Instructions to use legifx/Fara1.5-GGUF-Q4_K_M 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 legifx/Fara1.5-GGUF-Q4_K_M 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 legifx/Fara1.5-GGUF-Q4_K_M:Q4_K_M # Run inference directly in the terminal: llama cli -hf legifx/Fara1.5-GGUF-Q4_K_M:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf legifx/Fara1.5-GGUF-Q4_K_M:Q4_K_M # Run inference directly in the terminal: llama cli -hf legifx/Fara1.5-GGUF-Q4_K_M: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 legifx/Fara1.5-GGUF-Q4_K_M:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf legifx/Fara1.5-GGUF-Q4_K_M: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 legifx/Fara1.5-GGUF-Q4_K_M:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf legifx/Fara1.5-GGUF-Q4_K_M:Q4_K_M
Use Docker
docker model run hf.co/legifx/Fara1.5-GGUF-Q4_K_M:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use legifx/Fara1.5-GGUF-Q4_K_M with Ollama:
ollama run hf.co/legifx/Fara1.5-GGUF-Q4_K_M:Q4_K_M
- Unsloth Studio
How to use legifx/Fara1.5-GGUF-Q4_K_M 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 legifx/Fara1.5-GGUF-Q4_K_M 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 legifx/Fara1.5-GGUF-Q4_K_M to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for legifx/Fara1.5-GGUF-Q4_K_M to start chatting
- Atomic Chat new
- Docker Model Runner
How to use legifx/Fara1.5-GGUF-Q4_K_M with Docker Model Runner:
docker model run hf.co/legifx/Fara1.5-GGUF-Q4_K_M:Q4_K_M
- Lemonade
How to use legifx/Fara1.5-GGUF-Q4_K_M with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull legifx/Fara1.5-GGUF-Q4_K_M:Q4_K_M
Run and chat with the model
lemonade run user.Fara1.5-GGUF-Q4_K_M-Q4_K_M
List all available models
lemonade list
Fara1.5-9B Q4_K_M GGUF
About
GGUF-quantized version of Microsoft Fara1.5-9B, a multimodal computer-use agent (CUA) from Microsoft Research AI Frontiers.
Fara1.5-9B is a vision-language model specialized for browser automation. It observes web browsers through screenshots and performs actions (click, type, scroll, navigate) to complete tasks end-to-end. It is NOT a general-purpose chat model โ it is an autonomous web agent.
Original Model
- Source: microsoft/Fara1.5-9B
- Architecture: Qwen3.5-9B (Hybrid Transformer + SSM,
Qwen3_5ForConditionalGeneration) - Parameters: 9B
- Context Length: 262,144 tokens
- License: MIT
Quantization Details
- Format: GGUF (llama.cpp compatible)
- Quantization: Q4_K_M (4-bit, mixed precision K-quant)
- Original size: ~18 GB (BF16 safetensors)
- Quantized size: ~5.3 GB
- BPW (bits per weight): ~5.02
- Conversion tool: llama.cpp b10089 (
convert_hf_to_gguf.py+llama-quantize)
Hardware Requirements
- RAM/VRAM: Minimum ~6-7 GB for inference (5.3 GB model + overhead)
- GPU: NVIDIA with CUDA support recommended. CPU inference is possible but slow.
- Tested GPU: RTX 5070 Ti (16 GB VRAM)
Usage with llama.cpp
# Server mode
LD_LIBRARY_PATH=/path/to/llama-bins \
./llama-server \
-m Faral-1.5-GGUF-Q4_K_M.gguf \
--host 0.0.0.0 \
--port 8080 \
-ngl 99 \
-c 8192
# CLI mode
./llama-cli -m Faral-1.5-GGUF-Q4_K_M.gguf -p "Hello" -n 256
Usage with LM Studio
- Download the GGUF file from this repository
- Open LM Studio
- Go to "My Models" โ drag and drop the file, or browse to it
- Select the model and start chatting
Note: LM Studio's vision/multimodal support may be limited. For full functionality (screenshot input), use llama.cpp with the multimodal CLI (llama-mtmd-cli).
Usage with Ollama
# Create a Modelfile
echo 'FROM ./Faral-1.5-GGUF-Q4_K_M.gguf' > Modelfile
ollama create fara1.5-9b -f Modelfile
ollama run fara1.5-9b
System Prompt (Recommended)
For optimal results, use the official system prompt from Microsoft:
You are Fara, a computer use agent (CUA) specialized for web browsers.
You are developed by Microsoft AI Frontiers. You assist users with
completing and automating tasks that require the use of a web browser.
Key Features
- Vision-only perception: Operates on browser screenshots alone, no DOM access needed
- Coordinate-grounded actions: Predicts pixel-level click targets directly
- Safety design: Trained to pause before irreversible actions (payments, form submissions, account logins)
- 262K context: Long enough for multi-step browser task trajectories
- Tool-call output: Emits structured XML
<tool_call>blocks with actions likeleft_click(x,y),type(text),scroll(),visit_url(), etc.
Supported Actions
| Action | Purpose |
|---|---|
left_click, right_click, double_click |
Mouse clicks at (x, y) coordinates |
type, key |
Keyboard input |
scroll, hscroll |
Page scrolling |
visit_url, history_back |
Browser navigation |
web_search |
Search query |
terminate |
End task with final answer |
Limitations
- This is a specialized web agent, not a general chatbot
- Vision/multimodal input required for full functionality (screenshots)
- Best used with 1440ร900 screen resolution
- Without vision capabilities (text-only mode), quality degrades significantly
- Knowledge cutoff: April 2026
Credits
- Model: Microsoft Research AI Frontiers (microsoft/Fara1.5-9B)
- Quantization: llama.cpp project (ggerganov/llama.cpp)
- GGUF conversion by: legifx
License
MIT License โ same as the original model.
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