How to use from the
Use from the
llama-cpp-python library
# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="GabForge/GabForge-Mini-v1-GGUF",
	filename="GabForge-Mini-v1-Q4_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"
					}
				}
			]
		}
	]
)

GabForge Mini v1 — Vision + Coding GGUF

The first GabForge model: a fine-tuned Qwen3.5-9B with built-in vision capability, optimized for screenshot→code generation.

Model Details

Property Value
Base Model Qwen3.5-9B (multimodal)
Fine-tuning QLoRA (rank 32), 7,419 WebSight screenshot→code examples
Training 2.15 epochs, final loss 0.15
Quantization Q4_K_M
File Size 5.3 GB
Min VRAM 8 GB
License Apache 2.0

Capabilities

  • Screenshot→Code: Give it a UI screenshot, get HTML/CSS/JS back
  • General Coding: Inherits Qwen3.5-9B's strong coding ability
  • Vision Understanding: Reads UI layouts, diagrams, charts, error screenshots
  • Chat: Standard instruction-following conversational model

Usage

Works with any llama.cpp-compatible inference engine:

# With llama-server
llama-server -m GabForge-Mini-v1-Q4_K_M.gguf --port 8766

# With GabForge AI Studio (automatic)
# Download via Settings → AI Models → Local tab

Made for GabForge AI Studio

This model is the default local model in GabForge AI Studio — the privacy-first AI coding IDE. Vision runs entirely on your machine.

Training Data

  • HuggingFaceM4/WebSight — screenshot→HTML/CSS pairs
  • Additional coding data from Qwen3.5-9B's base knowledge
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GGUF
Model size
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Architecture
qwen35
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