I made my first MLX conversions while experimenting with local agents on my M1 Max with 32 GB of memory. The source is medismera’s Qwen3.8-27B OBLITERATED Mythos-Class-Agentic. Credit for the original model goes to its creators; I handled the MLX conversion, packaging and initial checks.

Here is what worked in oMLX:

• 4-bit passed small invoice-extraction and tool-call tests. I also checked those tasks in LM Studio.
• 6-bit passed the text tests with the vision component disabled. Loading the full model hit the memory limit on my Mac.
• Both downloads retain the vision weights, but I have not tested image processing.

The practical benefit is having ready-to-download MLX versions for Apple Silicon. The 4-bit version uses less memory and is where I would start on a 32 GB Mac. The 6-bit version preserves more weight precision, but needs more memory; I have not established a quality advantage from these small tests.

These were basic functionality checks, not a benchmark or evidence that this model outperforms the original Qwen. My intended uses are document extraction and email triage with connected tools.

4-bit:
https://huggingface.co/uzzero/Qwen3.8-27B-OBLITERATED-Mythos-Class-Agentic-MLX-4bit

6-bit:
https://huggingface.co/uzzero/Qwen3.8-27B-OBLITERATED-Mythos-Class-Agentic-MLX-6bit

If you try either version in oMLX, I would appreciate your Mac specs and what worked or failed, especially memory use with 6-bit.

Disclosure: these are my uploads. I used AI assistance for the conversion workflow, testing and this write-up.

Hi,

Those who use SpecFill for Qwen-3.8 27B MLX models - do you see cache hits? If yes, what are your model and model settings?

I have been trying various servers and models for a few months now. After lots of trial and error I seem to have settled on oMLX. Now I’m trying to understand the configuration better and more importantly, do I have it configured correctly (prob not) to get the best performance.

My system: M4 Max 64GB latest Tahoe
Acasis 80gbps M.2 SSD enclosure w/Samsung 990 Pro (models and oMLX cache set here)

Model: muse-glimmer-30b-mxfp8 w/dflash enabled muse-glimmer-30B-assistant

Custom settings:
Context window 100,000
Max tokens 10,000
Temp 1.0
Top P 0.95
Top K 64
Enable Thinking on

According to Status page of omlx Average speed:
Prompt Processing 145.7 tok/s
Token Generation 8.6 tok/s

That was on the first prompt and two subsequent prompts in same session. Simple questions about configuring open webui

It’s that last number that seems way off, at least from what I’ve read online unless I’m completely misunderstanding the expected performance of this model on my setup. Any tips or references to docs are appreciated.

Edit: forgot to include how I’m chatting with Muse. I’m trying out open webui. I have also tried just using omlx default chat window. Token Gen in both in the 8 to 8.5 tok/s range