Text Generation
Transformers
PyTorch
English
prot2text
feature-extraction
Causal Language Modeling
GPT2
ESM2
Proteins
GNN
custom_code
Instructions to use habdine/Prot2Text-Small-v1-0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use habdine/Prot2Text-Small-v1-0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="habdine/Prot2Text-Small-v1-0", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("habdine/Prot2Text-Small-v1-0", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use habdine/Prot2Text-Small-v1-0 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "habdine/Prot2Text-Small-v1-0" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "habdine/Prot2Text-Small-v1-0", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/habdine/Prot2Text-Small-v1-0
- SGLang
How to use habdine/Prot2Text-Small-v1-0 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 "habdine/Prot2Text-Small-v1-0" \ --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": "habdine/Prot2Text-Small-v1-0", "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 "habdine/Prot2Text-Small-v1-0" \ --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": "habdine/Prot2Text-Small-v1-0", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use habdine/Prot2Text-Small-v1-0 with Docker Model Runner:
docker model run hf.co/habdine/Prot2Text-Small-v1-0
- Xet hash:
- f3521e27500240376fed13446bb266e528aa86e9ba4f4ec30f57bbc5a460fb79
- Size of remote file:
- 1.03 GB
- SHA256:
- e36c10c23704ecd2c2b14f28ad2bf574536d699251d04dceefdd16fd49449105
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