Instructions to use AntibodyGeneration/fine-tuned-progen2-medium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AntibodyGeneration/fine-tuned-progen2-medium with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="AntibodyGeneration/fine-tuned-progen2-medium")

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("AntibodyGeneration/fine-tuned-progen2-medium", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AntibodyGeneration/fine-tuned-progen2-medium with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AntibodyGeneration/fine-tuned-progen2-medium"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AntibodyGeneration/fine-tuned-progen2-medium",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/AntibodyGeneration/fine-tuned-progen2-medium

SGLang

How to use AntibodyGeneration/fine-tuned-progen2-medium 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 "AntibodyGeneration/fine-tuned-progen2-medium" \
    --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": "AntibodyGeneration/fine-tuned-progen2-medium",
		"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 "AntibodyGeneration/fine-tuned-progen2-medium" \
        --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": "AntibodyGeneration/fine-tuned-progen2-medium",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use AntibodyGeneration/fine-tuned-progen2-medium with Docker Model Runner:
```
docker model run hf.co/AntibodyGeneration/fine-tuned-progen2-medium
```

rsuresh2011 commited on Dec 3, 2023

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@@ -1,6 +1,5 @@
 ---
 # Model Card for Antibody Generator (Based on ProGen2)
-inference: false
 ## Model Details
 - Model Name: Antibody Generator
@@ -9,8 +8,7 @@ inference: false
 - Model Developer: Joesph Roberts, David Noble, Rahul Suresh, Neel Patel
 - Model Type: Protein Generation, based on the ProGen2 architecture.
 - License: Apache 2.0
-- Code Repository: https://github.com/joethequant/docker_protein_generator, https://github.com/joethequant/docker_streamlit_antibody_protein_generation
-- Baseline Model Reference: [ProGen2 Paper](https://arxiv.org/pdf/2206.13517.pdf)
 ## Model Overview
 The Antibody Generator is a specialized protein generation model developed for creating therapeutic antibodies. It is based on the ProGen2 model, an advanced language model developed by Salesforce. ProGen2, an enhancement of the original ProGen model launched in 2020, is pre-trained on a vast dataset of over 280 million protein sequences. With up to 6.4B parameters, ProGen2 demonstrates state-of-the-art performance in generating novel, viable protein sequences and predicting protein fitness.
@@ -71,3 +69,5 @@ For questions or feedback regarding this model, please contact [XYZ].

 ---
 # Model Card for Antibody Generator (Based on ProGen2)
 ## Model Details
 - Model Name: Antibody Generator
 - Model Developer: Joesph Roberts, David Noble, Rahul Suresh, Neel Patel
 - Model Type: Protein Generation, based on the ProGen2 architecture.
 - License: Apache 2.0
+- Code Repository: https://github.com/joethequant/docker_protein_generator
 ## Model Overview
 The Antibody Generator is a specialized protein generation model developed for creating therapeutic antibodies. It is based on the ProGen2 model, an advanced language model developed by Salesforce. ProGen2, an enhancement of the original ProGen model launched in 2020, is pre-trained on a vast dataset of over 280 million protein sequences. With up to 6.4B parameters, ProGen2 demonstrates state-of-the-art performance in generating novel, viable protein sequences and predicting protein fitness.