DEPRECIATED!

This model was surpassed by the new MagicQuant hybrids. The collection can be found here:

https://huggingface.co/collections/magiccodingman/magic-quant

Use the new version. This shown MXFP4 hybrid is no longer viable in comparison, nor really useable. The data collected is good for understanding and research, but it's not as good for real use.

Unsloth - Qwen3 VL 8B Instruct MXFP4 Hybrid GGUF

Dense model utilizing MXFP4_MOE with hybrid weights on a dense model. Achieving interesting results that show smaller file size, more TPS, and near lossless precision.

Use one of the 3 found magic models!

Stats compared against the standard Q8_0 (precision loss still compared to F16)

• MXFP4_MOE-Q6_K

  5.2% smaller than Q8 • 264.49 TPS • 0.0992% precision loss

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• MXFP4_MOE-output_q6_K-router_gate_emb_q6_K

  10.1% smaller than Q8 • 247.84 TPS • 0.1078% precision loss

  (TLDR: The perfect balance)

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This repository contains a set of hybrid MXFP4 quantized GGUF models designed to explore a surprising discovery:

A carefully targeted combination of MXFP4 + high-precision embeddings/output weights can deliver near-Q8 accuracy with Q4–Q6 level throughput and smaller file sizes than Q8.

Unlike pure MXFP4, which heavily degrades dense models. This hybrid method selectively protects tensors that matter most for semantic stability, while allowing MXFP4 to accelerate everything else.

This is experimental. And should be treated as such. I am more than encouraging people to use these models and leave feedback! Though precision loss seemed near lossless, did the hybrid models act strange in certain situations? Worse or better on some topics compared to the original model? Did it do better/worse overall on everything? I'd love to hear back from others!

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The Magic Models - Use One Of These 3 Models!

Each of these models achieved:

File size reduction compared to the Q8_0

Better precision loss scores than the pure Q6_K

Achieving noticeably better TPS than a Q4_K_M

I have personally deemed these in the category of "Q7.5" quantization.

The following are the special models to note from what was created. Each of the 3 models shown below are being compared to the Q8 model.

MXFP4_MOE-Q8

(5.2% smaller than Q8 • 264.49 TPS • 0.0992% precision loss )

Honestly, this one is hands down the best. Best TPS, lowest precision loss, this is the one you want.

The following was the conversion script:

llama-quantize \
  --tensor-type token_embd.weight=Q8_0 \
  --tensor-type output.weight=Q8_0 \
  "Path_To_F16_GGUF.gguf" \
  "Path_To_GGUF.gguf" \
  mxfp4_moe

MXFP4_MOE-output_q6_K-router_gate_emb_q6_K

(10.1% smaller than Q8 • 247.84 TPS • 0.1078% precision loss)

Still a great version, but you really only want this if you truly can't spare the extra 400 MB to use the MXFP4_MOE-Q8 instead.

The following was the conversion script:

llama-quantize \
  --tensor-type token_embd.weight=Q6_K \
  --tensor-type output.weight=Q6_K \
  --tensor-type 'router.*'=Q6_K \
  --tensor-type 'gate.*'=Q6_K \
  "Path_To_F16_GGUF.gguf" \
  "Path_To_GGUF.gguf" \
  mxfp4_moe

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MXFP4_MOE Hybrid Naming Scheme & Synopsis

Multiple different combinations of converted models were created. The results were interesting to say the least. The following table will explain my naming scheme to what was done to the model to create it.

The results achieved were interesting to say the least. It was a brute force game of mass creating models with hybrid methods to find combinations that didn't cause too much noise and paired well with MXFP4.

This repo showcases the converted models, whether good or bad that was created. But, I have been testing other models in different combinations as well. The winning hybrid combinations shown in this repo DOES NOT always equate to the same results on different models.

Some models do better or worse with different kinds of combinations. It depends if it's dense, MOE, and much more. Many times the results surprise me. Many models no matter the combination will not play nice with MXFP4. At least with the methods shown here.

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Benchmark Methodology

All models were tested with a unified automated harness using llama.cpp tools.

Included tests:

• Throughput:
  llama-bench with descending GPU offload (-ngl 35 → 0) and automatic OOM retry.
  Highest successful TPS is recorded.

• Perplexity:
  Three domains: general, code, math.
  Each uses an auto-generated corpus of ~32k tokens.
  Perplexity is computed with llama-perplexity at 2048-token context.
  Same GPU retry logic as above.

• Precision loss:
  Each model is compared to its family F16 baseline.
  Precision-loss % is computed for all PPL domains, plus an averaged score.
  Models are ranked by this metric.

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Table - Overview of Results

Comparing to F16.

• All percentages compared against the selected family F16 baseline.

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Table - File Size + TPS + Avg Precision Loss

• Bench NGL was 35
• Utilized CUDA

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Table - PPL Columns

• gen = ppl_general
• gen_er = ppl_general_error
• code = ppl_code
• code_er = ppl_code_error
• math = ppl_math
• math_er = ppl_math_error

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Table - Precision Loss Columns

• loss_general = precision_loss_general_pct
• loss_code = precision_loss_code_pct
• loss_math = precision_loss_math_pct