Instructions to use matrixportalx/Aya-Medikal-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use matrixportalx/Aya-Medikal-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("matrixportalx/Aya-Medikal-GGUF", dtype="auto")

llama-cpp-python

How to use matrixportalx/Aya-Medikal-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="matrixportalx/Aya-Medikal-GGUF",
	filename="aya-medikal.f16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use matrixportalx/Aya-Medikal-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/Aya-Medikal-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf matrixportalx/Aya-Medikal-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/Aya-Medikal-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf matrixportalx/Aya-Medikal-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/Aya-Medikal-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf matrixportalx/Aya-Medikal-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/Aya-Medikal-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf matrixportalx/Aya-Medikal-GGUF:Q4_K_M

Use Docker

docker model run hf.co/matrixportalx/Aya-Medikal-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use matrixportalx/Aya-Medikal-GGUF with Ollama:
```
ollama run hf.co/matrixportalx/Aya-Medikal-GGUF:Q4_K_M
```

Unsloth Studio

How to use matrixportalx/Aya-Medikal-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/Aya-Medikal-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/Aya-Medikal-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/Aya-Medikal-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use matrixportalx/Aya-Medikal-GGUF with Docker Model Runner:
```
docker model run hf.co/matrixportalx/Aya-Medikal-GGUF:Q4_K_M
```

Lemonade

How to use matrixportalx/Aya-Medikal-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull matrixportalx/Aya-Medikal-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Aya-Medikal-GGUF-Q4_K_M

List all available models

lemonade list

Aya-Medikal-GGUF

File size: 2,575 Bytes

---
base_model: CohereLabs/aya-expanse-8b
language:
- tr
- en
library_name: transformers
license: cc-by-nc-4.0
tags:
- matrixportal
inference: false
---

# Aya-Medikal GGUF Quantized Models

## Technical Details
- **Quantization Tool:** llama.cpp
- **Version:** version: 5186 (13be08da)

## Model Information
- **Base Model:** [matrixportal/Aya-Medikal](https://huggingface.co/matrixportal/Aya-Medikal)
- **Quantized by:** [matrixportal](https://huggingface.co/matrixportal)

## Available Files
| 🚀 Download | 🔢 Type | 📝 Description |
|------------|---------|---------------|
| [Download](https://huggingface.co/matrixportal/Aya-Medikal-GGUF/resolve/main/aya-medikal.q2_k.gguf) | Q2 K | Tiny size, lowest quality (emergency use only) |
| [Download](https://huggingface.co/matrixportal/Aya-Medikal-GGUF/resolve/main/aya-medikal.q3_k_s.gguf) | Q3 K S | Very small, low quality (basic tasks) |
| [Download](https://huggingface.co/matrixportal/Aya-Medikal-GGUF/resolve/main/aya-medikal.q3_k_m.gguf) | Q3 K M | Small, acceptable quality |
| [Download](https://huggingface.co/matrixportal/Aya-Medikal-GGUF/resolve/main/aya-medikal.q3_k_l.gguf) | Q3 K L | Small, better than Q3_K_M (good for low RAM) |
| [Download](https://huggingface.co/matrixportal/Aya-Medikal-GGUF/resolve/main/aya-medikal.q4_0.gguf) | Q4 0 | Standard 4-bit (fast on ARM) |
| [Download](https://huggingface.co/matrixportal/Aya-Medikal-GGUF/resolve/main/aya-medikal.q4_k_s.gguf) | Q4 K S | 4-bit optimized (good space savings) |
| [Download](https://huggingface.co/matrixportal/Aya-Medikal-GGUF/resolve/main/aya-medikal.q4_k_m.gguf) | Q4 K M | 4-bit balanced (recommended default) |
| [Download](https://huggingface.co/matrixportal/Aya-Medikal-GGUF/resolve/main/aya-medikal.q5_0.gguf) | Q5 0 | 5-bit high quality |
| [Download](https://huggingface.co/matrixportal/Aya-Medikal-GGUF/resolve/main/aya-medikal.q5_k_s.gguf) | Q5 K S | 5-bit optimized |
| [Download](https://huggingface.co/matrixportal/Aya-Medikal-GGUF/resolve/main/aya-medikal.q5_k_m.gguf) | Q5 K M | 5-bit best (recommended HQ option) |
| [Download](https://huggingface.co/matrixportal/Aya-Medikal-GGUF/resolve/main/aya-medikal.q6_k.gguf) | Q6 K | 6-bit near-perfect (premium quality) |
| [Download](https://huggingface.co/matrixportal/Aya-Medikal-GGUF/resolve/main/aya-medikal.q8_0.gguf) | Q8 0 | 8-bit maximum (overkill for most) |
| [Download](https://huggingface.co/matrixportal/Aya-Medikal-GGUF/resolve/main/aya-medikal.f16.gguf) | F16 | Full precision (maximum accuracy) |

💡 **Q4 K M** provides the best balance for most use cases