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magiccodingman/Qwen3.6-35B-A3B-Uncensored-MagicQuant-GGUF

magiccodingman Qwen 35B GGUF MoE second-order 262K ctx
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Response includes
  • classification m-uncensored
  • files 23
  • benchmarks 11 entries
  • hub_downloads_all_time 28,381
  • author_summary 2 models
  • readme_text full
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Abliteration classifier · v1.0.0
M-U
Primary method

Uncensored (method unknown)

No other method signals detected in this model.
Confidence
LOW
Why this label 3 signals
Weak or ambiguous signals. Best guess based on catalog patterns; treat as tentative and check the evidence below.
  • 'uncensored' in name/tags but no 'abliterated' marker
  • method not identifiable from author declaration alone
  • may be DPO fine-tune, prompt engineering, or unknown technique
Refusal direction extraction

No specific extraction method could be identified for this model. The producer either did not document it or used a proprietary pipeline.

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Downloads · lifetime
28K
730 last 30d - cooling
Likes
5
Model age
5mo ago
created 2026-05-01
Downloads over time
Now28.6K→from1K↑2,661%
010.5K20.9K31.4K1K on Apr 2928.6K on Oct 11AprMayJunJulAugSepOct
Apr 29 → Oct 11 · 65 snapshots · spans 165 days

Benchmarks

Benchmark Score Source
Entertainment 1.9 UGI
Hazardous 2.4 UGI
Natural Intelligence 28.42 UGI
Political lean -12.5% UGI
Sensitive-Info 18.88 UGI
SocPol 1.5 UGI
UGI 44.25 UGI
Willingness (10) 9.5 UGI
W10-Adherence 9 UGI
W10-Direct 10 UGI
Writing 37.6 UGI

Genealogy 0 direct forks

Full fork graph →

This model's place in the market. Above: what it was derived from. Below: the tree of everything derived from it.

Metadata

License
apache-2.0
Quantizations
IQ2 IQ3 IQ4 Q4_K Q5_K Q6_K Q8_0
Tags
safetensors gguf qwen3_5_moe text-generation magicquant conversational base_model:llmfan46/Qwen3.6-35B-A3B-uncensored-heretic base_model:quantized:llmfan46/Qwen3.6-35B-A3B-uncensored-heretic license:apache-2.0 endpoints_compatible region:us imatrix

Related

Total size
217 GB
Files
23
Quantizations
9
Registered
2026-08-22 13:56
Last updated on HF
2026-05-26 14:51

Files by quantization

Q8_0 1 file 34.4 GB
Qwen3.6-35B-A3B-LM-Q8_0.gguf 34.4 GB 33d75b48 download
Q6_K 1 file 29.4 GB
Qwen3.6-35B-A3B-MQ-Q6_K_1.gguf 29.4 GB b0823232 download
Q5_K 2 files 51.7 GB
Qwen3.6-35B-A3B-MQ-Q5_K_1.gguf 27.2 GB 9d210d5a download
Qwen3.6-35B-A3B-MQ-Q5_K_S_1.gguf 24.5 GB 8d76a6d3 download
Q4_K 2 files 43.9 GB
Qwen3.6-35B-A3B-MQ-Q4_K_M_1.gguf 23.1 GB 8812699b download
Qwen3.6-35B-A3B-MQ-Q4_K_M_2.gguf 20.8 GB 9887232d download
IQ4 1 file 19.5 GB
Qwen3.6-35B-A3B-MQ-IQ4_NL_1.gguf 19.5 GB 2fc6f7a0 download
IQ3 2 files 29.1 GB
Qwen3.6-35B-A3B-MQ-IQ3_M_1.gguf 16.4 GB 83a63eb0 download
Qwen3.6-35B-A3B-UD-IQ3_S.gguf 12.7 GB ff1b40be download
IQ2 1 file 8.93 GB
Qwen3.6-35B-A3B-MQ-IQ2_XXS_1.gguf 8.93 GB 4f66a74f download
BF16 1 file 1.72 KB
mmproj-BF16.gguf 1.72 KB 735b6b0d download
Auxiliary files 12 files 205 MB
imatrix.dat 183 MB 3837ca11 download
tokenizer.json 12.2 MB 5f9e4d49 download
vocab.json 6.41 MB 0aa0ce06 download
merges.txt 3.20 MB a494e019 download
model.safetensors.index.json 101 KB 194c98d2 download
tokenizer_config.json 16.3 KB 28d96ff3 download
LICENSE 11.1 KB 1d5180a4 download
README.md 9.46 KB 9fab04ef download
chat_template.jinja 7.58 KB a8755d82 download
config.json 3.60 KB 9c2aacf8 download
.gitattributes 3.53 KB 09718107 download
generation_config.json 202 B 023756cf download

README current version from Hugging Face


license: apache-2.0
tags:

  • gguf
  • text-generation
  • magicquant
  • conversational
    base_model:
  • llmfan46/Qwen3.6-35B-A3B-uncensored-heretic

MagicQuant Hybrids (v2.0) - Qwen3.6-35B-A3B Uncensored (By llmfan46)

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.

