Instructions to use deepcs233/VisCoT-7b-336 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepcs233/VisCoT-7b-336 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="deepcs233/VisCoT-7b-336")# Load model directly from transformers import AutoProcessor, AutoModelForCausalLM processor = AutoProcessor.from_pretrained("deepcs233/VisCoT-7b-336") model = AutoModelForCausalLM.from_pretrained("deepcs233/VisCoT-7b-336") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use deepcs233/VisCoT-7b-336 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "deepcs233/VisCoT-7b-336" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "deepcs233/VisCoT-7b-336", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/deepcs233/VisCoT-7b-336
- SGLang
How to use deepcs233/VisCoT-7b-336 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 "deepcs233/VisCoT-7b-336" \ --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": "deepcs233/VisCoT-7b-336", "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 "deepcs233/VisCoT-7b-336" \ --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": "deepcs233/VisCoT-7b-336", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use deepcs233/VisCoT-7b-336 with Docker Model Runner:
docker model run hf.co/deepcs233/VisCoT-7b-336
- Xet hash:
- f7a2b0be7b23e3e6a022c391adeb5da62899ae161c71f07bb4a2b5749b551a2d
- Size of remote file:
- 6.14 kB
- SHA256:
- de3f2bb4ca6e3cd26c871ec9746e21f1ea798fa8f8a227f9897f138c7c11bb00
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