https://qwen.ai/blog?id=qwen3.8
The model weights will be open-sourced on Hugging Face and ModelScope next week — stay tuned.
I’m hoping they also release a new 35b a3b, for us VRAM poors, a new 9b would also be great!
https://qwen.ai/blog?id=qwen3.8
The model weights will be open-sourced on Hugging Face and ModelScope next week — stay tuned.
I’m hoping they also release a new 35b a3b, for us VRAM poors, a new 9b would also be great!
The setup described in this Codacus video is what got me started getting over 100k context with decent speeds on my RTX 3070 that I’ve been using daily for the last month: https://m.youtube.com/watch?v=0AqpaFm11oI.
The TheTom fork of llama.cpp adds asymmetric TurboQuant support that allows k at tq4 and v at tq2, which allows squeezing in more KV cache without quality loss. The REAP version of the MoE model also works just fine while further reducing the model size.
The TheTom fork doesn’t have pre-built container images, so it’s necessary to build your own (the ROCm Dockerfile in the devops directory).
If anyone does want to use the fork, I just recently built the ROCm image myself and ran into an issue where the fork’s Dockerfile was pulling a UI build package from Huggingface that doesn’t exist anymore, so I swapped in the latest ROCm Dockerfile from upstream that now builds the UI from source, and that worked fine.