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

Libraries

How to use prithivMLmods/Fara-7B-AIO-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="prithivMLmods/Fara-7B-AIO-GGUF")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/Fara-7B-AIO-GGUF", dtype="auto")

llama-cpp-python

How to use prithivMLmods/Fara-7B-AIO-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="prithivMLmods/Fara-7B-AIO-GGUF",
	filename="Fara-7B.BF16.gguf",
)

llm.create_chat_completion(
	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"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use prithivMLmods/Fara-7B-AIO-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf prithivMLmods/Fara-7B-AIO-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/Fara-7B-AIO-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 prithivMLmods/Fara-7B-AIO-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/Fara-7B-AIO-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 prithivMLmods/Fara-7B-AIO-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/Fara-7B-AIO-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 prithivMLmods/Fara-7B-AIO-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/Fara-7B-AIO-GGUF:Q4_K_M

Use Docker

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

LM Studio
Jan

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Fara-7B-AIO-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": "prithivMLmods/Fara-7B-AIO-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/prithivMLmods/Fara-7B-AIO-GGUF:Q4_K_M

SGLang

How to use prithivMLmods/Fara-7B-AIO-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "prithivMLmods/Fara-7B-AIO-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Fara-7B-AIO-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 images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "prithivMLmods/Fara-7B-AIO-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Fara-7B-AIO-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"
						}
					}
				]
			}
		]
	}'

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

Unsloth Studio new

How to use prithivMLmods/Fara-7B-AIO-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 prithivMLmods/Fara-7B-AIO-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 prithivMLmods/Fara-7B-AIO-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

Docker Model Runner
How to use prithivMLmods/Fara-7B-AIO-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Fara-7B-AIO-GGUF:Q4_K_M
```

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Fara-7B-AIO-GGUF

Fara-7B is Microsoft's first agentic small language model (SLM) with 7 billion parameters, built on Qwen2.5-VL-7B as a multimodal decoder-only architecture specialized for computer use tasks like web automation, shopping, booking travel, restaurant reservations, and account workflows. It processes user goals, browser screenshots, and action history within a 128k token context to generate chain-of-thought reasoning followed by grounded tool calls for actions such as mouse movements, clicks, typing, scrolling, URL visits, and web searches, mimicking human-like desktop interactions without accessibility trees. Trained in just 2.5 days on 64 H100 GPUs using 145k synthetic trajectories from a multi-agent pipeline, it achieves state-of-the-art results in its size class—73.5% on WebVoyager, 38.4% on WebTailBench—outperforming peers like UI-TARS-7B while incorporating safety safeguards to halt at critical points (e.g., purchases, personal info entry) and refuse harmful tasks.[1][2][3][4]

Fara-7B [GGUF]

File Name	Quant Type	File Size	File Link
Fara-7B.BF16.gguf	BF16	15.2 GB	Download
Fara-7B.F16.gguf	F16	15.2 GB	Download
Fara-7B.F32.gguf	F32	30.5 GB	Download
Fara-7B.IQ4_XS.gguf	IQ4_XS	4.25 GB	Download
Fara-7B.Q2_K.gguf	Q2_K	3.02 GB	Download
Fara-7B.Q3_K_L.gguf	Q3_K_L	4.09 GB	Download
Fara-7B.Q3_K_M.gguf	Q3_K_M	3.81 GB	Download
Fara-7B.Q3_K_S.gguf	Q3_K_S	3.49 GB	Download
Fara-7B.Q4_K_M.gguf	Q4_K_M	4.68 GB	Download
Fara-7B.Q4_K_S.gguf	Q4_K_S	4.46 GB	Download
Fara-7B.Q5_K_M.gguf	Q5_K_M	5.44 GB	Download
Fara-7B.Q5_K_S.gguf	Q5_K_S	5.32 GB	Download
Fara-7B.Q6_K.gguf	Q6_K	6.25 GB	Download
Fara-7B.Q8_0.gguf	Q8_0	8.1 GB	Download
Fara-7B.i1-IQ1_M.gguf	i1-IQ1_M	2.04 GB	Download
Fara-7B.i1-IQ1_S.gguf	i1-IQ1_S	1.9 GB	Download
Fara-7B.i1-IQ2_M.gguf	i1-IQ2_M	2.78 GB	Download
Fara-7B.i1-IQ2_S.gguf	i1-IQ2_S	2.6 GB	Download
Fara-7B.i1-IQ2_XS.gguf	i1-IQ2_XS	2.47 GB	Download
Fara-7B.i1-IQ2_XXS.gguf	i1-IQ2_XXS	2.27 GB	Download
Fara-7B.i1-IQ3_M.gguf	i1-IQ3_M	3.57 GB	Download
Fara-7B.i1-IQ3_S.gguf	i1-IQ3_S	3.5 GB	Download
Fara-7B.i1-IQ3_XS.gguf	i1-IQ3_XS	3.35 GB	Download
Fara-7B.i1-IQ3_XXS.gguf	i1-IQ3_XXS	3.11 GB	Download
Fara-7B.i1-IQ4_NL.gguf	i1-IQ4_NL	4.44 GB	Download
Fara-7B.i1-IQ4_XS.gguf	i1-IQ4_XS	4.22 GB	Download
Fara-7B.i1-Q2_K.gguf	i1-Q2_K	3.02 GB	Download
Fara-7B.i1-Q2_K_S.gguf	i1-Q2_K_S	2.83 GB	Download
Fara-7B.i1-Q3_K_L.gguf	i1-Q3_K_L	4.09 GB	Download
Fara-7B.i1-Q3_K_M.gguf	i1-Q3_K_M	3.81 GB	Download
Fara-7B.i1-Q3_K_S.gguf	i1-Q3_K_S	3.49 GB	Download
Fara-7B.i1-Q4_0.gguf	i1-Q4_0	4.44 GB	Download
Fara-7B.i1-Q4_1.gguf	i1-Q4_1	4.87 GB	Download
Fara-7B.i1-Q4_K_M.gguf	i1-Q4_K_M	4.68 GB	Download
Fara-7B.i1-Q4_K_S.gguf	i1-Q4_K_S	4.46 GB	Download
Fara-7B.i1-Q5_K_M.gguf	i1-Q5_K_M	5.44 GB	Download
Fara-7B.i1-Q5_K_S.gguf	i1-Q5_K_S	5.32 GB	Download
Fara-7B.i1-Q6_K.gguf	i1-Q6_K	6.25 GB	Download
Fara-7B.imatrix.gguf	imatrix	4.56 MB	Download
Fara-7B.mmproj-bf16.gguf	mmproj-bf16	1.36 GB	Download
Fara-7B.mmproj-f16.gguf	mmproj-f16	1.35 GB	Download
Fara-7B.mmproj-f32.gguf	mmproj-f32	2.71 GB	Download
Fara-7B.mmproj-q8_0.gguf	mmproj-q8_0	856 MB	Download

Quants Usage

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):

Downloads last month: 1,679

GGUF

Model size

8B params

Architecture

qwen2vl

Hardware compatibility

1-bit

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

32-bit

View +1 variant

Model tree for prithivMLmods/Fara-7B-AIO-GGUF

Base model

microsoft/Fara-7B

Quantized

(13)

this model