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 matrixportalx/Maya-GGUF:# Run inference directly in the terminal:
llama cli -hf matrixportalx/Maya-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 matrixportalx/Maya-GGUF:# Run inference directly in the terminal:
./llama-cli -hf matrixportalx/Maya-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 matrixportalx/Maya-GGUF:# Run inference directly in the terminal:
./build/bin/llama-cli -hf matrixportalx/Maya-GGUF:Use Docker
docker model run hf.co/matrixportalx/Maya-GGUF:Quick Links
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Check out the documentation for more information.
Maya GGUF Quantized Models
Technical Details
- Quantization Tool: llama.cpp
- Version: version: 5158 (00137157)
Model Information
- Base Model: matrixportal/Maya
- Quantized by: matrixportal
Available Files
| 🚀 Download | 🔢 Type | 📝 Description |
|---|---|---|
| Download | Q2 K | Tiny size, lowest quality (emergency use only) |
| Download | Q3 K S | Very small, low quality (basic tasks) |
| Download | Q3 K M | Small, acceptable quality |
| Download | Q3 K L | Small, better than Q3_K_M (good for low RAM) |
| Download | Q4 0 | Standard 4-bit (fast on ARM) |
| Download | Q4 K S | 4-bit optimized (good space savings) |
| Download | Q4 K M | 4-bit balanced (recommended default) |
| Download | Q5 0 | 5-bit high quality |
| Download | Q5 K S | 5-bit optimized |
| Download | Q5 K M | 5-bit best (recommended HQ option) |
| Download | Q6 K | 6-bit near-perfect (premium quality) |
| Download | Q8 0 | 8-bit maximum (overkill for most) |
| Download | F16 | Full precision (maximum accuracy) |
💡 Q4 K M provides the best balance for most use cases
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Hardware compatibility
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Install (macOS, Linux)
# Start a local OpenAI-compatible server with a web UI: llama serve -hf matrixportalx/Maya-GGUF:# Run inference directly in the terminal: llama cli -hf matrixportalx/Maya-GGUF: