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
```

nielsr HF Staff commited on Sep 16, 2025

Commit

b729aea

verified ·

1 Parent(s): c7adc64

Add `library_name` and `pipeline_tag` to model card

Browse files

This PR improves the discoverability and usability of the ProLLaMA model by adding the `library_name: transformers` and `pipeline_tag: text-generation` to the model card's metadata.

- The `library_name` tag ensures that the model is correctly recognized as a Transformers-compatible model, enabling an automated code snippet on the Hub page for easy usage.
- The `pipeline_tag` helps users find this model when searching for text generation models, specifically in the context of protein language processing.

The existing content of the model card remains unchanged to reflect the majority consensus among colleagues.

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)
 ```