Text Generation
Transformers
Safetensors
gpt2
biology
plasmid
dna
synthetic-biology
text-generation-inference
Instructions to use UCL-CSSB/PlasmidGPT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UCL-CSSB/PlasmidGPT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="UCL-CSSB/PlasmidGPT")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("UCL-CSSB/PlasmidGPT") model = AutoModelForCausalLM.from_pretrained("UCL-CSSB/PlasmidGPT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use UCL-CSSB/PlasmidGPT with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "UCL-CSSB/PlasmidGPT" # 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", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/UCL-CSSB/PlasmidGPT
- SGLang
How to use UCL-CSSB/PlasmidGPT 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" \ --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", "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" \ --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", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use UCL-CSSB/PlasmidGPT with Docker Model Runner:
docker model run hf.co/UCL-CSSB/PlasmidGPT
Upload 8 files
Browse files- .gitattributes +1 -0
- config.json +38 -0
- generation_config.json +7 -0
- model.safetensors +3 -0
- special_tokens_map.json +23 -0
- test_generation.py +35 -0
- tokenizer.json +0 -0
- tokenizer_config.json +67 -0
.gitattributes
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model.safetensors filter=lfs diff=lfs merge=lfs -text
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config.json
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{
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"activation_function": "gelu_new",
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"architectures": [
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"GPT2LMHeadModel"
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],
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"attn_pdrop": 0.1,
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"bos_token_id": 50256,
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"embd_pdrop": 0.1,
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"eos_token_id": 2,
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"initializer_range": 0.02,
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"layer_norm_epsilon": 1e-05,
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"model_type": "gpt2",
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"n_ctx": 2048,
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"n_embd": 768,
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"n_head": 12,
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"n_inner": null,
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"n_layer": 12,
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"n_positions": 2048,
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"reorder_and_upcast_attn": false,
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"resid_pdrop": 0.1,
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"scale_attn_by_inverse_layer_idx": false,
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"scale_attn_weights": true,
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"summary_activation": null,
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"summary_first_dropout": 0.1,
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"summary_proj_to_labels": true,
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"summary_type": "cls_index",
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"summary_use_proj": true,
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"task_specific_params": {
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"text-generation": {
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"do_sample": true,
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"max_length": 50
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}
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},
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"torch_dtype": "float32",
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"transformers_version": "4.55.4",
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"use_cache": true,
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"vocab_size": 30002
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}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 50256,
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"eos_token_id": 2,
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"pad_token_id": 3,
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"transformers_version": "4.55.4"
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:02365cb1aa0e86be0439567e7a8d15b51234dfdc051025ab1b2e4b157923debb
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size 438696576
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special_tokens_map.json
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{
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"bos_token": {
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"content": "<s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"eos_token": {
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"content": "[SEP]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"pad_token": {
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"content": "[PAD]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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}
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}
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test_generation.py
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
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device = 'cuda' if torch.cuda.is_available() else 'cpu'
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print(f"Using device: {device}")
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model = AutoModelForCausalLM.from_pretrained(
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"/Users/mcclainthiel/plasmidgpt-addgene-gpt2",
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trust_remote_code=True
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).to(device)
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model.eval()
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tokenizer = AutoTokenizer.from_pretrained(
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"/Users/mcclainthiel/plasmidgpt-addgene-gpt2",
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trust_remote_code=True
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)
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start_sequence = 'ATGGCTAGCGAATTCGGCGCGCCT'
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print(f"Start sequence: {start_sequence}\n")
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input_ids = tokenizer.encode(start_sequence, return_tensors='pt').to(device)
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outputs = model.generate(
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input_ids,
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max_length=300,
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num_return_sequences=1,
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temperature=1.0,
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do_sample=True,
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pad_token_id=tokenizer.pad_token_id,
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eos_token_id=tokenizer.eos_token_id
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)
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generated_sequence = tokenizer.decode(outputs[0], skip_special_tokens=True)
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print(f"Generated sequence:\n{generated_sequence}\n")
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print(f"Length: {len(generated_sequence)} bp")
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tokenizer.json
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The diff for this file is too large to render.
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tokenizer_config.json
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{
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"added_tokens_decoder": {
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"0": {
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"content": "[UNK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"1": {
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"content": "[CLS]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"2": {
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"content": "[SEP]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"3": {
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"content": "[PAD]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"4": {
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"content": "[MASK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"30000": {
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"content": "<s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"30001": {
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"content": "</s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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}
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},
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"bos_token": "<s>",
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"clean_up_tokenization_spaces": false,
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"eos_token": "[SEP]",
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"extra_special_tokens": {},
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": "[PAD]",
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"tokenizer_class": "PreTrainedTokenizerFast"
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}
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