How to use from
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 "ZeroAgency/Zero-Mistral-24B-gguf" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ZeroAgency/Zero-Mistral-24B-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'
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 "ZeroAgency/Zero-Mistral-24B-gguf" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ZeroAgency/Zero-Mistral-24B-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'
Quick Links

Model Card for Zero-Mistral

This is a GGUF version of ZeroAgency/Zero-Mistral-24B.

All quants made with llama.cpp version b5083.

Quants available:

  • BF16
  • F16
  • IQ4_NL
  • IQ4_NL_L - same as above but with --leave-output-tensors
  • IQ4_XS
  • IQ4_XS_L - same as above but with --leave-output-tensors
  • Q4_K_M
  • Q4_K_M_L - same as above but with --leave-output-tensors
  • Q6_K
  • Q6_K_L - same as above but with --leave-output-tensors
  • Q8_0 - quantized from bf16 gguf
  • Q8_0-direct - direct convertation from hf
  • Q8_0_L - quantized from bf16 but with --leave-output-tensors

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Model size
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Architecture
llama
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