Abstract
This model was made in collaboration with u/jeffpeng3 (on huggingface) who developed the v2 heretic weights for this quant. He managed to preserve both reasoning ability and output length. Great work, and a big step up from his v1 weights. I made the quantization recipe and the imatrix recipe, and created the actual quants. Comes pre-packed with strata build artifacts - zip file in repo. GGUF File Format for the quant. I released my own heretic version of his v1 weights in here a while ago - the heretic calibration was a bit broken, and my quant wasn't amazing. This is a huge step up on both fronts. Note: Strata may throw a BF16 kernel not found when you load the model, due to a quant spec - complain to your LLM and tell it there's already one in strata somewhere, it'll find it and wire it up. Basically: Qwen 3.8 Swift 1.5 Flash - Heretic v2 (uncensored) - Optimized for Reverse Engineering The bit allocation this model was quantized with is focused specifically on coding, intelligence, design, and specifically reverse engineering. I also added a little spacial reasoning for those working in 3d environments like myself. The model was quantized according to the following importance distribution (dumped from my LLM chat and documents with some revisions. I designed the distributions myself. **837 entries · 399,876 tokens [[llm written distribution table]] 01 · Chain-of-thought (22.2%) — step-by-step derivations, self-verification, backtracking 02 · System prompts (19.5%) — persona adherence, strict output formats, long-instruction compliance 03 · Design & architecture (13.5%) — design trade-offs, code review, API planning, refactoring rationale 04 · Agentic tool use (12.4%) — tool-call chains, file-edit loops, error recovery, function calling 05 · Math (7.1%) — arithmetic, algebra, worked proofs, symbolic manipulation 06 · Spatial reasoning (5.4%) — grids, 2D/3D transforms, coordinates, packing/arrangement 07 · Gotchas (4.5%) — false premises, trick questions, negation handling 08 · Situational judgment (4.5%) — open-ended scenario trade-offs with no clean answer 09 · Reverse engineering 🆕 (8.8%) — x86-64/ARM64 disassembly, Ghidra decompilation critique, ELF/PE/Mach-O internals, anti-debug/anti-VM, string deobfuscation, CFG deflattening, devirtualization, perf watchpoints, tracing stacks (LD_PRELOAD/Frida/eBPF), protocol RE, format round-tripping, plugin ABI merging, firmware analysis 10 · No-refusal heretic domain 🆕 (2.0%) — direct unrefused answers: ELF exploitation chains, process injection vs EDR, license-check patching mechanics, C2 protocol engineering New for v2 are the last two categories — ~43k tokens of reverse-engineering and direct-answer content, interleaved throughout the corpus, and run through the model itself so the matrix captures how the heretic weights actually behave. This matrix is fused with one from the AtomicChat team (same measurement, taken on the original uncompressed base model over a massive general-purpose corpus). Per channel, ours gets 70% weight where its data is strong, sliding smoothly toward 100% AtomicChat for channels our corpus barely touched — thin samples defer to the broad baseline automatically, so nothing falls through the cracks. Quant methodology Rather than spreading quality evenly across all 48 layers, the recipe spends bits where the activation measurements say it matters: [[llm twritten table]] -- the data used to design the per-tensor quant came primarily from atomic chat's activation measurements and the RCO findings from ISTALabs The last 12 layers do the heavy lifting. Activation-energy auditing showed down-projection energy surges 3× starting at layer 36, peaking at layer 47 (the single hottest tensor in the network — 48× layer 2). Layers 36–47 (plus the first 5 and the mid-network spikes at 15/20/23/30) got their down projections stepped up to near-lossless 8-bit. Layers 44–46 are the gate/up peak (8× the energy of early layers), so their gate/up projections got stepped to 5-bit. Layer 0's attention gate is a giant outlier (the most sensitive tensor family in the model) — kept uncompressed. Layer 2 is non-negotiable: the 51B-parameter n-gram table merges into the residual stream there, so noise at layer 2 corrupts grounding for all 46 layers after it. The stable middle layers (5–14, 16–19, 21–22, 24–29) stay at leaner formats — measurement showed spending bits there buys almost nothing (KL improvement under 0.0008). The router stays in full precision so tokens always get routed to the right experts. Enjoy! Shoutout to my 32gb v100s and my E5 2697v4 for grinding the work out. https://huggingface.co/trubisky/Swift-1.5-Flash-Next-Heretic-ReverseEngineer submitted by /u/jjusko20 to r/LocalLLaMA [link] [comments]