Instructions to use Tesslate/UIGEN-T1-7B-q8_0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Tesslate/UIGEN-T1-7B-q8_0-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 Tesslate/UIGEN-T1-7B-q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf Tesslate/UIGEN-T1-7B-q8_0-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Tesslate/UIGEN-T1-7B-q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf Tesslate/UIGEN-T1-7B-q8_0-GGUF:Q8_0

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 Tesslate/UIGEN-T1-7B-q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf Tesslate/UIGEN-T1-7B-q8_0-GGUF:Q8_0

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 Tesslate/UIGEN-T1-7B-q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Tesslate/UIGEN-T1-7B-q8_0-GGUF:Q8_0

Use Docker

docker model run hf.co/Tesslate/UIGEN-T1-7B-q8_0-GGUF:Q8_0

LM Studio
Jan

vLLM

How to use Tesslate/UIGEN-T1-7B-q8_0-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Tesslate/UIGEN-T1-7B-q8_0-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": "Tesslate/UIGEN-T1-7B-q8_0-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Tesslate/UIGEN-T1-7B-q8_0-GGUF:Q8_0

Ollama
How to use Tesslate/UIGEN-T1-7B-q8_0-GGUF with Ollama:
```
ollama run hf.co/Tesslate/UIGEN-T1-7B-q8_0-GGUF:Q8_0
```
Unsloth Desktop

How to use Tesslate/UIGEN-T1-7B-q8_0-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Tesslate/UIGEN-T1-7B-q8_0-GGUF:Q8_0

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Tesslate/UIGEN-T1-7B-q8_0-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Tesslate/UIGEN-T1-7B-q8_0-GGUF with Docker Model Runner:
```
docker model run hf.co/Tesslate/UIGEN-T1-7B-q8_0-GGUF:Q8_0
```

Lemonade

How to use Tesslate/UIGEN-T1-7B-q8_0-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Tesslate/UIGEN-T1-7B-q8_0-GGUF:Q8_0

Run and chat with the model

lemonade run user.UIGEN-T1-7B-q8_0-GGUF-Q8_0

List all available models

lemonade list

Hermes Agent

How to use Tesslate/UIGEN-T1-7B-q8_0-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 Tesslate/UIGEN-T1-7B-q8_0-GGUF:Q8_0

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 Tesslate/UIGEN-T1-7B-q8_0-GGUF:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Tesslate/UIGEN-T1-7B-q8_0-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Tesslate/UIGEN-T1-7B-q8_0-GGUF:Q8_0

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 "Tesslate/UIGEN-T1-7B-q8_0-GGUF:Q8_0" \
  --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"

A newer version of this model is available: Tesslate/UIGEN-T1.1-Qwen-14B

Model Card for UIGEN-T1

Model Summary

UIGEN-T1 is a 7-billion parameter transformer model fine-tuned on Qwen2.5-Coder-7B-Instruct. It is designed for reasoning-based UI generation, leveraging a complex chain-of-thought approach to produce robust HTML and CSS-based UI components. Currently, it is limited to basic applications such as dashboards, landing pages, and sign-up forms.

Model Details

Model Description

UIGEN-T1 generates HTML and CSS-based UI layouts by reasoning through design principles. While it has a strong chain-of-thought reasoning process, it is currently limited to text-based UI elements and simpler frontend applications. The model excels at dashboards, landing pages, and sign-up forms, but lacks advanced interactivity (e.g., JavaScript-heavy functionalities).

Developed by: smirki
Shared by: smirki
Model type: Transformer-based
Language(s) (NLP): English
License: Apache 2.0
Finetuned from model: Qwen2.5-Coder-7B-Instruct

Model Sources

Repository: (Will be uploaded to GitHub soon)
Hosted on: Hugging Face
Demo: Coming soon

Uses

Direct Use

Generates HTML and CSS code for basic UI elements
Best suited for dashboards, landing pages, and sign-up forms
Requires manual post-processing to refine UI outputs
May require using the word "answer" at the end of the input prompt to get better inference

Downstream Use (optional)

Can be fine-tuned further for specific frontend frameworks (React, Vue, etc.)
May be integrated into no-code/low-code UI generation tools

Out-of-Scope Use

Not suitable for complex frontend applications involving JavaScript-heavy interactions
May not generate fully production-ready UI code
Limited design variety – biased towards basic frontend layouts

Bias, Risks, and Limitations

Biases

Strong bias towards basic frontend design patterns (may not generate creative or advanced UI layouts)
May produce repetitive designs due to limited training scope

Limitations

Artifacting issues: Some outputs may contain formatting artifacts
Limited generalization: Performs best in HTML + CSS UI generation, but not robust for complex app logic
May require prompt engineering (e.g., adding "answer" to input for better results)

How to Get Started with the Model

Example Model Template

<|im_start|>user
{question}<|im_end|>
<|im_start|>assistant
<|im_start|>think
{reasoning}<|im_end|>
<|im_start|>answer

Basic Inference Code

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "smirki/UIGEN-T1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name).to("cuda")

prompt = """<|im_start|>user
Make a dark-themed dashboard for an oil rig.<|im_end|>
<|im_start|>assistant
<|im_start|>think
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=12012, do_sample=True, temperature=0.7) #max tokens has to be greater than 12k

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Details

Training Data

Based on: Qwen2.5-Coder-7B-Instruct
Fine-tuned on: UI-related datasets with reasoning-based HTML/CSS examples

Training Procedure

Preprocessing: Standard text-tokenization using Hugging Face transformers
Training Precision: bf16 mixed precision quantized to q8

Evaluation

Testing Data, Factors & Metrics

Testing Data: Internal UI design-related datasets
Evaluation Factors: Bias towards basic UI components, robustness in reasoning, output quality
Metrics: Subjective evaluation based on UI structure, correctness, and usability

Results

Strengths:
- Good at reasoning-based UI layouts
- Generates structured and valid HTML/CSS
Weaknesses:
- Limited design diversity
- Artifacting in outputs

Technical Specifications

Model Architecture and Objective

Architecture: Transformer-based LLM fine-tuned for UI reasoning
Objective: Generate robust frontend UI layouts with chain-of-thought reasoning

Compute Infrastructure

Hardware Requirements: 12GB VRAM reccomended
Software Requirements:
- Transformers library (Hugging Face)
- PyTorch

Citation

If using this model, please cite:

BibTeX:

@misc{smirki_UIGEN-T1,
  title={UIGEN-T1: Chain-of-Thought UI Generation Model},
  author={smirki},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/smirki/UIGEN-T1}
}