You will be given a task. You
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pipeline_tag: text-generation
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library_name: transformers
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widget:
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- example_title: Explain the name of CrystalChat
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text: >-
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<s> <|sys_start|> You are an AI assistant. You will be given a task. You must generate a detailed \
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and long answer. <|sys_end|> <|im_start|> Give me a one-sentence description of crystal. And tell me what \
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might the name "Crystal" mean for a transparant and open-source language model?
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output:
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text: >-
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A crystal is an ordered, hexagonal lattice of atoms that displays a unique crystal structure resulting in transparency and diverse physical and chemical properties, and as a transparent and open-source language model, "Crystal" might represent transparency and accessibility by enabling users to understand the inner workings of the model.
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- example_title: Write python function and explain
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text: >-
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<s> <|sys_start|> You are an AI assistant. You will be given a task. You
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