Text Generation
GGUF
English
conversational
How to use from
vLLM
Install from pip and serve model
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GrainWare/tuxsentience-beta2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GrainWare/tuxsentience-beta2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'
Use Docker
docker model run hf.co/GrainWare/tuxsentience-beta2:Q4_K_M
Quick Links

Our first open-weight AI model, based off https://huggingface.co/datasets/GrainWare/tuxsentience-v1 and https://huggingface.co/unsloth/Llama-3.2-1B-Instruct-unsloth-bnb-4bit.

Fine-tuned locally using Unsloth on a RX 7600.

Accuracy at all costs.

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GGUF
Model size
1B params
Architecture
llama
Hardware compatibility
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4-bit

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