Instructions to use prithivMLmods/Fara1.5-9B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Fara1.5-9B-GGUF with Transformers:

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

pipe = pipeline("image-text-to-text", model="prithivMLmods/Fara1.5-9B-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/Fara1.5-9B-GGUF", device_map="auto")

llama-cpp-python

How to use prithivMLmods/Fara1.5-9B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="prithivMLmods/Fara1.5-9B-GGUF",
	filename="Fara1.5-9B.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 Settings

llama.cpp

How to use prithivMLmods/Fara1.5-9B-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 prithivMLmods/Fara1.5-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/Fara1.5-9B-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 prithivMLmods/Fara1.5-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/Fara1.5-9B-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/Fara1.5-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/Fara1.5-9B-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/Fara1.5-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/Fara1.5-9B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/Fara1.5-9B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use prithivMLmods/Fara1.5-9B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Fara1.5-9B-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/Fara1.5-9B-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/Fara1.5-9B-GGUF:Q4_K_M

SGLang

How to use prithivMLmods/Fara1.5-9B-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/Fara1.5-9B-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/Fara1.5-9B-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/Fara1.5-9B-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/Fara1.5-9B-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/Fara1.5-9B-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/Fara1.5-9B-GGUF:Q4_K_M
```

Unsloth Studio

How to use prithivMLmods/Fara1.5-9B-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/Fara1.5-9B-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/Fara1.5-9B-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/Fara1.5-9B-GGUF to start chatting

How to use prithivMLmods/Fara1.5-9B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/Fara1.5-9B-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": "prithivMLmods/Fara1.5-9B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use prithivMLmods/Fara1.5-9B-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 prithivMLmods/Fara1.5-9B-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 prithivMLmods/Fara1.5-9B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use prithivMLmods/Fara1.5-9B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/Fara1.5-9B-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 "prithivMLmods/Fara1.5-9B-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"

Docker Model Runner
How to use prithivMLmods/Fara1.5-9B-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Fara1.5-9B-GGUF:Q4_K_M
```

Lemonade

How to use prithivMLmods/Fara1.5-9B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/Fara1.5-9B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Fara1.5-9B-GGUF-Q4_K_M

List all available models

lemonade list

Fara1.5-9B-GGUF

Fara1.5-9B is a multimodal computer use agent (CUA) for web browsers from Microsoft Research AI Frontiers, supervised fine-tuned from Qwen3.5-9B on synthetic trajectories generated by FaraGen1.5, a multi-agent pipeline that synthesizes web tasks, executes and verifies them before training. The model operates purely on vision — observing the browser through screenshots rather than the DOM or accessibility tree — and predicts grounded next actions such as pixel-level click coordinates, typing, scrolling, and URL navigation, emitted as XML-tagged <tool_call> blocks following a chain-of-thought reasoning trace, with a 262K token context window supporting long multi-screenshot trajectories. Its critical-points safety design trains the model to pause and request user confirmation before entering personal information, making payments, submitting forms, sending messages, or taking other irreversible actions, and it's designed for co-deployment with MagenticLite for sandboxing, domain allow-listing, watch-mode monitoring, and pause controls. On end-to-end web agent benchmarks, Fara1.5-9B scores 86.6 on WebVoyager, 63.4 on Online-Mind2Web, and 32.3 on WebTailBench — positioned between the smaller Fara1.5-4B and the larger Fara1.5-27B in the same family — and is released under the MIT license, trained on ~1 billion text tokens and under 1 billion images with a March 2026 data cutoff, intended for automating repetitive web tasks like form-filling, shopping, and travel booking within sandboxed, English-only, non-high-stakes deployments.

Model Files

File Name	Quant Type	File Size	File Link
Fara1.5-9B.BF16.gguf	BF16	17.9 GB	Download
Fara1.5-9B.F16.gguf	F16	17.9 GB	Download
Fara1.5-9B.Q3_K_L.gguf	Q3_K_L	4.93 GB	Download
Fara1.5-9B.Q3_K_M.gguf	Q3_K_M	4.62 GB	Download
Fara1.5-9B.Q3_K_S.gguf	Q3_K_S	4.26 GB	Download
Fara1.5-9B.Q4_K_M.gguf	Q4_K_M	5.63 GB	Download
Fara1.5-9B.Q4_K_S.gguf	Q4_K_S	5.35 GB	Download
Fara1.5-9B.Q5_K_M.gguf	Q5_K_M	6.47 GB	Download
Fara1.5-9B.Q5_K_S.gguf	Q5_K_S	6.31 GB	Download
Fara1.5-9B.Q8_0.gguf	Q8_0	9.53 GB	Download
Fara1.5-9B.mmproj-bf16.gguf	mmproj-bf16	922 MB	Download
Fara1.5-9B.mmproj-f16.gguf	mmproj-f16	922 MB	Download
Fara1.5-9B.mmproj-q8_0.gguf	mmproj-q8_0	624 MB	Download

System Prompt

Fara1.5-4B is trained with a specific system prompt. For the best results, use it verbatim.

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.

The model was trained during the period from January to April 2026. You can effectively perform tasks beyond this timeframe by accessing a web browser and using the latest information from the live web. However, your knowledge cutoff is limited to early 2026, so you may not be be aware of events or developments that occurred after that time unless you explicitly browse the web for the latest information.

This edition of the model was trained using supervised fine-tuning (SFT) on top of Qwen3.5-4B, with a synthetic data mixture generated and developed by Microsoft AI Frontiers.

A critical point is a situation where we must pause and request information or confirmation from the user before proceeding. There are three types:

Case 1: Missing User Information — The task requires personal information that the user has not provided (e.g., email address, phone number, postal address, or payment details). Never fabricate or assume personal information. Fill in only the information the user has explicitly provided, then pause and ask for any remaining required fields.

Case 2: Underspecified Task — The task description is ambiguous or lacks the details needed to make a decision at the current step. Pause and ask the user for clarification.

Case 3: Irreversible Action — We are about to perform an action that cannot be undone (e.g., submitting a form, completing a purchase, sending a message, or deleting data). If the user has explicitly authorized the action, proceed. Otherwise, stop and ask for confirmation.

Only stop at a critical point if (1) required information is missing, (2) the task is ambiguous, or (3) an irreversible action lacks explicit user authorization.