Text Generation
Transformers
Safetensors
gpt_neox
science
antibodies
protein
drug discovery
text-generation-inference
How to use from
SGLangUse 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 "opig/p-IgGen" \
--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": "opig/p-IgGen",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'Quick Links
Model Card for Model ID
Auto-regressive protein language model for paired antibody library generation.
Pretrained on unpaired heavy and light chain sequences from the Observed Antibody Space (OAS), and finetuned on paired sequences.
Getting Started?
This model was featured in the Hugging Face blog How to Train an Antibody Developability Model (found here: https://huggingface.co/blog/ginkgo-datapoints/making-antibody-embeddings-and-predictions). Sample code on how to get started using this model can be found in the blog.
Model Details
Model Description
- Funded by: EPSRC, AstraZeneca
- License: BSD-3-Clause license
Model Sources
Note: This is a duplicate of Ollie Turnbull's model repo, found here: https://huggingface.co/ollieturnbull/p-IgGen
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Install from pip and serve model
# Install SGLang from pip: pip install sglang# Start the SGLang server: python3 -m sglang.launch_server \ --model-path "opig/p-IgGen" \ --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": "opig/p-IgGen", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'