Text Generation
Transformers
Safetensors
mixtral
biology
protein-language-model
protein-generation
causal-lm
mixture-of-experts
dpo
alignment
protein-design
text-generation-inference
Instructions to use AI4PD/ProtGPT3-10B-dpo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AI4PD/ProtGPT3-10B-dpo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="AI4PD/ProtGPT3-10B-dpo")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("AI4PD/ProtGPT3-10B-dpo") model = AutoModelForCausalLM.from_pretrained("AI4PD/ProtGPT3-10B-dpo", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use AI4PD/ProtGPT3-10B-dpo with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AI4PD/ProtGPT3-10B-dpo" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AI4PD/ProtGPT3-10B-dpo", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/AI4PD/ProtGPT3-10B-dpo
- SGLang
How to use AI4PD/ProtGPT3-10B-dpo 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 "AI4PD/ProtGPT3-10B-dpo" \ --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": "AI4PD/ProtGPT3-10B-dpo", "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 "AI4PD/ProtGPT3-10B-dpo" \ --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": "AI4PD/ProtGPT3-10B-dpo", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use AI4PD/ProtGPT3-10B-dpo with Docker Model Runner:
docker model run hf.co/AI4PD/ProtGPT3-10B-dpo
Update README.md
Browse files
README.md
CHANGED
|
@@ -54,10 +54,13 @@ As with other generative protein models, ProtGPT3-10B-dpo may present dual-use r
|
|
| 54 |
**BibTeX:**
|
| 55 |
|
| 56 |
```bibtex
|
| 57 |
-
@article{
|
| 58 |
title={ProtGPT3: an Open-source family of Promptable and Aligned Protein Language Models},
|
| 59 |
-
author={
|
| 60 |
-
|
|
|
|
|
|
|
|
|
|
| 61 |
}
|
| 62 |
```
|
| 63 |
|
|
|
|
| 54 |
**BibTeX:**
|
| 55 |
|
| 56 |
```bibtex
|
| 57 |
+
@article{garibbo2026protgpt3,
|
| 58 |
title={ProtGPT3: an Open-source family of Promptable and Aligned Protein Language Models},
|
| 59 |
+
author={Garibbo, Michele and Boxo Corominas, Gerard and Stocco, Filippo and Illanes Vicioso, Ramiro and Middendorf, Lasse and Ferruz, Noelia},
|
| 60 |
+
journal={bioRxiv},
|
| 61 |
+
pages={2026--06},
|
| 62 |
+
year={2026},
|
| 63 |
+
publisher={Cold Spring Harbor Laboratory}
|
| 64 |
}
|
| 65 |
```
|
| 66 |
|