Instructions to use jackjusko/Swift-1.5-Flash-Next-Heretic-ReverseEngineer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use jackjusko/Swift-1.5-Flash-Next-Heretic-ReverseEngineer with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf jackjusko/Swift-1.5-Flash-Next-Heretic-ReverseEngineer:IQ3_S
# Run inference directly in the terminal:
llama cli -hf jackjusko/Swift-1.5-Flash-Next-Heretic-ReverseEngineer:IQ3_S

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf jackjusko/Swift-1.5-Flash-Next-Heretic-ReverseEngineer:IQ3_S
# Run inference directly in the terminal:
llama cli -hf jackjusko/Swift-1.5-Flash-Next-Heretic-ReverseEngineer:IQ3_S

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf jackjusko/Swift-1.5-Flash-Next-Heretic-ReverseEngineer:IQ3_S
# Run inference directly in the terminal:
./build/bin/llama cli -hf jackjusko/Swift-1.5-Flash-Next-Heretic-ReverseEngineer:IQ3_S

Use Docker

docker model run hf.co/jackjusko/Swift-1.5-Flash-Next-Heretic-ReverseEngineer:IQ3_S

Llama new
LM Studio
Jan

vLLM

How to use jackjusko/Swift-1.5-Flash-Next-Heretic-ReverseEngineer with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jackjusko/Swift-1.5-Flash-Next-Heretic-ReverseEngineer"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jackjusko/Swift-1.5-Flash-Next-Heretic-ReverseEngineer",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/jackjusko/Swift-1.5-Flash-Next-Heretic-ReverseEngineer:IQ3_S

Ollama
How to use jackjusko/Swift-1.5-Flash-Next-Heretic-ReverseEngineer with Ollama:
```
ollama run hf.co/jackjusko/Swift-1.5-Flash-Next-Heretic-ReverseEngineer:IQ3_S
```
Unsloth Desktop

How to use jackjusko/Swift-1.5-Flash-Next-Heretic-ReverseEngineer with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jackjusko/Swift-1.5-Flash-Next-Heretic-ReverseEngineer:IQ3_S

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "jackjusko/Swift-1.5-Flash-Next-Heretic-ReverseEngineer:IQ3_S"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use jackjusko/Swift-1.5-Flash-Next-Heretic-ReverseEngineer with Docker Model Runner:
```
docker model run hf.co/jackjusko/Swift-1.5-Flash-Next-Heretic-ReverseEngineer:IQ3_S
```

Lemonade

How to use jackjusko/Swift-1.5-Flash-Next-Heretic-ReverseEngineer with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jackjusko/Swift-1.5-Flash-Next-Heretic-ReverseEngineer:IQ3_S

Run and chat with the model

lemonade run user.Swift-1.5-Flash-Next-Heretic-ReverseEngineer-IQ3_S

List all available models

lemonade list

Hermes Agent

How to use jackjusko/Swift-1.5-Flash-Next-Heretic-ReverseEngineer with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jackjusko/Swift-1.5-Flash-Next-Heretic-ReverseEngineer:IQ3_S

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default jackjusko/Swift-1.5-Flash-Next-Heretic-ReverseEngineer:IQ3_S

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use jackjusko/Swift-1.5-Flash-Next-Heretic-ReverseEngineer with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jackjusko/Swift-1.5-Flash-Next-Heretic-ReverseEngineer:IQ3_S

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "jackjusko/Swift-1.5-Flash-Next-Heretic-ReverseEngineer:IQ3_S" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Swift-1.5-Flash-Next-Heretic-ReverseEngineer / README.md

jackjusko

Update README.md

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---
license: other
license_name: swift-open-license-1.0
license_link: https://huggingface.co/ukisai/Swift1.5-Qwen3.8-Flash-Next/blob/main/LICENSE
base_model: jeffpeng3/Swift-1.5-Qwen3.8-Flash-Next-heretic
base_model_relation: quantized
quantized_by: trubisky
gguf: Swift-1.5-Qwen3.8-Flash-Next-Heretic-V2-IQ4_NL
gauge:
  Swift15-Qwen38-Flash-Next-Heretic-V2-IQ4_NL: IQ4_NL
library_name: gguf
pipeline_tag: image-text-to-text
language:
- en
- zh
tags:
- qwen3_8
- moe
- heretic
- abliterated
- uncensored
- reasoning-efficient
- imatrix
- quantized
- gguf
- strata
- v100
- reverse-engineering
---
# Qwen 3.8 Swift 1.5 Flash — Heretic (Reverse Engineering Edition, GGUF)

