Instructions to use batiai/Fara-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use batiai/Fara-7B-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 batiai/Fara-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf batiai/Fara-7B-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 batiai/Fara-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf batiai/Fara-7B-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 batiai/Fara-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf batiai/Fara-7B-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 batiai/Fara-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf batiai/Fara-7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/batiai/Fara-7B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use batiai/Fara-7B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "batiai/Fara-7B-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": "batiai/Fara-7B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/batiai/Fara-7B-GGUF:Q4_K_M

Ollama
How to use batiai/Fara-7B-GGUF with Ollama:
```
ollama run hf.co/batiai/Fara-7B-GGUF:Q4_K_M
```

Unsloth Studio

How to use batiai/Fara-7B-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 batiai/Fara-7B-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 batiai/Fara-7B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for batiai/Fara-7B-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use batiai/Fara-7B-GGUF with Docker Model Runner:
```
docker model run hf.co/batiai/Fara-7B-GGUF:Q4_K_M
```

Lemonade

How to use batiai/Fara-7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull batiai/Fara-7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Fara-7B-GGUF-Q4_K_M

List all available models

lemonade list

Microsoft Fara-7B GGUF — Quantized by BatiAI

imatrix-calibrated GGUF quantizations of microsoft/Fara-7B (Qwen 2.5 VL based multimodal, 7B). Quantized directly from official Microsoft BF16 weights by BatiAI.

Why Fara-7B?

Microsoft-built agentic multimodal model (Qwen 2.5 VL backbone)
7B parameters — runs on Mac mini 16GB
Multimodal native: text + vision (image-text-to-text)
128K context window
MIT license — fully commercial-friendly
arxiv:2511.19663 — research paper
Released 2026-05-19 by Microsoft

Quick Start

# Q4_K_M (recommended for most users, ~5GB)
ollama pull batiai/fara-7b:q4

# IQ3_XXS (smallest, ~3GB, Mac mini 16GB)
ollama pull batiai/fara-7b:iq3

# Q8_0 (highest quality, ~8GB)
ollama pull batiai/fara-7b:q8

Available Quantizations

Quant	Size	Min RAM	Target Hardware
IQ3_XXS	~3 GB	8 GB	Mac mini M4 16GB
Q3_K_M	~3.5 GB	8 GB	Mac mini 16GB
IQ4_XS	~4 GB	10 GB	Mac mini 16GB+
Q4_K_M	~5 GB	10 GB	Mac mini 16GB+ (recommended)
Q5_K_M	~5.5 GB	12 GB	Mac mini 16GB+
Q6_K	~6.5 GB	14 GB	Mac mini 24GB+
Q8_0	~8 GB	16 GB	Mac mini 24GB+

Multimodal: download mmproj-*-Q6_K.gguf and use with llama-mtmd-cli / llama-server --mmproj.

How to run

Ollama (text-only)

ollama run batiai/fara-7b:q4

llama.cpp (text + vision)

hf download batiai/Fara-7B-GGUF --include "*Q4_K_M*" --include "mmproj-*-Q6_K.gguf" --local-dir ./fara-7b

llama-mtmd-cli \
    -m ./fara-7b/microsoft-Fara-7B-Q4_K_M.gguf \
    --mmproj ./fara-7b/mmproj-microsoft-Fara-7B-Q6_K.gguf \
    --image input.jpg -p "Describe this image."

Model details

Source: microsoft/Fara-7B
Architecture: Qwen2_5_VLForConditionalGeneration — Qwen 2.5 VL backbone, Microsoft fine-tuned
Context: 128K
License: MIT

BatiAI signing

All GGUFs carry:

general.author = BatiAI
general.url = https://flow.bati.ai

License

Inherits source: MIT.

About BatiFlow

BatiFlow — free on-device AI automation for Mac.

Benchmarks coming once Mac measurements complete.

Downloads last month: 340

GGUF

Model size

8B params

Architecture

qwen2vl

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for batiai/Fara-7B-GGUF

Base model

microsoft/Fara-7B

Quantized

(16)

this model

Paper for batiai/Fara-7B-GGUF

Fara-7B: An Efficient Agentic Model for Computer Use

Paper • 2511.19663 • Published Nov 24, 2025 • 22