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

nielsr

Sep 16, 2025

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.

Add `library_name` and `pipeline_tag` to model cardb729aeaf

GreatCaptainNemo changed pull request status to merged Sep 18, 2025

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