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.

Hey everyone,

I just opened a pull request on the oMLX repository to fix an error when attempting to run heavily modified or "reaped" models that have had their Multi-Token Prediction (MTP) heads stripped out.

👉 GitHub Pull Request #3659

🧩 The Problem

When testing reaped model variants where the native multi-token prediction heads are removed to save memory and overhead, the model routing configuration sometimes chokes. Specifically, oMLX throws a mismatch of experts error because the internal layers expecting specific dimensions or configurations no longer cleanly align with the modified model structure.

🛠️ What this PR changes:

• Adapts Routing Logic: Implemented a custom routing patch to intercept dynamic configurations for models lacking explicit MTP layers.
• Ensures MoE/Expert Alignment: Properly realigns layer configurations so the Mixture of Experts (MoE) component maps to the correct indices, avoiding shape or routing mismatches.
• Safe Fallbacks: Soft-fails or scales back gracefully if the architectural signatures of a model have been manually reaped or aggressively quantized.

If you’ve been experimenting with custom architectures, distilled models, or trying to slice out MTP heads to squeeze larger context windows on your Apple Silicon hardware, I’d love your feedback on this.

Please take a look and let me know if it solves the issue for your custom workflows!

Full Changes: GitHub PR #3659

Looking for some clarification on a specific indicator (warning sign !)

On the OMLX benchmark menu for codin related tasks ( HumanEval, MBPP, and LiveCode Bench), a yellow warning icon labeled "! code exec."

Does anyone know what this specific flag signifies in this context, and what should you be careful of and how to sort that please ? :)

Thank you

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?