Instructions to use naniltx/codonGPT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use naniltx/codonGPT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="naniltx/codonGPT")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("naniltx/codonGPT") model = AutoModelForCausalLM.from_pretrained("naniltx/codonGPT") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use naniltx/codonGPT with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "naniltx/codonGPT" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "naniltx/codonGPT", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/naniltx/codonGPT
- SGLang
How to use naniltx/codonGPT 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 "naniltx/codonGPT" \ --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": "naniltx/codonGPT", "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 "naniltx/codonGPT" \ --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": "naniltx/codonGPT", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use naniltx/codonGPT with Docker Model Runner:
docker model run hf.co/naniltx/codonGPT
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### Model Description
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This repository ships the CodonGPT model checkpoint together with its codon-level Tokenizer and the SynonymousLogitProcessor, so you can reproduce the constrained generation workflow straight from
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the model card. The model was pretrained on Ensembl CDS sequences with a GPT-2–style decoder, learns synonymous structure and CAI/GC biases, and is optimized for codon-
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aware sequence design. After pulling the snapshot, load the tokenizer and processor from the repo files to enable synonym-aware decoding that encourages biologically equivalent alternatives while preserving
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sequence-level realism.
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### Model Description
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This repository ships the CodonGPT model checkpoint together with its Custom codon-level Tokenizer and the Custom SynonymousLogitProcessor, so you can reproduce the constrained generation workflow straight from
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the model card. The model was pretrained on Ensembl CDS sequences with a GPT-2–style decoder, learns synonymous structure and CAI/GC biases, and is optimized for codon-
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aware sequence design. After pulling the snapshot, load the tokenizer and processor from the repo files to enable synonym-aware decoding that encourages biologically equivalent alternatives while preserving
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sequence-level realism.
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