Instructions to use GreatCaptainNemo/ProLLaMA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GreatCaptainNemo/ProLLaMA with Transformers:

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

pipe = pipeline("text-generation", model="GreatCaptainNemo/ProLLaMA")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("GreatCaptainNemo/ProLLaMA")
model = AutoModelForCausalLM.from_pretrained("GreatCaptainNemo/ProLLaMA")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use GreatCaptainNemo/ProLLaMA with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/GreatCaptainNemo/ProLLaMA

SGLang

How to use GreatCaptainNemo/ProLLaMA 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 "GreatCaptainNemo/ProLLaMA" \
    --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": "GreatCaptainNemo/ProLLaMA",
		"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 "GreatCaptainNemo/ProLLaMA" \
        --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": "GreatCaptainNemo/ProLLaMA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use GreatCaptainNemo/ProLLaMA with Docker Model Runner:
```
docker model run hf.co/GreatCaptainNemo/ProLLaMA
```

Add `library_name` and `pipeline_tag` to model card

by nielsr HF Staff - opened Sep 16, 2025

base: refs/heads/main

←

from: refs/pr/7

Discussion Files changed

-2

Files changed (1) hide show

README.md +7 -2

README.md CHANGED Viewed

@@ -1,6 +1,9 @@
 ---
 license: apache-2.0
 ---
 # ProLLaMA: A Protein Large Language Model for Multi-Task Protein Language Processing
 [Paper on arxiv](https://arxiv.org/abs/2402.16445) for more information
@@ -106,7 +109,8 @@ if __name__ == '__main__':
                 s = generation_output[0]
                 output = tokenizer.decode(s,skip_special_tokens=True)
                 print("Output:",output)
-                print("\n")
         else:
             outputs=[]
             with open(args.input_file, 'r') as f:
@@ -126,7 +130,8 @@ if __name__ == '__main__':
                 output = tokenizer.decode(s,skip_special_tokens=True)
                 outputs.append(output)
             with open(args.output_file,'w') as f:
-                f.write("\n".join(outputs))
             print("All the outputs have been saved in",args.output_file)
 ```

 ---
 license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
 ---
 # ProLLaMA: A Protein Large Language Model for Multi-Task Protein Language Processing
 [Paper on arxiv](https://arxiv.org/abs/2402.16445) for more information
                 s = generation_output[0]
                 output = tokenizer.decode(s,skip_special_tokens=True)
                 print("Output:",output)
+                print("
+")
         else:
             outputs=[]
             with open(args.input_file, 'r') as f:
                 output = tokenizer.decode(s,skip_special_tokens=True)
                 outputs.append(output)
             with open(args.output_file,'w') as f:
+                f.write("
+".join(outputs))
             print("All the outputs have been saved in",args.output_file)
 ```