Instructions to use Abiray/Fara1.5-27B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Abiray/Fara1.5-27B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Abiray/Fara1.5-27B-GGUF",
	filename="Fara1.5-27B.Q3_K_M.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 Abiray/Fara1.5-27B-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 Abiray/Fara1.5-27B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Abiray/Fara1.5-27B-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 Abiray/Fara1.5-27B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Abiray/Fara1.5-27B-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 Abiray/Fara1.5-27B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Abiray/Fara1.5-27B-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 Abiray/Fara1.5-27B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Abiray/Fara1.5-27B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Abiray/Fara1.5-27B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Abiray/Fara1.5-27B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Abiray/Fara1.5-27B-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": "Abiray/Fara1.5-27B-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/Abiray/Fara1.5-27B-GGUF:Q4_K_M

Ollama
How to use Abiray/Fara1.5-27B-GGUF with Ollama:
```
ollama run hf.co/Abiray/Fara1.5-27B-GGUF:Q4_K_M
```

Unsloth Studio

How to use Abiray/Fara1.5-27B-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 Abiray/Fara1.5-27B-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 Abiray/Fara1.5-27B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Abiray/Fara1.5-27B-GGUF to start chatting

How to use Abiray/Fara1.5-27B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Abiray/Fara1.5-27B-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": "Abiray/Fara1.5-27B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Abiray/Fara1.5-27B-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 Abiray/Fara1.5-27B-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 Abiray/Fara1.5-27B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Abiray/Fara1.5-27B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Abiray/Fara1.5-27B-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 "Abiray/Fara1.5-27B-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 Abiray/Fara1.5-27B-GGUF with Docker Model Runner:
```
docker model run hf.co/Abiray/Fara1.5-27B-GGUF:Q4_K_M
```

Lemonade

How to use Abiray/Fara1.5-27B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Abiray/Fara1.5-27B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Fara1.5-27B-GGUF-Q4_K_M

List all available models

lemonade list

Fara1.5-27B - GGUF Quants & Multimodal Projectors

This repository contains GGUF quantizations and multimodal projector (mmproj) files for microsoft/Fara1.5-27B.

Original Model: microsoft/Fara1.5-27B
Developer: Microsoft Research AI Frontiers
Base Architecture: Qwen3.5-27B (Multimodal Vision-Language / Computer Use Agent)
Quantization Format: GGUF (Q3_K_M, Q4_K_M, Q4_K_S, Q5_K_M, Q5_K_S, Q6_K, Q8_0) + mmproj vision projectors

Repository Files

Model Weights (`.gguf`)

File Name	Size	Quant Method	Description
`Fara1.5-27B.Q3_K_M.gguf`	~13.3 GB	`Q3_K_M`	3-bit medium. Lowest VRAM footprint.
`Fara1.5-27B.Q4_K_S.gguf`	~15.6 GB	`Q4_K_S`	4-bit small. Balanced size and performance.
`Fara1.5-27B.Q4_K_M.gguf`	~16.5 GB	`Q4_K_M`	4-bit medium. Recommended balance of speed, memory usage, and quality.
`Fara1.5-27B.Q5_K_S.gguf`	~18.7 GB	`Q5_K_S`	5-bit small. High quality, lower loss.
`Fara1.5-27B.Q5_K_M.gguf`	~19.2 GB	`Q5_K_M`	5-bit medium. Superior precision.
`Fara1.5-27B.Q6_K.gguf`	~22.1 GB	`Q6_K`	6-bit quantization. Extremely close to native FP16 accuracy.
`Fara1.5-27B.Q8_0.gguf`	~28.6 GB	`Q8_0`	8-bit quantization. Maximum accuracy.

Multimodal Vision Projectors (`.mmproj-*.gguf`)

(Required to pass screenshot images into the model in llama.cpp)

File Name	Size	Format	Description
`Fara1.5-27B.mmproj-q8_0.gguf`	~629 MB	`Q8_0`	Quantized vision projector (recommended for low VRAM).
`Fara1.5-27B.mmproj-bf16.gguf`	~931 MB	`BF16`	Native BFloat16 vision projector.
`Fara1.5-27B.mmproj-f16.gguf`	~931 MB	`FP16`	Standard FP16 vision projector.

Quickstart Guide

Because Fara1.5-27B processes screenshots, you must pass both a model weight file (-m) AND a multimodal projector file (--mmproj) during inference in llama.cpp.

1. Running with `llama.cpp`

# Clone and build llama.cpp
git clone [https://github.com/ggml-org/llama.cpp](https://github.com/ggml-org/llama.cpp)
cd llama.cpp && cmake -B build -DGGML_CUDA=ON && cmake --build build --config Release -j

# Download the model and vision projector from this repository
huggingface-cli download Abiray/Fara1.5-27B-GGUF Fara1.5-27B.Q4_K_M.gguf --local-dir .
huggingface-cli download Abiray/Fara1.5-27B-GGUF Fara1.5-27B.mmproj-f16.gguf --local-dir .

# Serve with llama-server
./build/bin/llama-server \
  -m Fara1.5-27B.Q4_K_M.gguf \
  --mmproj Fara1.5-27B.mmproj-f16.gguf \
  -ngl 99 \
  --port 8000 \
  -c 262144

Downloads last month: 1,057

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for Abiray/Fara1.5-27B-GGUF

Base model

Qwen/Qwen3.5-27B

Finetuned

microsoft/Fara1.5-27B

Quantized

(5)

this model

Fara1.5-27B - GGUF Quants & Multimodal Projectors

Repository Files

Model Weights (.gguf)

Multimodal Vision Projectors (.mmproj-*.gguf)

Quickstart Guide

1. Running with llama.cpp

Model tree for Abiray/Fara1.5-27B-GGUF

Model Weights (`.gguf`)

Multimodal Vision Projectors (`.mmproj-*.gguf`)

1. Running with `llama.cpp`