Prerequisites
Feature Description
Please consider adding support for Ring-1T and Ling-1T models.
In this discussion bartowski mentioned it is not yet supported in llama.cpp yet: https://huggingface.co/inclusionAI/Ring-1T-preview/discussions/5 - hence no GGUF files for this model yet.
Motivation
It is a series of 1T modes that appeared recently:
https://huggingface.co/inclusionAI/Ring-1T-preview
https://huggingface.co/inclusionAI/Ling-1T
https://huggingface.co/inclusionAI/Ring-1T
It potentially may be even better than Kimi K2, and Ring-1T has thinking capability that Kimi K2 lacks. This model claimed to be one of the best open weight models. It would be awesome to run it locally.
Support for it would help greatly to keep memory requirement reasonable and performance good, for example by allowing to run it as IQ4 GGUF quant (the same quant type I use to run Kimi K2 as my daily driver, which is also 1T model) - great for 768GB or 1TB systems where FP8 would not fit; lower GGUF quants like IQ2 or IQ3 potentially could work on 512 GB systems, making it more accessible (as accessible as running 1T model can be).
Possible Implementation
No response
Prerequisites
Feature Description
Please consider adding support for Ring-1T and Ling-1T models.
In this discussion bartowski mentioned it is not yet supported in llama.cpp yet: https://huggingface.co/inclusionAI/Ring-1T-preview/discussions/5 - hence no GGUF files for this model yet.
Motivation
It is a series of 1T modes that appeared recently:
https://huggingface.co/inclusionAI/Ring-1T-preview
https://huggingface.co/inclusionAI/Ling-1T
https://huggingface.co/inclusionAI/Ring-1T
It potentially may be even better than Kimi K2, and Ring-1T has thinking capability that Kimi K2 lacks. This model claimed to be one of the best open weight models. It would be awesome to run it locally.
Support for it would help greatly to keep memory requirement reasonable and performance good, for example by allowing to run it as IQ4 GGUF quant (the same quant type I use to run Kimi K2 as my daily driver, which is also 1T model) - great for 768GB or 1TB systems where FP8 would not fit; lower GGUF quants like IQ2 or IQ3 potentially could work on 512 GB systems, making it more accessible (as accessible as running 1T model can be).
Possible Implementation
No response