Instructions to use adriabama06/UI-TARS-1.5-7B-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use adriabama06/UI-TARS-1.5-7B-exl2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="adriabama06/UI-TARS-1.5-7B-exl2") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("adriabama06/UI-TARS-1.5-7B-exl2", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use adriabama06/UI-TARS-1.5-7B-exl2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "adriabama06/UI-TARS-1.5-7B-exl2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "adriabama06/UI-TARS-1.5-7B-exl2", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/adriabama06/UI-TARS-1.5-7B-exl2
- SGLang
How to use adriabama06/UI-TARS-1.5-7B-exl2 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 "adriabama06/UI-TARS-1.5-7B-exl2" \ --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": "adriabama06/UI-TARS-1.5-7B-exl2", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'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 "adriabama06/UI-TARS-1.5-7B-exl2" \ --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": "adriabama06/UI-TARS-1.5-7B-exl2", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use adriabama06/UI-TARS-1.5-7B-exl2 with Docker Model Runner:
docker model run hf.co/adriabama06/UI-TARS-1.5-7B-exl2
Wow so fast!
thanks this model is great! I was using bnb and transformers for UITARS-1.5 2B for computer use
...this is so much faster on exllamav2!!!!
It took a l;ittle work to rearrange my pipeline but works flawlessly
I wish you could please quantise openCUA7B?? Holo1.5?
was a lot of hard work but got OpenCUA7B quantised working on exllamav2... needed to build a custom arcitecture profile and some epxerimentation
https://huggingface.co/sujitvasanth/OpenCUA-7B-exl2
just uploading at present and successfuly tried basic image-text prompts in ubuntu havent fully tested or optimised yet.
Sorry, I just saw your message.
I'm pretty busy with college and don't have much free time. When I have time and see interesting unquantized models, I'll continue quantizing more models.
I got openCUA fully working now .. its a non-standard model so had to develop special inference code to replace the transformer custom code and needed exllamav2/architecture.py monkeypatch for quantization and inference.
lease checj it out at https://huggingface.co/sujitvasanth/OpenCUA-7B-exl2
I've also written some special "computer use" inference software for your and my models and for other computer use models such as Halo1.5