Visual Question Answering
GGUF
English
phi4
phi4mm
quantized
vision-language
audio
multimodal
llama-cpp
lm-studio
conversational
Instructions to use Swicked86/phi4-mm-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Swicked86/phi4-mm-gguf with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Swicked86/phi4-mm-gguf", filename="mmproj-phi4-mm-f16.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use Swicked86/phi4-mm-gguf with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Swicked86/phi4-mm-gguf:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Swicked86/phi4-mm-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 Swicked86/phi4-mm-gguf:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Swicked86/phi4-mm-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 Swicked86/phi4-mm-gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Swicked86/phi4-mm-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 Swicked86/phi4-mm-gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Swicked86/phi4-mm-gguf:Q4_K_M
Use Docker
docker model run hf.co/Swicked86/phi4-mm-gguf:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use Swicked86/phi4-mm-gguf with Ollama:
ollama run hf.co/Swicked86/phi4-mm-gguf:Q4_K_M
- Unsloth Studio new
How to use Swicked86/phi4-mm-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 Swicked86/phi4-mm-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 Swicked86/phi4-mm-gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Swicked86/phi4-mm-gguf to start chatting
- Pi new
How to use Swicked86/phi4-mm-gguf with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf Swicked86/phi4-mm-gguf:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "Swicked86/phi4-mm-gguf:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use Swicked86/phi4-mm-gguf with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf Swicked86/phi4-mm-gguf:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default Swicked86/phi4-mm-gguf:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use Swicked86/phi4-mm-gguf with Docker Model Runner:
docker model run hf.co/Swicked86/phi4-mm-gguf:Q4_K_M
- Lemonade
How to use Swicked86/phi4-mm-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Swicked86/phi4-mm-gguf:Q4_K_M
Run and chat with the model
lemonade run user.phi4-mm-gguf-Q4_K_M
List all available models
lemonade list
File size: 2,039 Bytes
2904873 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | # Ollama Modelfile β Phi-4 Multimodal Instruct Q4_K_M
# Optimised for: Intel 11th Gen NUC, 8 GB RAM, CPU-only
#
# Source model : microsoft/Phi-4-multimodal-instruct
# License : MIT https://huggingface.co/microsoft/Phi-4-multimodal-instruct/blob/main/LICENSE
# Quantization : Q4_K_M via llama.cpp llama-quantize
# Architecture : phi3 (3.8B LLM backbone + vision/speech adapters in base GGUF)
FROM ./phi4-mm-Q4_K_M.gguf
# ββ Context & KV cache βββββββββββββββββββββββββββββββββββββββββββββββββββββββ
# 8 192 tokens balances capability vs RAM on 8 GB hardware.
# Lower to 4096 if you observe OOM / heavy swapping.
PARAMETER num_ctx 8192
# ββ CPU tuning βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
# 11th Gen NUC typically 4 cores / 8 logical threads (i5/i7-1135G7 / 1165G7).
# Reduce to 4 if the NUC is a Core i3 variant.
PARAMETER num_thread 8
# No discrete GPU β all layers run on CPU.
PARAMETER num_gpu 0
# Flash attention is a GPU feature; disable for CPU inference.
PARAMETER flash_attn false
# ββ Generation defaults βββββββββββββββββββββββββββββββββββββββββββββββββββββββ
PARAMETER temperature 0.7
PARAMETER top_p 0.9
PARAMETER repeat_penalty 1.1
PARAMETER stop "<|end|>"
PARAMETER stop "<|user|>"
PARAMETER stop "<|assistant|>"
# ββ System prompt βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
SYSTEM """You are a helpful, accurate, and concise AI assistant. You excel at reasoning, analysis, writing, coding, and answering questions. Be direct and thorough."""
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