🤖 QwenPaw-Flash-9B-heretic — Importance Matrix GGUF

This repository hosts importance-matrix (imatrix) optimized GGUF weights, available in multiple quantization formats, and the associated vision projection matrix for QwenPaw-Flash-9B-heretic, quantized from the source floating-point tensors provided by coder3101/QwenPaw-Flash-9B-heretic.

🔄 Sister Repository: Check out the Standard GGUF Sister Repository for uncalibrated and full 8-bit precision options.

🎯 Matrix-Weighted Calibration (Imatrix)

An Importance Matrix (imatrix) calculation tracks activations across network layers using a calibration sequence, then weights the quantization process to preserve the parameters that matter most for output quality — improving fidelity at low bit depths.

➡️ Calibration dataset: Bartowski's calibration_datav5.txt.

IQ4_NL is included because the matrix enables a non-linear 4-bit format that outperforms standard linear 4-bit quantization.

Q8_0 is absent because 8-bit quantization already introduces near-zero degradation, making calibration unnecessary — see the standard sister repository for that variant.

ℹ️ Model Profile & Core Features

QwenPaw-Flash-9B is a model by agentscope-ai, fine-tuned from Qwen3.5-9B and deeply optimised for the QwenPaw autonomous agent scenario. Since its training phase, the model has been specifically refined for QwenPaw tasks, delivering enhanced agentic performance in tool invocation, command execution, memory management, and multi-step planning. It inherits the base model's hybrid Gated Delta Network + sparse MoE architecture, along with vision capability carried over from the Qwen3.5-9B base.

The heretic suffix denotes post-processing via the Heretic v1.2.0 Arbitrary-Rank Ablation (ARA) method with row-norm preservation performed by coder3101, ensuring refusal responses never interrupt agent workflows or autonomous task loops.

📋 Technical Specifications

Property	Value
Base Architecture	Qwen3.5 hybrid (Gated Delta Networks + sparse MoE)
Developed by	agentscope-ai
Fine-tuned from	Qwen/Qwen3.5-9B
Primary Use	Autonomous agents, tool calling, memory management, multi-step planning
Context Window	262,144 tokens
Vision Capability	Inherited from Qwen3.5-9B (early-fusion)
Abliteration Tool	Heretic v1.2.0
Abliteration Method	Arbitrary-Rank Ablation (ARA) with row-norm preservation
Prompt Format	ChatML

🛠️ Heretic Overrides (ARA)

Property	Value
start_layer_index	13
end_layer_index	16
preserve_good_behavior_weight	0.9042
steer_bad_behavior_weight	0.0003
overcorrect_relative_weight	1.0109
neighbor_count	13

📊 Refusal Bypass Metrics

The metrics below are self-reported by the original model author (coder3101) and have not been independently reproduced.

Metric	This model	Original (agentscope-ai/QwenPaw-Flash-9B)
KL divergence	0.0099	0 (by definition)
Refusals	12/100	96/100

🧮 Numerical & Tensor Formats

Property	Value
Text Tensor Types	IQ4_NL, Q4_K_M, Q5_K_M (all with imatrix calibration)
Importance Matrix	Bartowski's `calibration_datav5.txt`
Vision Tensors	Q8_0, BF16

📦 Available Model Files

Main model weights

Filename	Quantization	llama.cpp Build	Size	Download
`QwenPaw-Flash-9B-heretic-IQ4_NL-imatrix.gguf`	`IQ4_NL`	`b9843`	5.05 GB	📥 Download
`QwenPaw-Flash-9B-heretic-Q4_K_M-imatrix.gguf`	`Q4_K_M`	`b9821`	5.24 GB	📥 Download
`QwenPaw-Flash-9B-heretic-Q5_K_M-imatrix.gguf`	`Q5_K_M`	`b9860`	6.02 GB	📥 Download

mmproj — vision projector files

Filename	Quantization	Size	Download
`mmproj-QwenPaw-Flash-9B-heretic-Q8_0.gguf`	`Q8_0`	595 MB	📥 Download
`mmproj-QwenPaw-Flash-9B-heretic-BF16.gguf`	`BF16`	879 MB	📥 Download

mtp — speculative decoding draft files

Filename	Quantization	Size	Download
`mtp-QwenPaw-Flash-9B-heretic-Q8_0.gguf`	`Q8_0`	2.02 GB	📥 Download
`mtp-QwenPaw-Flash-9B-heretic-BF16.gguf`	`BF16`	3.80 GB	📥 Download

🎛️ Component Pairing Guide

Download exactly one main weights file:

IQ4_NL: Non-linear 4-bit format, best choice for constrained memory when imatrix calibration is present.
Q4_K_M: Balanced 4-bit format suitable for most everyday use.
Q5_K_M: Higher-fidelity mid-range format recommended as a general default.

mmproj files (optional): multimodal vision projectors. Pass one via the --mmproj flag in llama.cpp to enable image input.