MagicQuant Info & Wiki

MagicQuant is a project intended to become open source. Currently the full methodology is documented at the MagicQuant Wiki.

That wiki will eventually have the code uploaded and be renamed to just "MagicQuant" for the repo instead of "MagicQuant-Wiki". The code is still too much in the early prototype stage. It's beginning to mature, but it requires a heavy hand throughout the process.

Once I'm confident in the code, believe the methodology and protocols are mature enough that I won't be changing it weekly and bricking every setup after every update. I'm excited to share not just the entire methodology, but the entire code base to reproduce MagicQuant :)

Support MagicQuant

I’m a solo developer working full time for myself to achieve my dream. I build open source code on the side. If you like any of my work, buying me a coffee is always appreciated. Otherwise, I hope you enjoy, maybe give me a star or something. Or just send me good vibes. Either way, thank you!

Click here to see ways to support - BTC, Paypal, GitHub sponsors.

Clone Notice

This repository did not run through the full MagicQuant evolution/search pipeline. It is a clone of the final survivor tensor configurations from magiccodingman/Qwen3.6-35B-A3B-MagicQuant-GGUF, 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.


Final survivors

Name Provider KLD Size (GB) Download
LM-Q8_0 llama.cpp 0.004771 36.91 Link
MQ-Q6_K_1 MagicQuant 0.005383 31.59 Link
MQ-Q5_K_1 MagicQuant 0.006012 29.19 Link
MQ-Q5_K_S_1 MagicQuant 0.007155 26.33 Link
MQ-Q4_K_M_1 MagicQuant 0.007832 24.82 Link
MQ-Q4_K_M_2 MagicQuant 0.010894 22.32 Link
MQ-IQ4_NL_1 MagicQuant 0.013040 20.89 Link
MQ-IQ3_M_1 MagicQuant 0.026825 17.60 Link
UD-IQ3_S Unsloth 0.068513 13.68 Link
MQ-IQ2_XXS_1 MagicQuant 0.275805 9.59 Link
Provider credits
  • llama.cpp — Baseline quantization formats and llama.cpp tooling.
Warning - Is MagicQuant Better? (hint: how you frame the question matters)

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.

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".

Re-Uploading External Provider Baselines

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).


Release metadata

  • Final survivor metrics — full file names, KLD, PPL, PPL delta %, byte sizes, download targets, and replacement lineage. PPL delta % is measured against the native/reference PPL when available; negative is better and larger positive values are worse.
  • Hybrid tensor map — tensor-group assignments and effective-state details for MagicQuant hybrid GGUFs.
  • Clone tensor configs — exact per-GGUF tensor quantization maps for reproducing this final output list in repository clone mode.
  • Isolation samples — isolated base/group probe samples with KLD, PPL, PPL delta %, and size truth.
  • Bad trade details — structured bad-trade pruning decisions from the isolation optimizer.
  • Clone benchmark summary — fresh benchmark results from this clone run.
  • Replacement details — structured details for baselines or anchors removed from the final download table, including reason codes, KLD deltas, PPL delta %, and size deltas.
Replacement reason codes
  • STRICT_DOMINANCE — the winner was no larger and had lower real KLD than the removed anchor.
  • NEAR_BASELINE_PREMIUM — the winner used only the configured near-baseline size premium and beat the real linear KLD trade line.
  • INTERIOR_DISCOVERY — the winner was selected as a useful interior point inside a size/KLD gap between anchors.
  • SPACING_COLLAPSE — two candidates were too close in practical output space, so the stronger one was kept.
  • FINAL_DOMINANCE — a later validated survivor dominated this artifact in final real benchmark comparison.


Underlined names in the table replaced or ultimately inherited the replacement of another artifact. Hover the name for the short replacement summary, or inspect magicquant-manifest/magicquant.replacements.json for exact KLD/PPL/size deltas.


README history 2 versions

The author's README evolved over time. Click a version to see its content at that point.

  1. 2026-05-26Update README.mdaeead929.5 KB
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  2. 2026-05-26Super-squash branch 'main' using huggingface_hub400cb338.7 KB
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