Instructions to use afrideva/Phi-3-mini-128k-instruct_function-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use afrideva/Phi-3-mini-128k-instruct_function-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 afrideva/Phi-3-mini-128k-instruct_function-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf afrideva/Phi-3-mini-128k-instruct_function-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 afrideva/Phi-3-mini-128k-instruct_function-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf afrideva/Phi-3-mini-128k-instruct_function-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 afrideva/Phi-3-mini-128k-instruct_function-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf afrideva/Phi-3-mini-128k-instruct_function-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 afrideva/Phi-3-mini-128k-instruct_function-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf afrideva/Phi-3-mini-128k-instruct_function-GGUF:Q4_K_M
Use Docker
docker model run hf.co/afrideva/Phi-3-mini-128k-instruct_function-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use afrideva/Phi-3-mini-128k-instruct_function-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "afrideva/Phi-3-mini-128k-instruct_function-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "afrideva/Phi-3-mini-128k-instruct_function-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/afrideva/Phi-3-mini-128k-instruct_function-GGUF:Q4_K_M
- Ollama
How to use afrideva/Phi-3-mini-128k-instruct_function-GGUF with Ollama:
ollama run hf.co/afrideva/Phi-3-mini-128k-instruct_function-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use afrideva/Phi-3-mini-128k-instruct_function-GGUF with Docker Model Runner:
docker model run hf.co/afrideva/Phi-3-mini-128k-instruct_function-GGUF:Q4_K_M
- Lemonade
How to use afrideva/Phi-3-mini-128k-instruct_function-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull afrideva/Phi-3-mini-128k-instruct_function-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Phi-3-mini-128k-instruct_function-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
NickyNicky/Phi-3-mini-128k-instruct_function-GGUF
Quantized GGUF model files for Phi-3-mini-128k-instruct_function from NickyNicky
| Name | Quant method | Size |
|---|---|---|
| phi-3-mini-128k-instruct_function.fp16.gguf | fp16 | 7.64 GB |
| phi-3-mini-128k-instruct_function.q2_k.gguf | q2_k | 1.42 GB |
| phi-3-mini-128k-instruct_function.q3_k_m.gguf | q3_k_m | 1.96 GB |
| phi-3-mini-128k-instruct_function.q4_k_m.gguf | q4_k_m | 2.39 GB |
| phi-3-mini-128k-instruct_function.q5_k_m.gguf | q5_k_m | 2.82 GB |
| phi-3-mini-128k-instruct_function.q6_k.gguf | q6_k | 3.14 GB |
| phi-3-mini-128k-instruct_function.q8_0.gguf | q8_0 | 4.06 GB |
Original Model Card:
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Model tree for afrideva/Phi-3-mini-128k-instruct_function-GGUF
Base model
NickyNicky/Phi-3-mini-128k-instruct_function