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
```

joethequant commited on Dec 7, 2023

Commit

174c105

1 Parent(s): 80c79c2

Update README.md

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added additional info

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README.md +49 -0

README.md CHANGED Viewed

@@ -62,6 +62,55 @@ Performance and analytics:
 ## How to Use
 Instructions on how to use the model, including example prompts and API documentation, are available in the [Code Repository](https://github.com/joethequant/docker_streamlit_antibody_protein_generation).
 ## Limitations and Future Work
 - Predictions require experimental validation for practical use.
 - Future improvements will focus on incorporating diverse training data and enhancing prediction accuracy for the efficacy of generated antibodies.

 ## How to Use
 Instructions on how to use the model, including example prompts and API documentation, are available in the [Code Repository](https://github.com/joethequant/docker_streamlit_antibody_protein_generation).
+### Example Code
+```python
+from models.progen.modeling_progen import ProGenForCausalLM
+import torch
+from tokenizers import Tokenizer
+import json
+# Define the model identifier from Hugging Face's model hub
+model_path = 'AntibodyGeneration/fine-tuned-progen2-small'
+# Load the model and tokenizer
+model = ProGenForCausalLM.from_pretrained(model_path)
+tokenizer = Tokenizer.from_file('tokenizer.json')
+# Define your sequence and other parameters
+target_sequence = 'MQIPQAPWPVVWAVLQLGWRPGWFLDSPDRPWNPPTFSPALLVVTEGDNATFTCSFSNTSESFVLNWYRMSPSNQTDKLAAFPEDRSQPGQDCRFRVTQLPNGRDFHMSVVRARRNDSGTYLCGAISLAPKAQIKESLRAELRVTERRAEVPTAHPSPSPRPAGQFQTLVVGVVGGLLGSLVLLVWVLAVICSRAARGTIGARRTGQPLKEDPSAVPVFSVDYGELDFQWREKTPEPPVPCVPEQTEYATIVFPSGMGTSSPARRGSADGPRSAQPLRPEDGHCSWPL'
+number_of_sequences = 2
+# Tokenize the sequence
+tokenized_sequence = tokenizer(target_sequence, return_tensors="pt")
+# Move model and tensors to CUDA if available
+device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+model = model.to(device)
+tokenized_sequence = tokenized_sequence.to(device)
+# Generate sequences
+with torch.no_grad():
+    output = model.generate(**tokenized_sequence, max_length=1024, pad_token_id=tokenizer.pad_token_id, do_sample=True, top_p=0.9, temperature=0.8, num_return_sequences=number_of_sequences)
+# Decoding the output to get generated sequences
+generated_sequences = [tokenizer.decode(output_seq, skip_special_tokens=True) for output_seq in output]
+```
+## Links:
+- [Huggingface Model Repository](https://huggingface.co/AntibodyGeneration)
+- [Web Demo](https://orca-app-ygzbp.ondigitalocean.app/Demo_Antibody_Generator)
+- [OpenSource RunPod Severless Rest API](https://github.com/joethequant/docker_protein_generator)
+- [The Code for this App](https://github.com/joethequant/docker_streamlit_antibody_protein_generation)
+## Additional Resources and Links
+- [Progen Foundation Models](https://github.com/salesforce/progen)
+- [ANARCI Github](https://github.com/oxpig/ANARCI)
+- [ANARCI Webserver](http://opig.stats.ox.ac.uk/webapps/anarci/)
+- [TAP: Therapeutic Antibody Profiler](https://opig.stats.ox.ac.uk/webapps/sabdab-sabpred/sabpred/tap)
+- [ESM Fold](https://esmatlas.com/resources?action=fold)
 ## Limitations and Future Work
 - Predictions require experimental validation for practical use.
 - Future improvements will focus on incorporating diverse training data and enhancing prediction accuracy for the efficacy of generated antibodies.