Instructions to use ESHMO-AI/UIGEN with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ESHMO-AI/UIGEN with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ESHMO-AI/UIGEN", dtype="auto")

llama-cpp-python

How to use ESHMO-AI/UIGEN with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ESHMO-AI/UIGEN",
	filename="uigen-t3-preview-14b-q8_0.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use ESHMO-AI/UIGEN 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 ESHMO-AI/UIGEN:Q8_0
# Run inference directly in the terminal:
llama cli -hf ESHMO-AI/UIGEN:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ESHMO-AI/UIGEN:Q8_0
# Run inference directly in the terminal:
llama cli -hf ESHMO-AI/UIGEN: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 ESHMO-AI/UIGEN:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf ESHMO-AI/UIGEN: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 ESHMO-AI/UIGEN:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ESHMO-AI/UIGEN:Q8_0

Use Docker

docker model run hf.co/ESHMO-AI/UIGEN:Q8_0

LM Studio
Jan
Ollama
How to use ESHMO-AI/UIGEN with Ollama:
```
ollama run hf.co/ESHMO-AI/UIGEN:Q8_0
```

Unsloth Studio

How to use ESHMO-AI/UIGEN 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 ESHMO-AI/UIGEN 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 ESHMO-AI/UIGEN to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ESHMO-AI/UIGEN to start chatting

How to use ESHMO-AI/UIGEN with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ESHMO-AI/UIGEN:Q8_0

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": "ESHMO-AI/UIGEN:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ESHMO-AI/UIGEN with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ESHMO-AI/UIGEN: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 ESHMO-AI/UIGEN:Q8_0

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use ESHMO-AI/UIGEN with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ESHMO-AI/UIGEN: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 "ESHMO-AI/UIGEN: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"

Docker Model Runner
How to use ESHMO-AI/UIGEN with Docker Model Runner:
```
docker model run hf.co/ESHMO-AI/UIGEN:Q8_0
```

Lemonade

How to use ESHMO-AI/UIGEN with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ESHMO-AI/UIGEN:Q8_0

Run and chat with the model

lemonade run user.UIGEN-Q8_0

List all available models

lemonade list

UIGEN / README.md

eshmoideas

Duplicate from Tesslate/UIGEN-T3-14B-Preview-Q8_0-GGUF

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preview code

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	---
	base_model:
	- Tesslate/UIGEN-T3-14B-Preview
	tags:
	- text-generation-inference
	- transformers
	- qwen3
	- ui-generation
	- tailwind-css
	- html
	language:
	- en
	---

	# UIGEN-T3 — Advanced UI Generation with Hybrid Reasoning

	> Tesslate’s next-gen UI model, built for thoughtful design.

	---

	<img src="https://cdn-uploads.huggingface.co/production/uploads/64d1129297ca59bcf7458d07/fqZE717ubrgSpAOHeMEeQ.png" alt="UIGEN-T3 UI Screenshot 1" width="500">
	<img src="https://cdn-uploads.huggingface.co/production/uploads/64d1129297ca59bcf7458d07/N5gfKRD-j9JAeMoGy7O3K.png" alt="UIGEN-T3 UI Screenshot 2" width="500">
	<img src="https://cdn-uploads.huggingface.co/production/uploads/64d1129297ca59bcf7458d07/PDGXycDkVsxFRxqHc6TKD.png" alt="UIGEN-T3 UI Screenshot 2" width="500">
	<img src="https://cdn-uploads.huggingface.co/production/uploads/64d1129297ca59bcf7458d07/Q4CHYYcDAN60NFjIPVQnH.png" alt="UIGEN-T3 UI Screenshot 3" width="500">
	<img src="https://cdn-uploads.huggingface.co/production/uploads/64d1129297ca59bcf7458d07/ePZoCA8lS4Y1KdkchrTlQ.png" alt="UIGEN-T3 UI Screenshot 4" width="500">
	<img src="https://cdn-uploads.huggingface.co/production/uploads/64d1129297ca59bcf7458d07/BU-Pk2moNsTOGuYF1XuRx.png" alt="UIGEN-T3 UI Screenshot 5" width="500">
	<img src="https://cdn-uploads.huggingface.co/production/uploads/64d1129297ca59bcf7458d07/gz5ub0lDpS-hhw1MB7e07.png" alt="UIGEN-T3 UI Screenshot 6" width="500">

	## Demos

	Explore 39+ UI generations:
	📂 [https://uigenoutput.pages.dev/response_39](https://uigenoutput.pages.dev/response_39)
	(Just change the end number from 1–39 to see more examples!)

