llmfan46/Omega-Evolution-27B-v2.0-uncensored-heretic-GGUF

<div style="background-color: #ff4444; color: white; padding: 20px; border-radius: 10px; text-align: center; margin: 20px 0;"> <h2 style="color: white; margin: 0 0 10px 0;">🚨⚠️ I HAVE REACHED HUGGING FACE'S FREE STORAGE LIMIT ⚠️🚨</h2> <p style="font-size: 18px; margin: 0 0 15px 0;">I can no longer upload new models unless I can cover the cost of additional storage.<br>I host <b>70+ free models</b> as an independent contributor and this work is unpaid.<br><b>Without your support, no more new models can be uploaded.</b></p> <p style="font-size: 20px; margin: 0;"> <a href="https://patreon.com/LLMfan46" style="color: white; text-decoration: underline;">🎉 Patreon (Monthly)</a> &nbsp;|&nbsp; <a href="https://ko-fi.com/llmfan46" style="color: white; text-decoration: underline;">☕ Ko-fi (One-time)</a> </p> <p style="font-size: 16px; margin: 10px 0 0 0;">Every contribution goes directly toward Hugging Face storage fees to keep models free for everyone.</p> </div>

93% fewer refusals (6/100 Uncensored vs 91/100 Original) while preserving model quality (0.0772 KL divergence).

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GGUF quantizations of llmfan46/Omega-Evolution-27B-v2.0-uncensored-heretic.

This is a decensored version of ReadyArt/Omega-Evolution-27B-v2.0, made using Heretic v1.2.0 with the Arbitrary-Rank Ablation (ARA) method

Abliteration parameters

Targeted components

• —attn.out_proj
• —attn.o_proj

Performance

PIQA test results with batch size 128:

<span style="color:blue">Original:</span>

<span style="color:darkgreen">Heretic:</span>

Lower refusals indicate fewer content restrictions, while lower KL divergence indicates more closeness to the original model's baseline. Higher refusals cause more rejections, objections, pushbacks, lecturing, censorship, softening and deflections. PIQA (Physical Intuition Question Answering) a ~1,800 questions tests common-sense understanding of how the physical world works with benchmark scores to measure physical reasoning ability. The Heretic model's <u>acc</u> and <u>accnorm</u> scores closer to the original model's indicate better capability preservation, a big decrease in <u>acc</u> and <u>accnorm</u> in the <span style="color:darkgreen">Heretic</span> model compared to <span style="color:blue">Original</span> model's results means a big decrease in the Hereticated model capabilities. <u>acc</u> measures raw accuracy (which answer gets higher probability), while <u>accnorm</u> measures length-normalized accuracy (corrects for answer length bias). For this purpose, <u>accnorm</u> matters more because longer answers naturally have lower probabilities (more tokens = more chances to lose probability). Without normalization, models favor shorter answers unfairly. <u>acc_norm</u> divides by answer length to correct this.

MMLU test results with batch size 64:

<span style="color:blue">Original:</span>

<span style="color:darkgreen">Heretic:</span>

MMLU - Massive Multitask Language Understanding, ~14,000 multiple-choice questions across 57 subjects (math, history, law, medicine, etc.).

Quantizations

Vision Projector

A Vision Projector File is Required for vision/multimodal capabilities. Use alongside any quantization above.

Usage

Works with llama.cpp, LM Studio, Ollama, and other GGUF-compatible tools.

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/ ── Code ── / .gs pre { background: #030810; border: 1px solid var(--edge); border-left: 2px solid var(--azure); padding: 16px 18px; overflow-x: auto; font-family: var(--mono); font-size: 0.76rem; line-height: 1.6; color: var(--text); margin: 0 0 22px; } .gs pre:last-child { margin-bottom: 0; } .gs pre code { background: none; color: inherit; padding: 0; } .gs code { font-family: var(--mono); font-size: 0.875em; color: var(--crimson); background: var(--az-glow); padding: 2px 5px; } </style> <html lang="en"> <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>BlueStar</title> <link rel="preconnect" href="https://fonts.googleapis.com"> <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;600;700;900&family=JetBrains+Mono:wght@400;700&display=swap" rel="stylesheet"> </head> <body> <div class="gs">

<div class="gs-hero"> <img src="https://cdn-uploads.huggingface.co/production/uploads/65b19c6c638328850e12d38c/ruIMSzVP1DU1_HxUqBmFx.png" alt="image"> <div class="gs-ident"> <h1 class="gs-name">BlueStar v2</h1> <span class="gs-base">Qwen3.5 27B</span> </div> </div>

<div class="gs-section"> <div class="gs-shead"> <span class="gs-snum">01</span> <span class="gs-stitle">Overview</span> </div> <div class="gs-sbody"> <p>Designed for RP and writing tasks.</p> <p>Feels like a good improvement on v1. This version aims to fix the rep and improve the intelligence while keeping the creativity.</p> <p>Non thinking and thinking are both supported. If you want to use thinking, it is required to prefill the <code>&lt;think&gt;\n</code> as that is how it was trained.</p> </div> </div>

