Instructions to use matrixportalx/Llama3-8B-Instruct-Turkish-Finetuned-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use matrixportalx/Llama3-8B-Instruct-Turkish-Finetuned-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf matrixportalx/Llama3-8B-Instruct-Turkish-Finetuned-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf matrixportalx/Llama3-8B-Instruct-Turkish-Finetuned-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf matrixportalx/Llama3-8B-Instruct-Turkish-Finetuned-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf matrixportalx/Llama3-8B-Instruct-Turkish-Finetuned-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/Llama3-8B-Instruct-Turkish-Finetuned-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf matrixportalx/Llama3-8B-Instruct-Turkish-Finetuned-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/Llama3-8B-Instruct-Turkish-Finetuned-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf matrixportalx/Llama3-8B-Instruct-Turkish-Finetuned-GGUF:Q4_K_M

Use Docker

docker model run hf.co/matrixportalx/Llama3-8B-Instruct-Turkish-Finetuned-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use matrixportalx/Llama3-8B-Instruct-Turkish-Finetuned-GGUF with Ollama:
```
ollama run hf.co/matrixportalx/Llama3-8B-Instruct-Turkish-Finetuned-GGUF:Q4_K_M
```

Unsloth Studio

How to use matrixportalx/Llama3-8B-Instruct-Turkish-Finetuned-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/Llama3-8B-Instruct-Turkish-Finetuned-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/Llama3-8B-Instruct-Turkish-Finetuned-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/Llama3-8B-Instruct-Turkish-Finetuned-GGUF to start chatting

Docker Model Runner
How to use matrixportalx/Llama3-8B-Instruct-Turkish-Finetuned-GGUF with Docker Model Runner:
```
docker model run hf.co/matrixportalx/Llama3-8B-Instruct-Turkish-Finetuned-GGUF:Q4_K_M
```

Lemonade

How to use matrixportalx/Llama3-8B-Instruct-Turkish-Finetuned-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull matrixportalx/Llama3-8B-Instruct-Turkish-Finetuned-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Llama3-8B-Instruct-Turkish-Finetuned-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Matrix commited on Apr 21, 2025

Commit

92f0c80

verified ·

1 Parent(s): 9cce4ed

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	@@ -0,0 +1,41 @@

+---
+datasets:
+- matrixportal/aya-turkish-alpaca
+- cenfis/alpaca-turkish-combined
+- merve/turkish_instructions
+- CohereLabs/aya_collection_language_split
+language:
+- tr
+- en
+base_model:
+- meta-llama/Meta-Llama-3-8B-Instruct
+---
+# Llama3-8B-Instruct-Turkish-Finetuned GGUF Quantized Models
+## Technical Details
+- **Quantization Tool:** llama.cpp
+- **Version:** version: 5162 (2016f07b)
+## Model Information
+- **Base Model:** [matrixportal/Llama3-8B-Instruct-Turkish-Finetuned](https://huggingface.co/matrixportal/Llama3-8B-Instruct-Turkish-Finetuned)
+- **Quantized by:** [matrixportal](https://huggingface.co/matrixportal)
+## Available Files
+| 🚀 Download | 🔢 Type | 📝 Description |
+|------------|---------|---------------|
+| [Download](https://huggingface.co/matrixportal/Llama3-8B-Instruct-Turkish-Finetuned-GGUF/resolve/main/llama3-8b-instruct-turkish-finetuned.q2_k.gguf) | Q2 K | Tiny size, lowest quality (emergency use only) |
+| [Download](https://huggingface.co/matrixportal/Llama3-8B-Instruct-Turkish-Finetuned-GGUF/resolve/main/llama3-8b-instruct-turkish-finetuned.q3_k_s.gguf) | Q3 K S | Very small, low quality (basic tasks) |
+| [Download](https://huggingface.co/matrixportal/Llama3-8B-Instruct-Turkish-Finetuned-GGUF/resolve/main/llama3-8b-instruct-turkish-finetuned.q3_k_m.gguf) | Q3 K M | Small, acceptable quality |
+| [Download](https://huggingface.co/matrixportal/Llama3-8B-Instruct-Turkish-Finetuned-GGUF/resolve/main/llama3-8b-instruct-turkish-finetuned.q3_k_l.gguf) | Q3 K L | Small, better than Q3_K_M (good for low RAM) |
+| [Download](https://huggingface.co/matrixportal/Llama3-8B-Instruct-Turkish-Finetuned-GGUF/resolve/main/llama3-8b-instruct-turkish-finetuned.q4_0.gguf) | Q4 0 | Standard 4-bit (fast on ARM) |
+| [Download](https://huggingface.co/matrixportal/Llama3-8B-Instruct-Turkish-Finetuned-GGUF/resolve/main/llama3-8b-instruct-turkish-finetuned.q4_k_s.gguf) | Q4 K S | 4-bit optimized (good space savings) |
+| [Download](https://huggingface.co/matrixportal/Llama3-8B-Instruct-Turkish-Finetuned-GGUF/resolve/main/llama3-8b-instruct-turkish-finetuned.q4_k_m.gguf) | Q4 K M | 4-bit balanced (recommended default) |
+| [Download](https://huggingface.co/matrixportal/Llama3-8B-Instruct-Turkish-Finetuned-GGUF/resolve/main/llama3-8b-instruct-turkish-finetuned.q5_0.gguf) | Q5 0 | 5-bit high quality |
+| [Download](https://huggingface.co/matrixportal/Llama3-8B-Instruct-Turkish-Finetuned-GGUF/resolve/main/llama3-8b-instruct-turkish-finetuned.q5_k_s.gguf) | Q5 K S | 5-bit optimized |
+| [Download](https://huggingface.co/matrixportal/Llama3-8B-Instruct-Turkish-Finetuned-GGUF/resolve/main/llama3-8b-instruct-turkish-finetuned.q5_k_m.gguf) | Q5 K M | 5-bit best (recommended HQ option) |
+| [Download](https://huggingface.co/matrixportal/Llama3-8B-Instruct-Turkish-Finetuned-GGUF/resolve/main/llama3-8b-instruct-turkish-finetuned.q6_k.gguf) | Q6 K | 6-bit near-perfect (premium quality) |
+| [Download](https://huggingface.co/matrixportal/Llama3-8B-Instruct-Turkish-Finetuned-GGUF/resolve/main/llama3-8b-instruct-turkish-finetuned.q8_0.gguf) | Q8 0 | 8-bit maximum (overkill for most) |
+| [Download](https://huggingface.co/matrixportal/Llama3-8B-Instruct-Turkish-Finetuned-GGUF/resolve/main/llama3-8b-instruct-turkish-finetuned.f16.gguf) | F16 | Full precision (maximum accuracy) |
+💡 **Q4 K M** provides the best balance for most use cases