Instructions to use matrixportalx/Maya-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use matrixportalx/Maya-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="matrixportalx/Maya-GGUF", filename="maya.f16.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use matrixportalx/Maya-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf matrixportalx/Maya-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf matrixportalx/Maya-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf matrixportalx/Maya-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf matrixportalx/Maya-GGUF:Q4_K_M
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:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf matrixportalx/Maya-GGUF:Q4_K_M
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:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf matrixportalx/Maya-GGUF:Q4_K_M
Use Docker
docker model run hf.co/matrixportalx/Maya-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use matrixportalx/Maya-GGUF with Ollama:
ollama run hf.co/matrixportalx/Maya-GGUF:Q4_K_M
- Unsloth Studio new
How to use matrixportalx/Maya-GGUF with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for matrixportalx/Maya-GGUF to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for matrixportalx/Maya-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for matrixportalx/Maya-GGUF to start chatting
- Docker Model Runner
How to use matrixportalx/Maya-GGUF with Docker Model Runner:
docker model run hf.co/matrixportalx/Maya-GGUF:Q4_K_M
- Lemonade
How to use matrixportalx/Maya-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull matrixportalx/Maya-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Maya-GGUF-Q4_K_M
List all available models
lemonade list
YAML Metadata Warning:empty or missing yaml metadata in repo card
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
| ๐ Download | ๐ข Type | ๐ 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) |
๐ก Q4 K M provides the best balance for most use cases
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