Instructions to use lamm-mit/ProteinForceGPT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lamm-mit/ProteinForceGPT with Transformers:

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

pipe = pipeline("text-generation", model="lamm-mit/ProteinForceGPT")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("lamm-mit/ProteinForceGPT")
model = AutoModelForCausalLM.from_pretrained("lamm-mit/ProteinForceGPT", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use lamm-mit/ProteinForceGPT with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/lamm-mit/ProteinForceGPT

SGLang

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

Docker Model Runner
How to use lamm-mit/ProteinForceGPT with Docker Model Runner:
```
docker model run hf.co/lamm-mit/ProteinForceGPT
```

mjbuehler commited on Jan 14, 2024

Commit

4517d36

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1 Parent(s): 0a0e664

Update README.md

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@@ -1,9 +1,6 @@
 ---
 license: apache-2.0
 ---
----
-license: apache-2.0
----
 # ProteinForceGPT: Generative strategies for modeling, design and analysis of protein mechanics
@@ -51,6 +48,8 @@ model = AutoModelForCausalLM.from_pretrained(
 model.config.use_cache = False
 ```
 Sample inference using the "Sequence<...>" task, where here, the model will simply autocomplete the sequence starting with "AIIAA":
 ```python
@@ -113,6 +112,7 @@ To cite this work:
 The dataset used to fine-tune the model is available at:
 @article{GhafarollahiBuehler_2024,
     title   = {ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a protein language diffusion model},
     author  = {B. Ni, D.L. Kaplan, M.J. Buehler},

 ---
 license: apache-2.0
 ---
 # ProteinForceGPT: Generative strategies for modeling, design and analysis of protein mechanics
 model.config.use_cache = False
 ```
+### Inference
 Sample inference using the "Sequence<...>" task, where here, the model will simply autocomplete the sequence starting with "AIIAA":
 ```python
 The dataset used to fine-tune the model is available at:
+```
 @article{GhafarollahiBuehler_2024,
     title   = {ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a protein language diffusion model},
     author  = {B. Ni, D.L. Kaplan, M.J. Buehler},