gearwave00001/Huihui-Qwen3.8-27B-Quark-MXFP4-MagicQuant-GGUF

The repository contains huihui-ai/Huihui-Qwen3.8-27B-abliterated converted to the AMD Quark MXFP4 format using the AMD specified 128 calibration samples @ 512 sequences. The resulting gearwave00001/Huihui-Qwen3.8-27B-Quark-AWQ-MXFP4 was then converted to BF16 gguf and then MQ-IQ4_XS gguf using https://github.com/magiccodingman/MagicQuant.

magiccodingman/Qwen3.8-27B-MXFP4-MagicQuant-GGUF

MagicQuant Hybrids - Qwen3.8-27B / qwen3_5

MagicQuant is a benchmark driven GGUF hybrid discovery and validation system focused on finding real, practical GGUF quants specific to each architecture.

Whether it's a pure baseline model built by llama.cpp, learned tensor configurations from Unsloth, or a custom built MagicQuant hybrid, the model table below shows quants that have won dominance checks, survived collapse spaces, and/or were found to be nonlinearly better. Instead of dumping every quant type possible, MagicQuant tests, validates, and brutally murders anything deemed unworthy.

<details> <summary>Support MagicQuant</summary>

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<details> <summary>Clone Notice</summary>

This repository did not run through the full MagicQuant discovery pipeline. It is a clone of the final survivor tensor configurations from /mnt/NVME2/AI/MagicQuant-campaign/magicquant.clone-source.json, rebuilt and benchmarked locally for this model.

The archived MagicQuant JSON files in magicquant-manifest/ are copied from the source release for durability. The clone benchmark JSON and the table below are from this clone run, so those metrics reflect the rebuilt outputs in this repository.

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Final survivors

<details> <summary>Provider credits</summary>

• —llama.cpp — Baseline quantization formats and llama.cpp tooling.

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<details> <summary>Warning - Is MagicQuant Better? (hint: how you frame the question matters)</summary>

External/custom baselines are normalized into MagicQuant's controlled comparison flow. MagicQuant rebuilds a learned baseline under native-source / MagicQuant-controlled conditions, including its own imatrix handling, so hybrids or external baselines (like Unsloth) can be judged on a more equal footing. That does not mean MagicQuant proved the original upstream artifact or upstream imatrix was worse. These comparisons exist for internal hybrid-search consistency and equal playing field comparisons, not as a universal judgment of the original creator's exact release artifact.

MagicQuant learns tensor quantization assignments and rebuilds from local source weights. It does not automatically reproduce a provider's additional weight transformations, calibration recipes, custom processing, or other techniques unless explicitly supported. These results are not a byte-for-byte reproduction or a test of the provider's original GGUF.

Easier to digest explanation:

MagicQuant compares and benchmarks the models quant to tensor configurations, but not the original artifact. And there's different reasons MagicQuant chooses to lift up a winning quant, not all winners are purely "better". It depends heavily on a variety of factors. Though choices are always documented in the repo under the manifest folder. You can always view what and why decisions were made by the automated system.

So, MagicQuant can confidently tell you, "under the same quantization to tensor configurations and identical imatrix, with this benchmark, I deemed this a winner".

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<details> <summary>Re-Uploading External Provider Baselines</summary>

By default, if an external provider like Unsloth is deemed the winner, the repo should generally link directly to the original provider instead of re-hosting the quant. External GGUFs are normally only re-uploaded when a specific winning variant does not already exist (e.g. Heretic models or similar).

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Release metadata

• —Clone tensor configs — exact per-GGUF tensor quantization maps for reproducing this final output list in repository clone mode.
• —Clone benchmark summary — fresh benchmark results from this clone run.