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.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Swicked86/phi4-mm-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 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 serve -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/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -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
- Llama new
- 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 Desktop
- Pi
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 serve -hf Swicked86/phi4-mm-gguf:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/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
- 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
- Hermes Agent
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 serve -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
- Atomic Chat
- OpenClaw
How to use Swicked86/phi4-mm-gguf with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Swicked86/phi4-mm-gguf:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "Swicked86/phi4-mm-gguf:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
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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