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Situus/STARK-WEB-12B-v1.7-gguf
STARK-WEB-12B-v1.7-gguf is a text generation model from Situus. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as apache-2.0.
<div style="background: linear-gradient(135deg, 1e3a8a 0%, 3b82f6 100%); padding: 32px 20px; border-radius: 12px; margin-bottom: 24px; text-align: center; color: white; box-shadow: 0 4px 20px rgba(59, 130, 246, 0.15);…
Downloads · 30 days
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From the Hugging Face model README
⚡ 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, fixedenable_thinkingchat template perfectly tailored for offline usage.🔗 Looking for the original Safetensors model? Click here to visit the base model repository (STARK-WEB-12B-v1.7)
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.
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.
<div style="border: 1px solid #e5e7eb; border-left: 4px solid #3b82f6; padding: 16px; border-radius: 8px; margin: 20px 0; background-color: rgba(59, 130, 246, 0.02);"> <h3 style="margin-top: 0; color: #1e3a8a; display: flex; align-items: center; gap: 8px;"> <span>⚙️</span> The 9 Reasoning Stages </h3> <ol style="line-height: 1.6; padding-left: 20px; margin-bottom: 0;"> <li><b>Understand the Goal:</b> Analyze request scope, interface goals, and overall user flow.</li> <li><b>Inputs:</b> Map variables, event triggers, user inputs, and storage needs.</li> <li><b>Output:</b> Establish DOM results, target screens, and state resets.</li> <li><b>Identify Key Constraints & Technologies:</b> Lock down browser APIs, grid constraints, and physics bounds.</li> <li><b>Design the HTML Structure:</b> Plan semantic tag hierarchies and container distributions.</li> <li><b>Design the CSS:</b> Map custom color variables (HSL), responsive layouts, transitions, and glow shadows.</li> <li><b>Design the JavaScript:</b> Lay out event handlers, physics intervals, and rendering cycles.</li> <li><b>Refinement & Implementation:</b> Synthesize code cleanups, performance tweaks, and responsiveness.</li> <li><b>Final Review:</b> Final validation checklist (errors, edge-case safety, formatting).</li> </ol> </div>The thinking process executes seamlessly between the <|channel>thought and <channel|> tags.
The model works best when you clearly define your technical and visual requirements.
Write a fully functional Snake game in a single HTML file.
Use JavaScript and the Canvas API. Implement a modern, sleek Dark Mode design.
The game must include a score counter, collision detection, and the snake should speed up slightly after every apple eaten.
from transformers import AutoTokenizer, AutoModelForCausalLM
# Load the model and tokenizer (replace 'username' with your HF namespace)
tokenizer = AutoTokenizer.from_pretrained("username/stark-web-12b")
model = AutoModelForCausalLM.from_pretrained("username/stark-web-12b", device_map="auto", torch_dtype="auto")
messages = [
{"role": "user", "content": "Make a responsive, beautiful snake game in 1 file"}
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=4096, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=False))
</details>
This model is open-sourced under the Apache 2.0 license.
@misc{situus2026starkweb,
author = {Situus},
title = {STARK-WEB-12B v1.7: Optimizing Open Source Large Language Models for Structured Web Application Synthesis},
year = {2026},
publisher = {Hugging Face},
journal = {Hugging Face Model Hub}
}