How to use from
vLLM
Install from pip and serve model
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "roleplaiapp/Slush-Sunfall-Rocinante-GGLD-12B-IQ4_XS-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": "roleplaiapp/Slush-Sunfall-Rocinante-GGLD-12B-IQ4_XS-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'
Use Docker
docker model run hf.co/roleplaiapp/Slush-Sunfall-Rocinante-GGLD-12B-IQ4_XS-GGUF:IQ4_XS
Quick Links

roleplaiapp/Slush-Sunfall-Rocinante-GGLD-12B-IQ4_XS-GGUF

Repo: roleplaiapp/Slush-Sunfall-Rocinante-GGLD-12B-IQ4_XS-GGUF Original Model: Slush-Sunfall-Rocinante-GGLD-12B Quantized File: Slush-Sunfall-Rocinante-GGLD-12B.IQ4_XS.gguf Quantization: GGUF Quantization Method: IQ4_XS

Overview

This is a GGUF IQ4_XS quantized version of Slush-Sunfall-Rocinante-GGLD-12B

Quantization By

I often have idle GPUs while building/testing for the RP app, so I put them to use quantizing models. I hope the community finds these quantizations useful.

Andrew Webby @ RolePlai.

Downloads last month
4
GGUF
Model size
12B params
Architecture
llama
Hardware compatibility
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4-bit

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