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/Codestral-22B-v0.1-Q3_K_M-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/Codestral-22B-v0.1-Q3_K_M-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'
Use Docker
docker model run hf.co/roleplaiapp/Codestral-22B-v0.1-Q3_K_M-GGUF:Q3_K_M
Quick Links

roleplaiapp/Codestral-22B-v0.1-Q3_K_M-GGUF

Repo: roleplaiapp/Codestral-22B-v0.1-Q3_K_M-GGUF
Original Model: Codestral-22B-v0.1 Organization: mistralai Quantized File: codestral-22b-v0.1-q3_k_m.gguf Quantization: GGUF Quantization Method: Q3_K_M
Use Imatrix: False
Split Model: False

Overview

This is an GGUF Q3_K_M quantized version of Codestral-22B-v0.1.

Quantization By

I often have idle A100 GPUs while building/testing and training 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
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GGUF
Model size
22B params
Architecture
llama
Hardware compatibility
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3-bit

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