Text Generation
Transformers
PyTorch
English
crystalcoder
llm
code
custom_code
Eval Results (legacy)
Instructions to use LLM360/CrystalChat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LLM360/CrystalChat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="LLM360/CrystalChat", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("LLM360/CrystalChat", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use LLM360/CrystalChat with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "LLM360/CrystalChat" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "LLM360/CrystalChat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/LLM360/CrystalChat
- SGLang
How to use LLM360/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 "LLM360/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": "LLM360/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 "LLM360/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": "LLM360/CrystalChat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use LLM360/CrystalChat with Docker Model Runner:
docker model run hf.co/LLM360/CrystalChat
| { | |
| "additional_special_tokens": [ | |
| "<unk>", | |
| "<s>", | |
| "</s>", | |
| "<|fim_prefix|>", | |
| "<|fim_middle|>", | |
| "<|fim_suffix|>", | |
| "<|fim_pad|>", | |
| "<|filename|>", | |
| "<|gh_stars|>", | |
| "<|issue_start|>", | |
| "<|issue_comment|>", | |
| "<|issue_closed|>", | |
| "<|jupyter_start|>", | |
| "<|jupyter_text|>", | |
| "<|jupyter_code|>", | |
| "<|jupyter_output|>", | |
| "<|empty_output|>", | |
| "<|commit_before|>", | |
| "<|commit_msg|>", | |
| "<|commit_after|>", | |
| "<|reponame|>", | |
| "<|im_start|>", | |
| "<|im_end|>", | |
| "<|sys_start|>", | |
| "<|sys_end|>" | |
| ], | |
| "bos_token": "<s>", | |
| "eos_token": "</s>", | |
| "unk_token": "<unk>" | |
| } | |