u/86obsessed

I don't know if anyone else has ran into these issues but when using Qwen Flash Next, my confidence in it at iq4_xs is high but not 100%. I notice it not following instructions, hallucinating more often and surprisingly it uses way less tokens than 27b. After using Strata.... yes i know.... I thought it was a breath of the next step in Ai. I was mistaken, yes it is good, yes it is fast. Yes it can do better than 27b in some circumstances... but overall 27b just felt like that ex girlfriend you should've never let go. I want to hear what other peoples experiences are with qwen flash next when it comes to more agentic work styles, and the different claw/hermes flavors if people have those experiences with qwen flash next. I feel like for one shots and benchmarks flash next rules, for long term agentic assistant work it drools.. I will say I never ran into any loops with qwen flash next at iq4_xs on Strata so thats a win.

Is this something that would be worth a look into? I really like using unsloth and their tools/quants but I ran into a new project recently called FreeToken. Something they are doing really well is the expert caching for MoE models. unfortunately they do not support multimodal models, only text. I enjoy the simplicity and customization of unsloth but we are talking about 3x token generation speeds and running higher quants its like a whole new world of LLMs. Has Unsloth looked into FreeToken's approach? Specifically things like their dynamic expert caching, bandwidth-adaptive offload, and the CPU+GPU hybrid compute pipeline? I know Unsloth's focus has been heavily on training and GPU optimization, but MoE is becoming the dominant architecture and there's clear demand from people trying to run 125B+ models on consumer GPUs.

When running the qwen3.6 27b mtp model with the UD quant, it's like it takes up considerably more vram. I used to be able to make 110,000 context no problem, now I can only run maybe 60,000 context. When using api calls or even when using studio, it will just die in tool calls or mid generation. Anybody else having that issue with latest update? I've also noticed some new messages in the console when running:

Skipping import of cpp extensions due to incompatible torch version 2.10.0+cu130 for torchao version 0.14.0         Please see GitHub issue #2919 for more info
W0613 21:35:42.766000 26400 Lib\site-packages\torch\distributed\elastic\multiprocessing\redirects.py:29] NOTE: Redirects are currently not supported in Windows or MacOs.
`torch_dtype` is deprecated! Use `dtype` instead!

I mean I might be mistaken but it was working unbelievable good just yesterday and I can't figure out how I can roll back ...didn't do the precautions I typically do when updating... Any help is appreciated!

Edit: I would like to make an edit, when running unsloth and it auto generates a context that should work it puts a context of 110,000

Edit 2: after doing some more testing it seems its only related to the UD variants of 27b model

Last edit: I was able to roll back an update by downloading the git repo and its back to working wonderfully :) unfortunate the update broke it for me, wasnt the llama build or external sources, narrowed it down to unsloth studio update itself. If someone else is running into this or dev's hearing about this or see this, I hope I provided at least some help.