![banner](banner.png)

> [!WARNING]
> **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.
> 
Community made variations:

Halogen v2 weights: https://huggingface.co/johnlockejrr/Swift-1.5-Flash-Next-Heretic-ReverseEngineer-HGN

Apple MLX weights: https://huggingface.co/skudzinau/Swift-1.5-Flash-Next-Heretic-V2-MLX-Serve-iQ-MLX-4.7bpw

This model was made in collaboration with [u/jeffpeng3](https://huggingface.co/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) |
| `IQ3_S-85G/Swift-1.5-Qwen3.8-Flash-Next-Heretic-V2-IQ3_S-85G-000XX-of-00008.gguf` | Custom **IQ3_S-class** mixed recipe (~84.6 GB across 8 shards, 3.36 bpw expert mass, 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. (The IQ3_S-85G build below uses a re-fused matrix at 80% my corpus / 20% AtomicChat — see its section.)

## 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.
## IQ3_S-85G variant

The `IQ3_S-85G/` folder is the leanest heretic v2 build yet — an **IQ3_S-class** recipe: ~3.36 bits-per-weight across the routed expert matrices (nominal IQ3_S is 3.44), 84.6 GB on disk with the 51B-parameter n-gram table included. Same heretic v2 F16 weights, same fused-matrix pipeline, but this one is aggressively specialized: the fused imatrix was rebuilt with my calibration corpus getting **80% weight** on every channel it covered well (up from 70% in the other two builds), so the code/RE/agentic priorities steer the quantizer even harder, with the same smooth fallback to the 5M-token AtomicChat baseline on cold channels.

The layer map is not a hand-tuned recipe this time — it's solved. Per-layer activation energy measured by the 80/20 fused matrix feeds an exact bit-allocation solver (a Lagrangian sweep over each tensor family's format ladder) that spends a fixed byte budget where measured sensitivity says it matters most:

- **The hot tail gets 5-bit everywhere.** Layers 36–47 (the down-projection energy surge that starts at L36 and peaks at L47, the hottest tensor in the network) run gate/up at `Q5_K` and down at `Q5_1`, with layer 47's down at near-lossless `Q8_0`.
- **The cold body drops to 2-bit `IQ2_XXS`.** 28 of the stable middle layers measured so flat that spending bits there buys almost nothing — they carry `IQ2_XXS` gate/up and `Q2_0` down projections. This is where the size is won: dropping those layers below 3-bit funds the 5-bit tail.
- **The mid-network spikes stay protected.** Layers 15, 33 and 43 show localized energy surges and get individual treatment (`IQ3_XXS`, `IQ4_NL`).
- **Everything structural is untouched:** the router stays `BF16`, layer 0's attention-gate outlier stays uncompressed, `token_embd` and `output` stay `Q8_0`, and the n-gram table keeps its `IQ4_NL` floor.
Net effect vs the IQ4_XS-plus build: ~10 GB smaller, with the error concentrated in the layers the measurements say are most forgiving. Every type used is a compiled kernel format on Volta, so it runs natively on the same 3×V100 rig with zero CPU fallback.
## Credits & license
- Base architecture: [Qwen/Qwen3.8-Flash-Next](https://huggingface.co/Qwen/Qwen3.8-Flash-Next)
- Post-train: [ukisai/Swift1.5-Qwen3.8-Flash-Next](https://huggingface.co/ukisai/Swift1.5-Qwen3.8-Flash-Next)
- Abliterated weights: [jeffpeng3/Swift-1.5-Qwen3.8-Flash-Next-heretic](https://huggingface.co/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**.