	---

	Join our Discord: [https://discord.gg/GNbWAeJ4](https://discord.gg/GNbWAeJ4)
	Our Website: [https://tesslate.com](https://tesslate.com)

	## Quick Information

	* UI generation model built on Qwen3 architecture
	* Supports both components and full web pages
	* Hybrid reasoning system: Use `/think` or `/no_think` modes
	* Powered by UIGenEval, a first-of-its-kind benchmark for UI generation
	* Released for research, non-commercial use. If you want to use it commercially, please contact us for a pilot program.

	---

	## Model Details

	* Base Model: Qwen/Qwen3-14B
	* Reasoning Style: Hybrid (`/think` and `/no_think`)
	* Tokenizer: Qwen default, with design token headers
	* Output: Components + Full pages (with `<html>`, `<head>`)
	* Images: User-supplied or placehold.co – no images in the dataset due to licensing concerns.
	* License: Research only (non-commercial). Contact us for enterprise use cases.

	---

	## Reasoning System

	UIGEN-T3 was trained using a pre/post reasoning model architecture.

	You can explicitly control the reasoning mode:

	* `/think` → Enables guided reasoning with layout analysis and heuristics.
	* `/no_think` → Faster, raw code generation.

	Outputs also include design tokens at the top of each generation for easier site-wide customization.

	---

	## Inference Parameters

	Please use 20k context length to get the best results if using reasoning.

	\| Parameter \| Value \|
	\| :---------- \| :---- \|
	\| Temperature \| 0.6 \|
	\| Top P \| 0.95 \|
	\| Top K \| 20 \|
	\| Max Tokens \| 40k+ \|

	---

	## Evaluation: UIGenEval Framework

	UIGenEval is our internal evaluation suite, designed to bridge the gap between creative output and quality assurance.
	(Learn more in our upcoming paper: "UIGenEval: Bridging the Evaluation Gap in AI-Driven UI Generation" - August, 2025)

	UIGenEval evaluates models across four pillars:

	1. Technical Quality — Clean HTML, CSS structure, semantic accuracy.
	2. Prompt Adherence — Feature completeness and fidelity to instructions.
	3. Interaction Behavior — Dynamic logic hooks and functional interactivity.
	4. Responsive Design — Multi-viewport performance via Lighthouse, Axe-core, and custom scripts.

	This comprehensive framework directly informs our GRPO reward functions for the next release.

	---

	## Example Prompts to Try

	* `make a google drive clone`
	* `build a figma-style canvas with toolbar`
	* `create a modern pricing page with three plans`
	* `generate a mobile-first recipe sharing app layout`

	---

	## Use Cases

	\| Use Case \| Description \|
	\| :---------------------- \| :-------------------------------------------------------------------------- \|
	\| Startup MVPs \| Quickly scaffold UIs from scratch with clean code. \|
	\| Design-to-Code Transfer \| Figma (coming soon) → Code generation. \|
	\| Component Libraries \| Build buttons, cards, navbars, and export at scale. \|
	\| Internal Tool Builders \| Create admin panels, dashboards, and layout templates. \|
	\| Rapid Client Prototypes \| Save time on mockups with production-ready HTML+Tailwind outputs. \|

	---

	## Limitations

	* No Bootstrap support (planned).
	* Not suited for production use — research-only license.
	* Responsive tuning varies across output complexity.

	---

	## Roadmap

	\| Milestone \| Status \|
	\| :-------------------------- \| :----- \|
	\| Launch Tesslate Designer \| 2 days \|
	\| Figma convert \| \|
	\| Bootstrap & JS logic \| \|
	\| GRPO fine-tuning \| \|
	\| 4B draft model release \| Now \|

	---

	## Technical Requirements

	* GPU: ≥16GB VRAM for 14B inference on GGUF.
	* Libraries: `transformers`, `torch`, `peft`.
	* Compatible with Hugging Face inference APIs and local generation pipelines.

	---

	## Community & Contribution

	* Join our Discord: [https://discord.gg/GNbWAeJ4](https://discord.gg/GNbWAeJ4)
	* Chat about AI, design, or model training.
	* Want to contribute UIs or feedback? Let’s talk!

	---

	## Citation

	```bibtex
	@misc{tesslate_UIGEN-T3,
	title={UIGEN-T3: Hybrid Reasoning for Robust UI Generation on Qwen3},
	author={Tesslate Team},
	year={2025},
	publisher={Tesslate},
	note={Non-commercial Research License},
	url={https://huggingface.co/tesslate/UIGEN-T3}
	}