CohereLabs/aya_collection_language_split
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How to use matrixportalx/Llama3-8B-Instruct-Turkish-Finetuned-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="matrixportalx/Llama3-8B-Instruct-Turkish-Finetuned-GGUF", filename="llama3-8b-instruct-turkish-finetuned.f16.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
How to use matrixportalx/Llama3-8B-Instruct-Turkish-Finetuned-GGUF with llama.cpp:
brew install llama.cpp # 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
winget install llama.cpp # 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
# 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
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
docker model run hf.co/matrixportalx/Llama3-8B-Instruct-Turkish-Finetuned-GGUF:Q4_K_M
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
How to use matrixportalx/Llama3-8B-Instruct-Turkish-Finetuned-GGUF with Unsloth Studio:
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
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
# 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
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
How to use matrixportalx/Llama3-8B-Instruct-Turkish-Finetuned-GGUF with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull matrixportalx/Llama3-8B-Instruct-Turkish-Finetuned-GGUF:Q4_K_M
lemonade run user.Llama3-8B-Instruct-Turkish-Finetuned-GGUF-Q4_K_M
lemonade list
| 🚀 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
2-bit
3-bit
4-bit
5-bit
6-bit
16-bit
Base model
meta-llama/Meta-Llama-3-8B-Instruct