Instructions to use Gemstone-Models/Gemstone-3072x12 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Gemstone-Models/Gemstone-3072x12 with Transformers:

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

pipe = pipeline("text-generation", model="Gemstone-Models/Gemstone-3072x12")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Gemstone-Models/Gemstone-3072x12")
model = AutoModelForMultimodalLM.from_pretrained("Gemstone-Models/Gemstone-3072x12")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Gemstone-Models/Gemstone-3072x12 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Gemstone-Models/Gemstone-3072x12"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Gemstone-Models/Gemstone-3072x12",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Gemstone-Models/Gemstone-3072x12

SGLang

How to use Gemstone-Models/Gemstone-3072x12 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 "Gemstone-Models/Gemstone-3072x12" \
    --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": "Gemstone-Models/Gemstone-3072x12",
		"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 "Gemstone-Models/Gemstone-3072x12" \
        --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": "Gemstone-Models/Gemstone-3072x12",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Gemstone-Models/Gemstone-3072x12 with Docker Model Runner:
```
docker model run hf.co/Gemstone-Models/Gemstone-3072x12
```

Gemstone-1536x50

Gemstone-1536x50 is part of the Gemstone Suite of Models. A set of models trained with varying widths and depths.

Training

We train using litgpt and AxoNN using AMD MI250X GPUs on Frontier at Oak Ridge National Laboratory with a global batch size of 2048.

Data

Train and validation data is taken from non-overlapping subsets of dolma. As such it is not an instruction model. This model is trained for 350 billion tokens, we upload checkpoints every 2 billion tokens (477 steps).

Using Gemstone-1536x50

The Gemstones are based on the gemma-2b architecture and use modeling_gemma.py to run using the transformers library.

Licence

This model is released under the apache-2.0 licence.

Contact

Please, feel free to contact us with any questions, or open a discussion thread.

Citation

@article{mcleish2024gemstones
    title={Gemstones: A Model Suite for Multi-Faceted Scaling Laws}, 
    author={Sean McLeish and John Kirchenbauer and David Yu Miller and Siddharth Singh and Abhinav Bhatele and Micah Goldblum and Ashwinee Panda and Tom Goldstein},
    journal={arXiv preprint arXiv:2502.},
    year={2025}
}

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Safetensors

Model size

2B params

Tensor type

F32

Gemstone-Models
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Gemstone-3072x12