⚡ STARK-WEB-12B v1.7
The Premium Open-Source AI UI/UX Designer & Frontend Engineer
⚡ This is the GGUF Repository for Local Execution. This repository contains the optimized and quantized GGUF variants (f16, q8_0, q4_k_m) specifically designed for LM Studio, llama.cpp, and other local inference clients. All files here feature a built-in, fixed enable_thinking chat template perfectly tailored for offline usage.
🔍 Overview
STARK-WEB-12B v1.7 is an advanced, highly specialized model fine-tuned on top of the Gemma 4 12B architecture. It draws knowledge and architecture from the world's best large language models, engineered to act as a premium UI/UX designer and frontend developer.
The model generates complete, production-grade, single-file web applications combining semantic HTML5, modern styled CSS, and interactive JavaScript. Version 1.7 brings massive improvements to overall coding logic and a huge upgrade in browser game development (Canvas API, physics, and game loops).
Unlike standard coding assistants that output plain layouts, STARK-WEB-12B uses a custom 9-step Chain-of-Thought (CoT) reasoning process. This allows the model to outline logic, palettes, structure, and constraints before writing any code, yielding visually breathtaking interfaces with fluid transitions, HSL color dynamics, glassmorphism, and responsive CSS Grid/Flexbox architectures.
📊 Quick Specifications
| Feature | Technical Details |
|---|
| 🧠 Base Architecture | Gemma 4 12B (via google/gemma-4-12b-it) |
| 📈 Dataset Volume | ~16 Million Tokens (highly-complex refined web app cases) |
| 🎯 Target Capabilities | Single-file dashboards, canvas games, responsive tools, fluid animations |
| 🌍 Language Support | ~90% English, ~10% Polish (fully bilingual comprehension) |
🛡️ Structured Chain-of-Thought (CoT) Workflow
To resolve logical hallucinations and infinite loops, STARK-WEB-12B v1.7 is hard-constrained to reason through a strict 9-step algorithmic roadmap prior to outputting code. This ensures it thinks precisely like a senior frontend developer.
⚙️ The 9 Reasoning Stages
- Understand the Goal: Analyze request scope, interface goals, and overall user flow.
- Inputs: Map variables, event triggers, user inputs, and storage needs.
- Output: Establish DOM results, target screens, and state resets.
- Identify Key Constraints & Technologies: Lock down browser APIs, grid constraints, and physics bounds.
- Design the HTML Structure: Plan semantic tag hierarchies and container distributions.
- Design the CSS: Map custom color variables (HSL), responsive layouts, transitions, and glow shadows.
- Design the JavaScript: Lay out event handlers, physics intervals, and rendering cycles.
- Refinement & Implementation: Synthesize code cleanups, performance tweaks, and responsiveness.
- Final Review: Final validation checklist (errors, edge-case safety, formatting).
The thinking process executes seamlessly between the <|channel>thought and <channel|> tags.
⚠️ Limitations & Best Practices
🛠️ Known Limitations & Bug Fixing
While version 1.7 represents a massive leap forward and is much better at everything, it unfortunately still makes occasional logical errors or typos that can break the code rendering. Generating complex HTML, CSS, and JS simultaneously in a single shot remains a significant challenge, especially for highly advanced games.
💡 How to fix broken code:
If your application renders as a blank screen or has broken handlers, you have two great options:
- Option A (Recommended): Copy the generated code and paste it into a larger, more capable reasoning model with a prompt to "Fix the small syntax/logical errors in this code without changing the UI design." This yields the fastest and most reliable results.
- Option B (Self-Correction): Feed the code back into STARK-WEB-12B itself with the following prompt:
"Review the generated code above. Identify any rendering or logical errors, and output a corrected, fully functional, and complete single-file version."
By using a quick second pass, you can easily bridge this gap and enjoy the stunning frontend designs the model produces.
🖥️ Client Integration Note
Because the model outputs its Chain-of-Thought inside custom structural blocks (<|channel>thought), ensure that your client UI or inference script properly parses out or wraps these tags to avoid mixing raw thoughts with the final code block. If using the GGUF files in LM Studio, ensure you are using a compatible frontend or API that supports parsing these specific custom tags.
🚀 How to Use
1. Example Prompt
The model works best when you clearly define your technical and visual requirements.
1Write a fully functional Snake game in a single HTML file.
2Use JavaScript and the Canvas API. Implement a modern, sleek Dark Mode design.
3The game must include a score counter, collision detection, and the snake should speed up slightly after every apple eaten.
2. Python Inference Snippet
💻 Click to expand Python Hugging Face Transformers code
1from transformers import AutoTokenizer, AutoModelForCausalLM
2
3# Load the model and tokenizer (replace 'username' with your HF namespace)
4tokenizer = AutoTokenizer.from_pretrained("username/stark-web-12b")
5model = AutoModelForCausalLM.from_pretrained("username/stark-web-12b", device_map="auto", torch_dtype="auto")
6
7messages = [
8 {"role": "user", "content": "Make a responsive, beautiful snake game in 1 file"}
9]
10
11prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
12inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
13
14outputs = model.generate(**inputs, max_new_tokens=4096, temperature=0.7)
15print(tokenizer.decode(outputs[0], skip_special_tokens=False))
📜 Citation & License
This model is open-sourced under the Apache 2.0 license.
1@misc{situus2026starkweb,
2 author = {Situus},
3 title = {STARK-WEB-12B v1.7: Optimizing Open Source Large Language Models for Structured Web Application Synthesis},
4 year = {2026},
5 publisher = {Hugging Face},
6 journal = {Hugging Face Model Hub}
7}