BF16 (Recommended): Highest possible image processing accuracy. While older projectors were small, modern vision towers can hover around 1GB. If you are tight on VRAM, it is completely viable to run this on system RAM (CPU) with a minimal performance penalty, saving your precious GPU space for the main model layers.
Q8_0: Cuts the projector file size and memory footprint in half (~500MB for larger 1GB files). Use this if you prefer to keep the vision tower hosted entirely on your GPU but need to claw back some VRAM to avoid Out-Of-Memory (OOM) crashes.

mtp files (optional): multi-token-prediction draft models for speculative decoding. Pass one via the --draft flag in llama.cpp to speed up generation.

BF16 (Best Speed): Maximizes draft accuracy. The more accurate the draft model's predictions are, the higher your token acceptance rate, which translates directly into faster text generation. Use this if you have the VRAM headroom.
Q8_0 (Best VRAM Efficiency): Cuts the draft model size in half. Choose this if loading a heavy BF16 draft model would force you to drop layers of your main model to system RAM, which would heavily tank your total performance.

⚡ Deployment & Execution Commands

The vision projector (--mmproj) must be supplied at runtime whenever image inputs are used. Omitting it disables multimodal capability entirely.

agentscope-ai recommends the following sampling configuration for best results: temperature=1.0, top_p=0.95, presence_penalty=1.5, top_k=20.

Swap the -m filename below for either quantized file depending on your size/quality trade-off preference.

llama.cpp CLI (with image)

./llama-cli \
  -m QwenPaw-Flash-9B-heretic-IQ4_NL-imatrix.gguf \
  --mmproj mmproj-QwenPaw-Flash-9B-heretic-Q8_0.gguf \
  -c 8192 \
  -ngl 99 \
  --image "path/to/image.jpg" \
  -p "<|im_start|>user\nAnalyze the attached image and plan the next agent action.<|im_end|>\n<|im_start|>assistant\n"

Speculative Decoding (MTP Acceleration)

./llama-cli \
  -m QwenPaw-Flash-9B-heretic-IQ4_NL-imatrix.gguf \
  --draft mtp-QwenPaw-Flash-9B-heretic-Q8_0.gguf \
  -c 8192 \
  -ngl 99 \
  -p "<|im_start|>user\nAnalyze the attached image and plan the next agent action.<|im_end|>\n<|im_start|>assistant\n"

OpenAI-Compatible API Server

./llama-server \
  --host 0.0.0.0 \
  --port 8080 \
  -m QwenPaw-Flash-9B-heretic-IQ4_NL-imatrix.gguf \
  --mmproj mmproj-QwenPaw-Flash-9B-heretic-Q8_0.gguf \
  -c 16384 \
  -ngl 99 \
  --flash-attn

💬 Chat Templates & Prompt Design (ChatML)

Supply tool definitions and memory context in the system block for agentic use.

<|im_start|>system
You are QwenPaw, an autonomous agent with access to tools, memory, and planning capabilities.<|im_end|>
<|im_start|>user
Your task or image payload here.<|im_end|>
<|im_start|>assistant

⚠️ Safety & Operational Notes

This model is abliterated and will generate content that standard aligned models refuse. Use responsibly and in compliance with applicable laws.
Primarily designed for the QwenPaw agent stack; strengths are most evident in agentic, tool-calling contexts.
Vision tensors are kept at BF16 or Q8_0 depending on the mmproj variant chosen, to preserve spatial feature quality.
Speculative decoding via the accompanying mtp draft model can meaningfully increase throughput on supported llama.cpp builds.
Imatrix calibration improves perplexity recovery compared to non-imatrix quantization, particularly on low-frequency tokens.
IQ4_NL produces a smaller file than Q4_K_M and tends to run faster on CPU and ARM devices; imatrix calibration narrows the quality gap between the two formats considerably.