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

Libraries

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

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

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

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

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Use Docker

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

SGLang

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

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

c-bone commited on Sep 8

Commit

ae03244

verified ·

1 Parent(s): 6eb8584

Update model card

Browse files

Files changed (1) hide show

README.md +3 -3

README.md CHANGED Viewed

@@ -53,7 +53,7 @@ The model is intended for:
 ## Bias, Risks, and Limitations
 - **Training Distribution:** The model reflects the biases present in the LeMaterial dataset. It is most effective at generating structures similar to known stable inorganic compounds.
-- **Validity:** While it learns CIF syntax robustly, it may still generate physically invalid structures (e.g., overlapping atoms) or chemically unstable compositions.
 ## How to Get Started with the Model
@@ -84,7 +84,7 @@ The model is evaluated based on:
 ### Results
-The base model achieves high validity rates and effectively learns to generate chemically plausible structures, serving as a robust foundation for downstream property-conditioning tasks.
 ## Citation
@@ -96,5 +96,5 @@ The base model achieves high validity rates and effectively learns to generate c
       eprint={2511.21299},
       archivePrefix={arXiv},
       primaryClass={cond-mat.mtrl-sci},
-      url={[https://arxiv.org/abs/2511.21299](https://arxiv.org/abs/2511.21299)},
 }

 ## Bias, Risks, and Limitations
 - **Training Distribution:** The model reflects the biases present in the LeMaterial dataset. It is most effective at generating structures similar to known stable inorganic compounds.
+- **Validity:** While it learns CIF syntax reliably, it may still generate physically invalid structures (e.g., overlapping atoms) or chemically unstable compositions.
 ## How to Get Started with the Model
 ### Results
+The base model achieves high validity rates and effectively learns to generate chemically plausible structures, serving as a foundation for downstream property-conditioning tasks.
 ## Citation
       eprint={2511.21299},
       archivePrefix={arXiv},
       primaryClass={cond-mat.mtrl-sci},
+      url={https://arxiv.org/abs/2511.21299},
 }