How to use from
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
Quick Links

Llama3-8B-Instruct-Turkish-Finetuned GGUF Quantized Models

Technical Details

  • Quantization Tool: llama.cpp
  • Version: version: 5162 (2016f07b)

Model Information

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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GGUF
Model size
8B params
Architecture
llama
Hardware compatibility
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