Instructions to use UCL-CSSB/PlasmidGPT-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UCL-CSSB/PlasmidGPT-SFT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="UCL-CSSB/PlasmidGPT-SFT")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("UCL-CSSB/PlasmidGPT-SFT") model = AutoModelForCausalLM.from_pretrained("UCL-CSSB/PlasmidGPT-SFT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use UCL-CSSB/PlasmidGPT-SFT with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "UCL-CSSB/PlasmidGPT-SFT" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "UCL-CSSB/PlasmidGPT-SFT", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/UCL-CSSB/PlasmidGPT-SFT
- SGLang
How to use UCL-CSSB/PlasmidGPT-SFT 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 "UCL-CSSB/PlasmidGPT-SFT" \ --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": "UCL-CSSB/PlasmidGPT-SFT", "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 "UCL-CSSB/PlasmidGPT-SFT" \ --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": "UCL-CSSB/PlasmidGPT-SFT", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use UCL-CSSB/PlasmidGPT-SFT with Docker Model Runner:
docker model run hf.co/UCL-CSSB/PlasmidGPT-SFT
Replace model.safetensors with the canonical fine-tuned weights
Browse filesThe previous model.safetensors (sha 437b7ecef...) was structurally a Base
model copy: of 148 transformer-body tensors, 147 were bytewise-identical to
UCL-CSSB/PlasmidGPT, and only the embedding/lm_head pair differed. Anyone
loading this repo via AutoModelForCausalLM.from_pretrained got back
essentially Base, masking the real fine-tune.
The canonical weights were sitting under the non-standard filename
PlasmidGPT Finetuned Model.safetensors (sha 857d91938...). This commit
promotes those to the standard model.safetensors filename and removes the
non-standard duplicate.
End state: one model.safetensors in the repo, sha 857d91938..., containing
the real fine-tuned weights (148 of 148 layers differ from Base, mean
per-parameter diff 0.0037).
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