Instructions to use c-bone/CrystaLLM-pi_SLME with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use c-bone/CrystaLLM-pi_SLME with Transformers:

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

pipe = pipeline("text-generation", model="c-bone/CrystaLLM-pi_SLME")

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

tokenizer = AutoTokenizer.from_pretrained("c-bone/CrystaLLM-pi_SLME")
model = PKVGPT.from_pretrained("c-bone/CrystaLLM-pi_SLME", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use c-bone/CrystaLLM-pi_SLME with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/c-bone/CrystaLLM-pi_SLME

SGLang

How to use c-bone/CrystaLLM-pi_SLME 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 "c-bone/CrystaLLM-pi_SLME" \
    --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": "c-bone/CrystaLLM-pi_SLME",
		"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 "c-bone/CrystaLLM-pi_SLME" \
        --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": "c-bone/CrystaLLM-pi_SLME",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use c-bone/CrystaLLM-pi_SLME with Docker Model Runner:
```
docker model run hf.co/c-bone/CrystaLLM-pi_SLME
```

c-bone commited on 7 days ago

Commit

f2ba4b7

verified ·

1 Parent(s): 5da7f9f

Fix citation (current arXiv author list, plain url)

Browse files

Files changed (1) hide show

README.md +3 -3

README.md CHANGED Viewed

@@ -63,11 +63,11 @@ Generation: [`T2_load_and_generate.ipynb`](https://github.com/C-Bone-UCL/CrystaL
 ```bibtex
 @misc{bone2025discoveryrecoverycrystallinematerials,
-      title={Discovery and recovery of crystalline materials with property-conditioned transformers},
-      author={Cyprien Bone and Matthew Walker and Kuangdai Leng and Luis M. Antunes and Ricardo Grau-Crespo and Amil Aligayev and Javier Dominguez and Keith T. Butler},
       year={2025},
       eprint={2511.21299},
       archivePrefix={arXiv},
       primaryClass={cond-mat.mtrl-sci},
-      url={[https://arxiv.org/abs/2511.21299](https://arxiv.org/abs/2511.21299)},
 }

 ```bibtex
 @misc{bone2025discoveryrecoverycrystallinematerials,
+      title={Discovery and recovery of crystalline materials with property-conditioned transformers},
+      author={Cyprien Bone and Matthew Walker and Bradley A. A. Martin and Kuangdai Leng and Luis M. Antunes and Ricardo Grau-Crespo and Amil Aligayev and Javier Dominguez and Keith T. Butler},
       year={2025},
       eprint={2511.21299},
       archivePrefix={arXiv},
       primaryClass={cond-mat.mtrl-sci},
+      url={https://arxiv.org/abs/2511.21299},
 }