How to use from
vLLM
Install from pip and serve model
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Fu01978/OLMo-2-1B-openai-gsm8k-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Fu01978/OLMo-2-1B-openai-gsm8k-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'
Use Docker
docker model run hf.co/Fu01978/OLMo-2-1B-openai-gsm8k-GGUF:
Quick Links

OLMo-2-1B-openai-gsm8k-GGUF

This repository contains GGUF quantized versions of Fu01978/OLMo-2-1B-openai-gsm8k, intended for efficient inference with llama.cpp-compatible runtimes.

What’s in this repo

  • GGUF quantized files for inference
  • No training code
  • No safetensors weights

What’s NOT in this repo

  • Original model
  • Training or fine-tuning scripts

Base Model

These quantizations are derived from:

Fu01978/OLMo-2-1B-openai-gsm8k
👉 https://huggingface.co/Fu01978/OLMo-2-1B-openai-gsm8k

Please refer to the base model card for:

  • Training data
  • Intended use
  • Limitations

Usage

Example with llama.cpp:

./main \
  -m OLMo-2-1B-openai-gsm8k*.gguf \
  -p "Solve: 23 + 19 ="

Notes on Quantization

  • Quantization may slightly reduce accuracy
  • Smaller sizes offer faster inference and lower VRAM usage
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GGUF
Model size
1B params
Architecture
olmo2
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