	---
	license: other
	license_name: swift-open-license-1.0
	license_link: https://huggingface.co/ukisai/Swift1.5-Qwen3.8-Flash-Next/blob/main/LICENSE
	base_model: jeffpeng3/Swift-1.5-Qwen3.8-Flash-Next-heretic
	base_model_relation: quantized
	quantized_by: trubisky
	gguf: Swift-1.5-Qwen3.8-Flash-Next-Heretic-V2-IQ4_NL
	gauge:
	Swift15-Qwen38-Flash-Next-Heretic-V2-IQ4_NL: IQ4_NL
	library_name: gguf
	pipeline_tag: image-text-to-text
	language:
	- en
	- zh
	tags:
	- qwen3_8
	- moe
	- heretic
	- abliterated
	- uncensored
	- reasoning-efficient
	- imatrix
	- quantized
	- gguf
	- strata
	- v100
	- reverse-engineering
	---

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

	![banner](banner.png)

	> [!WARNING]
	> 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.
	>
	Community made variations:

	Halogen v2 weights: https://huggingface.co/johnlockejrr/Swift-1.5-Flash-Next-Heretic-ReverseEngineer-HGN

	Apple MLX weights: https://huggingface.co/skudzinau/Swift-1.5-Flash-Next-Heretic-V2-MLX-Serve-iQ-MLX-4.7bpw

	This model was made in collaboration with [u/jeffpeng3](https://huggingface.co/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) \|
	\| `IQ3_S-85G/Swift-1.5-Qwen3.8-Flash-Next-Heretic-V2-IQ3_S-85G-000XX-of-00008.gguf` \| Custom IQ3_S-class mixed recipe (~84.6 GB across 8 shards, 3.36 bpw expert mass, 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. (The IQ3_S-85G build below uses a re-fused matrix at 80% my corpus / 20% AtomicChat — see its section.)

	## 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.

	## IQ3_S-85G variant

	The `IQ3_S-85G/` folder is the leanest heretic v2 build yet — an IQ3_S-class recipe: ~3.36 bits-per-weight across the routed expert matrices (nominal IQ3_S is 3.44), 84.6 GB on disk with the 51B-parameter n-gram table included. Same heretic v2 F16 weights, same fused-matrix pipeline, but this one is aggressively specialized: the fused imatrix was rebuilt with my calibration corpus getting 80% weight on every channel it covered well (up from 70% in the other two builds), so the code/RE/agentic priorities steer the quantizer even harder, with the same smooth fallback to the 5M-token AtomicChat baseline on cold channels.

	The layer map is not a hand-tuned recipe this time — it's solved. Per-layer activation energy measured by the 80/20 fused matrix feeds an exact bit-allocation solver (a Lagrangian sweep over each tensor family's format ladder) that spends a fixed byte budget where measured sensitivity says it matters most:

	- The hot tail gets 5-bit everywhere. Layers 36–47 (the down-projection energy surge that starts at L36 and peaks at L47, the hottest tensor in the network) run gate/up at `Q5_K` and down at `Q5_1`, with layer 47's down at near-lossless `Q8_0`.
	- The cold body drops to 2-bit `IQ2_XXS`. 28 of the stable middle layers measured so flat that spending bits there buys almost nothing — they carry `IQ2_XXS` gate/up and `Q2_0` down projections. This is where the size is won: dropping those layers below 3-bit funds the 5-bit tail.
	- The mid-network spikes stay protected. Layers 15, 33 and 43 show localized energy surges and get individual treatment (`IQ3_XXS`, `IQ4_NL`).
	- Everything structural is untouched: the router stays `BF16`, layer 0's attention-gate outlier stays uncompressed, `token_embd` and `output` stay `Q8_0`, and the n-gram table keeps its `IQ4_NL` floor.

	Net effect vs the IQ4_XS-plus build: ~10 GB smaller, with the error concentrated in the layers the measurements say are most forgiving. Every type used is a compiled kernel format on Volta, so it runs natively on the same 3×V100 rig with zero CPU fallback.

	## Credits & license

	- Base architecture: [Qwen/Qwen3.8-Flash-Next](https://huggingface.co/Qwen/Qwen3.8-Flash-Next)
	- Post-train: [ukisai/Swift1.5-Qwen3.8-Flash-Next](https://huggingface.co/ukisai/Swift1.5-Qwen3.8-Flash-Next)
	- Abliterated weights: [jeffpeng3/Swift-1.5-Qwen3.8-Flash-Next-heretic](https://huggingface.co/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.