How to use from
llama.cppInstall from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf dekthedev/Qwen2.5-VL-3B-Instruct-GGUF:# Run inference directly in the terminal:
llama cli -hf dekthedev/Qwen2.5-VL-3B-Instruct-GGUF:Use pre-built binary
# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf dekthedev/Qwen2.5-VL-3B-Instruct-GGUF:# Run inference directly in the terminal:
./llama-cli -hf dekthedev/Qwen2.5-VL-3B-Instruct-GGUF:Build from source code
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf dekthedev/Qwen2.5-VL-3B-Instruct-GGUF:# Run inference directly in the terminal:
./build/bin/llama-cli -hf dekthedev/Qwen2.5-VL-3B-Instruct-GGUF:Use Docker
docker model run hf.co/dekthedev/Qwen2.5-VL-3B-Instruct-GGUF:Quick Links
Qwen2.5-VL-3B-Instruct — GGUF
Quantized by @dekthedev.
All quantized GGUF variants of Qwen/Qwen2.5-VL-3B-Instruct. Quantized with llama.cpp for local and edge deployment.
Files
| Variant | File | Size | Quality | Notes |
|---|---|---|---|---|
| Q2_K | qwen25vl-3b-q2_k.gguf |
1.27 GB | Low | Smallest, fastest inference |
| Q4_K_S | qwen25vl-3b-q4_k_s.gguf |
1.83 GB | Good | 4-bit, small variant |
| Q4_K_M | qwen25vl-3b-q4_k_m.gguf |
1.93 GB | Very good | 4-bit, medium variant — best balance |
| Q5_K_M | qwen25vl-3b-q5_k_m.gguf |
2.22 GB | Excellent | 5-bit, sharper outputs |
| Q8_0 | qwen25vl-3b-q8_0.gguf |
3.29 GB | Near-lossless | 8-bit, closest to full precision |
| mmproj F16 | qwen25vl-3b-mmproj-f16.gguf |
1.34 GB | — | Vision projector, required for image input |
Usage with llama.cpp
Text only:
llama-cli \
--model qwen25vl-3b-q4_k_m.gguf \
--prompt "Hello! Who are you?" \
--n-predict 128
With image:
llama-cli \
--model qwen25vl-3b-q4_k_m.gguf \
--mmproj qwen25vl-3b-mmproj-f16.gguf \
--image your_image.jpg \
--prompt "<|im_start|>user\nDescribe this image.<|im_end|>\n<|im_start|>assistant\n" \
--n-predict 256
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Hardware compatibility
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Model tree for dekthedev/Qwen2.5-VL-3B-Instruct-GGUF
Base model
Qwen/Qwen2.5-VL-3B-Instruct
Install (macOS, Linux)
# Start a local OpenAI-compatible server with a web UI: llama serve -hf dekthedev/Qwen2.5-VL-3B-Instruct-GGUF:# Run inference directly in the terminal: llama cli -hf dekthedev/Qwen2.5-VL-3B-Instruct-GGUF: