Instructions to use michaelfeil/ct2fast-mpt-30b-chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use michaelfeil/ct2fast-mpt-30b-chat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="michaelfeil/ct2fast-mpt-30b-chat", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("michaelfeil/ct2fast-mpt-30b-chat", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("michaelfeil/ct2fast-mpt-30b-chat", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use michaelfeil/ct2fast-mpt-30b-chat with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "michaelfeil/ct2fast-mpt-30b-chat" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "michaelfeil/ct2fast-mpt-30b-chat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/michaelfeil/ct2fast-mpt-30b-chat
- SGLang
How to use michaelfeil/ct2fast-mpt-30b-chat 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 "michaelfeil/ct2fast-mpt-30b-chat" \ --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": "michaelfeil/ct2fast-mpt-30b-chat", "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 "michaelfeil/ct2fast-mpt-30b-chat" \ --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": "michaelfeil/ct2fast-mpt-30b-chat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use michaelfeil/ct2fast-mpt-30b-chat with Docker Model Runner:
docker model run hf.co/michaelfeil/ct2fast-mpt-30b-chat
- Xet hash:
- 16fb1d383002ef3215eb6d7580f8ea3d9269eb5f562e242e7d5e6943a0310221
- Size of remote file:
- 30 GB
- SHA256:
- c63c1de64f18b04783aed6c99e29328dea1c5998abf62195d4765c2efe70e494
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