Text Generation
Safetensors
qwen3_5_moe
auto-round
intel
w4a16
int4
gptq
vllm
sglang
marlin
image-text-to-text
agentic
conversational
4-bit precision
Instructions to use quant-mind/Nex-N2.5-mini-W4A16-AutoRound with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Local Apps Settings
- vLLM
How to use quant-mind/Nex-N2.5-mini-W4A16-AutoRound with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "quant-mind/Nex-N2.5-mini-W4A16-AutoRound" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "quant-mind/Nex-N2.5-mini-W4A16-AutoRound", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/quant-mind/Nex-N2.5-mini-W4A16-AutoRound
- SGLang
How to use quant-mind/Nex-N2.5-mini-W4A16-AutoRound with 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 "quant-mind/Nex-N2.5-mini-W4A16-AutoRound" \ --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": "quant-mind/Nex-N2.5-mini-W4A16-AutoRound", "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 "quant-mind/Nex-N2.5-mini-W4A16-AutoRound" \ --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": "quant-mind/Nex-N2.5-mini-W4A16-AutoRound", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use quant-mind/Nex-N2.5-mini-W4A16-AutoRound with Docker Model Runner:
docker model run hf.co/quant-mind/Nex-N2.5-mini-W4A16-AutoRound
File size: 478 Bytes
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"data_format": "channels_first",
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"do_normalize": true,
"do_rescale": true,
"do_resize": true,
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"image_processor_type": "Qwen2VLImageProcessor",
"image_std": [
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"merge_size": 2,
"patch_size": 16,
"resample": 3,
"rescale_factor": 0.00392156862745098,
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"temporal_patch_size": 2
}
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