trubisky/Swift-1.5-Flash-Next-Heretic-ReverseEngineer

Qwen 3.8 Swift 1.5 Flash — Heretic (Reverse Engineering Edition, GGUF)

Uncensored model. The refusal behavior has been deliberately removed. You are solely responsible for how you use it — comply with all applicable laws and the license.

This model was made in collaboration with u/jeffpeng3, who developed the v2 heretic weights this quant is built on — and who reached out to me about making a new quant, since I made the original one. He managed to preserve both reasoning ability and output length while abliterating the model, which is a genuinely hard combination. 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 — and since his v2 weights held up so well under measurement, I took extra care with the bit allocation this time.

Basically: Qwen 3.8 Swift 1.5 Flash — Heretic (uncensored), tuned for reverse engineering.

The bit allocation is focused specifically on coding, intelligence, design, and reverse engineering — plus a little spatial reasoning for those of us working in 3D environments.

What's in this repo

File	Purpose
Swift-1.5-Qwen3.8-Flash-Next-Heretic-V2-IQ4_NL-0000X-of-00008.gguf	Custom IQ4_NL mixed recipe (~108.9 GB across 8 shards), sized for a 3×32 GB V100 rig (96 GB VRAM)
IQ4_XS/Swift-1.5-Qwen3.8-Flash-Next-Heretic-V2-IQ4_XS-plus-000XX-of-00006.gguf	Custom IQ4_XS-plus — unsloth UD-IQ4_XS recipe + KL-guided step-ups (94.7 GB, see below)
strata-pack-heretic-v2-70g-fix.zip	Pre-built runtime pack for the Strata engine (dense.bin, index.txt, native_experts.txt, conversions.json, compat-bf16.json, tokenizer)

The importance matrix

The model was quantized against a purpose-built imatrix (837 entries · 399,876 tokens · Qwen chat-template native <|im_start|> format):

• 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.

The 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, but anything with decent expert volume is 70% my imatrix. Basically a broad-spectrum coverage callback so my precision allocation doesn't wreck general capabilities.

Quant methodology

This is my new dynamic quant recipe — designed from Unsloth's Q4_K_XL information plus public numbers from ISTA-DASLab and AtomicChat on model structure. It's made for a very specific purpose: fit exactly on 96 GB of VRAM under Strata with precisely the right headroom left over for the vision projector (mmproj), 8192-token prefill batching, and KV streaming. It cuts ~6 GB from the Q4_K_XL build while staying substantially larger than the IQ4_XS version.

Rather than spreading quality evenly across all 48 layers, the recipe spends bits where the activation measurements say it matters:

• 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.

A regular Flash Next quant with this same design is coming as well — it was already in progress.

IQ4_XS-plus variant

The IQ4_XS/ folder holds a leaner variant built on the Unsloth UD-IQ4_XS tensor recipe, with one KL-guided change: the fused imatrix shows expert activation energy concentrating hard in the tail, so ffn_gate_exps and ffn_up_exps were stepped from IQ3_S to IQ4_XS on the six hottest layers (blk. 41, 42, 44, 45, 46, 47) — the same neighborhood the IQ4_NL recipe already treats as the hot tail. Estimated ~9.7% reduction in expert quantization error mass for +0.96 GiB over the plain UD-IQ4_XS recipe (94.7 GB total). Quantized from the same heretic v2 F16 weights with the same fused imatrix.

Credits & license

• Base architecture: Qwen/Qwen3.8-Flash-Next
• Post-train: ukisai/Swift1.5-Qwen3.8-Flash-Next
• Abliterated weights: jeffpeng3/Swift-1.5-Qwen3.8-Flash-Next-heretic
• Fusion baseline imatrix: the AtomicChat team
• Quant recipe, fused imatrix, and GGUFs: trubisky (myself and my own creations)

Inherits the Swift Open License v1.0 and the Qwen Community License.