Text Generation
Transformers
Safetensors
English
plasmid_lm
biology
genomics
plasmid
dna
causal-lm
synthetic-biology
custom_code
Instructions to use McClain/PlasmidLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use McClain/PlasmidLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="McClain/PlasmidLM", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("McClain/PlasmidLM", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use McClain/PlasmidLM with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "McClain/PlasmidLM" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "McClain/PlasmidLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/McClain/PlasmidLM
- SGLang
How to use McClain/PlasmidLM 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 "McClain/PlasmidLM" \ --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": "McClain/PlasmidLM", "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 "McClain/PlasmidLM" \ --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": "McClain/PlasmidLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use McClain/PlasmidLM with Docker Model Runner:
docker model run hf.co/McClain/PlasmidLM
Upload tokenization_plasmid_lm.py with huggingface_hub
Browse files- tokenization_plasmid_lm.py +3 -16
tokenization_plasmid_lm.py
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@@ -56,28 +56,15 @@ class PlasmidLMTokenizer(PreTrainedTokenizer):
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if tok in self._vocab:
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special_kwargs[name] = tok
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super().__init__(**special_kwargs, **kwargs)
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@property
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def vocab_size(self) -> int:
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return len(self._vocab)
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@property
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def pad_token_id(self) -> int:
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return self._vocab.get("<PAD>", 0)
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@property
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def bos_token_id(self) -> int:
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return self._vocab.get("<BOS>", 1)
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@property
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def eos_token_id(self) -> int:
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return self._vocab.get("<EOS>", 2)
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@property
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def sep_token_id(self) -> int:
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return self._vocab.get("<SEP>", 3)
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def get_vocab(self) -> dict:
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return dict(self._vocab)
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if tok in self._vocab:
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special_kwargs[name] = tok
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# Disable automatic BOS/EOS insertion (transformers 5.x); prompts
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# already contain <BOS>...<SEP> and the model generates <EOS> itself.
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kwargs.setdefault("special_tokens_pattern", "none")
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super().__init__(**special_kwargs, **kwargs)
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@property
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def vocab_size(self) -> int:
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return len(self._vocab)
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def get_vocab(self) -> dict:
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return dict(self._vocab)
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