Text Generation
Transformers
Safetensors
English
qwen3
text-generation-inference
ui-generation
tailwind-css
html
conversational
Instructions to use Tesslate/UIGEN-T3-4B-Preview-MAX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tesslate/UIGEN-T3-4B-Preview-MAX with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Tesslate/UIGEN-T3-4B-Preview-MAX") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("Tesslate/UIGEN-T3-4B-Preview-MAX") model = AutoModelForMultimodalLM.from_pretrained("Tesslate/UIGEN-T3-4B-Preview-MAX") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Tesslate/UIGEN-T3-4B-Preview-MAX with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Tesslate/UIGEN-T3-4B-Preview-MAX" # 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-T3-4B-Preview-MAX", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Tesslate/UIGEN-T3-4B-Preview-MAX
- SGLang
How to use Tesslate/UIGEN-T3-4B-Preview-MAX with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Tesslate/UIGEN-T3-4B-Preview-MAX" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Tesslate/UIGEN-T3-4B-Preview-MAX", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "Tesslate/UIGEN-T3-4B-Preview-MAX" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Tesslate/UIGEN-T3-4B-Preview-MAX", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Tesslate/UIGEN-T3-4B-Preview-MAX with Docker Model Runner:
docker model run hf.co/Tesslate/UIGEN-T3-4B-Preview-MAX
- Xet hash:
- 4be859812134a8f6ffd9b061704208cc7a8d896cd72d2424aec8dadc98f57c97
- Size of remote file:
- 4.97 GB
- SHA256:
- 0600c373410254b5130d6ddfd303d370334277ed806ee805e423d2325abb0410
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