<div class="gs-section"> <div class="gs-shead"> <span class="gs-snum">02</span> <span class="gs-stitle">SillyTavern Settings</span> </div> <div class="gs-sbody"> <div class="gs-stack"> <div class="gs-panel"> <div class="gs-panel-head">Recommended Roleplay Format</div> <div class="gs-row"><span class="gs-key">Actions</span><span class="gs-val">In plaintext</span></div> <div class="gs-row"><span class="gs-key">Dialogue</span><span class="gs-val">"In quotes"</span></div> <div class="gs-row"><span class="gs-key">Thoughts</span><span class="gs-val">In asterisks</span></div> </div> <div class="gs-panel"> <div class="gs-panel-head">Recommended Samplers</div> <div class="gs-row"><span class="gs-key">Temp</span><span class="gs-val">0.8 - 1.0</span></div> <div class="gs-row"><span class="gs-key">MinP</span><span class="gs-val">0.05 - 0.075</span></div> </div> <div class="gs-panel"> <div class="gs-panel-head">Instruct</div> <div class="gs-row"><span class="gs-val"><a href="https://huggingface.co/zerofata/Q3.5-BlueStar-v2-27B/raw/main/ChatML-Q3.5-Think.json">ChatML - Think</a></span></div> <div class="gs-row"><span class="gs-val"><a href="https://huggingface.co/zerofata/Q3.5-BlueStar-v2-27B/raw/main/ChatML-Q3.5-NoThink.json">ChatML - NoThink</a></span></div> </div> </div> </div> </div>

<div class="gs-section"> <div class="gs-shead"> <span class="gs-snum">03</span> <span class="gs-stitle">Quantizations</span> </div> <div class="gs-sbody"> <div class="gs-qrow"> <div class="gs-qpanel"> <span class="gs-qtype">GGUF</span> <div class="gs-qsep"></div> <a href="https://huggingface.co/zerofata/Q3.5-BlueStar-v2-27B-GGUF">iMatrix</a> </div> </div> </div> </div>

<div class="gs-section"> <div class="gs-shead"> <span class="gs-snum">04</span> <span class="gs-stitle">Creation Process</span> </div> <div class="gs-sbody"> <p>Creation Process: SFT</p> <p>SFT on approx 27 million tokens.</p> <p>I've confirmed the repetition coming from the RP datasets. Despite the extensive filtering, human editing, rewriting and deduping. Compared to other types of data like chat and writing, RP is just somewhat repetitive in nature. One idea to fix this is to just not use the RP datasets, or use less of them. This does seem to sort of work, but the model performs noticably worse at RP as a result. Which makes sense, given that's the entire idea of having RP data to begin with.</p> <p>The current solution I'm testing is using custom loss masking with the RP datasets. Most common phrases of slop are masked out, so the model doesn't get rewarded for learning these patterns. Overused words within a conversation also get masked out in later turns.</p> <p>It... seems to have worked? Repetition from my testing is greatly reduced after a few hours of using the model. It can still latch onto phrases, but I've seen much less verbatim repetition.</p> <p>Trained using Axolotl.</p> <details> <summary>Axolotl Config</summary> <div class="gs-detail-body"> <div class="gs-cfg-title">SFT (4&#215;H200)</div> <pre><code>basemodel: Qwen/Qwen3.5&#45;27B &#32; plugins: &#45; axolotl.integrations.cutcrossentropy.CutCrossEntropyPlugin strict: false &#32; datasets: &#45; path: ./data/bluestarv2sft3allrpattemptmasked20260318075236.jsonl &#32; valsetsize: 0.02 outputdir: ./Qwen3.5&#45;27B&#45;v2&#45;SFT&#45;5 &#32; sequencelen: 10756 samplepacking: true &#32; loadin8bit: true adapter: lora lorar: 128 loraalpha: 128 peftuserslora: true loratargetmodules: &#45; qproj &#45; kproj &#45; vproj &#45; oproj &#45; downproj &#45; upproj &#35; Uncomment below to also target the linear attention projections. &#35; These use separate inprojqkv / inprojz / outproj (Qwen3.5&#45;specific). &#45; linearattn.inprojqkv &#45; linearattn.inprojz &#45; linearattn.outproj &#32; wandbproject: Qwen3.5&#45;27B&#45;SFT wandbname: Qwen3.5&#45;27B&#45;v2&#45;SFT&#45;5 &#32; gradientaccumulationsteps: 4 microbatchsize: 1 numepochs: 2 optimizer: adamwtorch8bit lrscheduler: cosine learningrate: 1.2e&#45;5 weightdecay: 0.01 warmupratio: 0.05 &#32; bf16: auto tf32: true &#32; resumefromcheckpoint: loggingsteps: 1 flashattention: true &#32; evalsperepoch: 4 savesperepoch: 4 specialtokens: &#32; fsdpconfig: fsdpversion: 2 offloadparams: false cpuramefficientloading: false autowrappolicy: TRANSFORMERBASEDWRAP transformerlayerclstowrap: Qwen35DecoderLayer statedicttype: FULLSTATEDICT shardingstrategy: FULLSHARD reshardafterforward: true activationcheckpointing: true</code></pre> </div> </details> </div> </div>

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