Instructions to use inclusionAI/Ming-flash-omni-2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use inclusionAI/Ming-flash-omni-2.0 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("inclusionAI/Ming-flash-omni-2.0", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Ming-flash-omni-2.0-GGUF or Support Llama.cpp ?
#10
by Rebis - opened
Hi,
Would it be possible to make a GGUF version or make LLama.cpp support this model for future quantization ?
Thank you in advance.
Thank you for your suggestion.
We will work on adding support for GGUF and llama.cpp. According to our roadmap, we will prioritize Int8/Int4 quantization combined with vLLM capabilities.
Please stay tuned.