Text Generation
Transformers
Safetensors
gpt2
biology
plasmid
dna
synthetic-biology
grpo
reinforcement-learning
text-generation-inference
Instructions to use UCL-CSSB/PlasmidGPT-GRPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UCL-CSSB/PlasmidGPT-GRPO with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="UCL-CSSB/PlasmidGPT-GRPO")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("UCL-CSSB/PlasmidGPT-GRPO") model = AutoModelForCausalLM.from_pretrained("UCL-CSSB/PlasmidGPT-GRPO") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use UCL-CSSB/PlasmidGPT-GRPO with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "UCL-CSSB/PlasmidGPT-GRPO" # 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-GRPO", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/UCL-CSSB/PlasmidGPT-GRPO
- SGLang
How to use UCL-CSSB/PlasmidGPT-GRPO 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-GRPO" \ --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-GRPO", "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-GRPO" \ --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-GRPO", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use UCL-CSSB/PlasmidGPT-GRPO with Docker Model Runner:
docker model run hf.co/UCL-CSSB/PlasmidGPT-GRPO
Tweak README and open PR
#1
by McClain - opened
README.md
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Notes:
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- Use sampling (`temperature`, `top_p`) for diverse sequences; disable sampling for deterministic outputs.
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- Runs on CPU, CUDA, or Apple MPS depending on your PyTorch installation.
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Notes:
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- Use sampling (`temperature`, `top_p`) for diverse sequences; disable sampling for deterministic outputs.
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- Runs on CPU, CUDA, or Apple MPS depending on your PyTorch installation.
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Changelog:
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- Update weights to checkpoint-2300
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- Simplify README and add W&B run link
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