Instructions to use inclusionAI/Ming-Lite-Omni with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use inclusionAI/Ming-Lite-Omni with Transformers:
# Load model directly from transformers import AutoModelForSeq2SeqLM model = AutoModelForSeq2SeqLM.from_pretrained("inclusionAI/Ming-Lite-Omni", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Request for quantized version for 24GB VRAM (and also a gradio GUI demo script)
#4
by mingyi456 - opened
It would be great if this model can squeeze into 24GB of VRAM with 8 bit quantization, either int8 or fp8. Also, it is much more convenient to have a python script that provides a gradio GUI, compared to running the code manually.
mingyi456 changed discussion title from Request for quantized version for 24GB VRAM (also a gradio GUI demo script) to Request for quantized version for 24GB VRAM (and also a gradio GUI demo script)
The quantized version and demo is on the way, please refer to our response to the github issue: https://github.com/inclusionAI/Ming/issues/12#issuecomment-2977029447