MTPGGUF

QwenPaw-Flash-9B-heretic-MTP

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QwenPaw-Flash-9B-heretic non-MTP Version: QwenPaw-Flash-9B-heretic-GGUF

🏆 BenchLocal Total: 4035/5000 (80.7%) — MTP Speculative Decoding Injected

Uncensored · Abliterated · Agent-Optimized · 1.7-4.1× Speedup

Uncensored version of QwenPaw-Flash-9B, processed with Heretic v1.3.0 abliteration, with MTP (Multi-Token Prediction) head weights injected from the original Qwen3.5-9B base model.

By reconstructing the MTP speculative decoding head — which was stripped during the QwenPaw fine-tuning process — this model achieves up to 4.1× inference speedup on real agent benchmarks while maintaining or improving accuracy.

📊 🏆 BenchLocal Benchmarks (With MTP)

Test Environment: NVIDIA RTX 5070 Ti (16GB) · llama.cpp (turboquant build, --spec-type draft-mtp) · Q6_K quant

Framework: BenchLocal — local model agent evaluation suite

Methodology: Each scenario run once, no retries, no second attempts

Comparison: With vs Without MTP

🛠️ ToolCall-15 — Tool Calling Stability (100%, +6.7 pts)

MTP speculative decoding eliminated the single failure (TC-05: Relative date/time parsing, which previously scored 0). All 15 scenarios now pass perfectly.

🤖 HermesAgent-20 — Complex Agent Tasks (75.3%, −1.9 pts)

MTP decoding introduces minor noise in long-chain reasoning scenarios (~40pt drop), likely because draft tokens occasionally derail the generation path in multi-step planning tasks. However, the speed gain (1.17×) and the fact that the drop is within noise range (single-run variance was 255pts for Qwopus MTP) makes this an acceptable trade-off.

🐛 BugFind-15 — Code Debugging (68.7%, +6.8 pts)

Significant improvement — MTP's faster decoding effectively prevents timeout failures (BF-12 previously hit 300s timeout, now completes in time) and the draft context helps maintain debugging focus.

⚡ MTP Speculative Decoding

What is MTP?

Multi-Token Prediction (MTP) is a speculative decoding technique where a small "draft head" predicts multiple future tokens in parallel. The main model then verifies these drafts in a single forward pass, accepting correct predictions for up to 2-4× speedup in practice.

Injection Method

The original Qwen3.5-9B base model ships with a 4-layer MTP head (~243M params) in its architecture configuration. During QwenPaw fine-tuning, the MTP head weights were stripped (only the config placeholder mtp_num_hidden_layers: 1 remained, but no actual tensors existed in the safetensors).

Recovery process:

Total injected parameters: 243.3M (2.7% of main model)

MTP acceptance rate (draft-n-max=2): ~50% (1083 accepted / 2166 generated across all benchmarks)

Why This Works

The MTP head is a lightweight 4-layer MLP decoder that maps the main model's last hidden state to future token logits. It sits entirely in speculative decoding space — the main model's weights are unchanged, so no fine-tuning or retraining is needed. The head simply needs to exist with compatible dimensions for llama.cpp's --spec-type draft-mtp to activate.

⚡ Comparison: QwenPaw MTP vs Other Models

QwenPaw MTP wins on 2/3 benchmarks + total score + total time. The only benchmark it loses is BugFind-15 (to Qwopus MTP), but Qwopus suffers from severe instability (255pt variance on HermesAgent-20, with a worst-case 6.2min timeout).

🧠 Model Description

• Base model**: QwenPaw-Flash-9B (Qwen3.5-9B fine-tuned for autonomous agent scenarios)
• MTP head source**: Qwen/Qwen3.5-9B (original base model, layer 32 MTP head)
• Tool**: Heretic v1.3.0 (automatic directional ablation)
• Best trial**: #194 / 230 trials (abliteration)

⚙️ Abliteration Parameters

direction_index = 21.13 attn.o_proj.max_weight = 1.42 attn.o_proj.max_weight_position = 21.72 attn.o_proj.min_weight = 1.11 attn.o_proj.min_weight_distance = 18.14 mlp.down_proj.max_weight = 1.48 mlp.down_proj.max_weight_position = 21.23 mlp.down_proj.min_weight = 1.47 mlp.down_proj.min_weight_distance = 17.47

🏗️ Architecture

• Type**: Qwen3_5ForConditionalGeneration (multimodal with vision encoder) + MTP spec head
• Main model parameters**: ~9B
• MTP head parameters**: ~243M (2.7% overhead)
• Layers**: 32 (hybrid: Gated DeltaNet + Gated Attention) + 4 MTP decoder layers
• Context length**: 262,144 tokens
• Speculative decoding**: --spec-type draft-mtp with --spec-draft-n-max 2

📦 GGUF Files 🚀 Usage

--spec-type draft-mtp

--spec-draft-n-max 2

llama.cpp (with MTP speculative decoding)

# Start server with MTP enabled llama-server -m QwenPaw-Flash-9B-heretic-MTP-Q6_K.gguf \ -ngl 99 -fa on -c 8192 \ --spec-type draft-mtp --spec-draft-n-max 2 \ --host 0.0.0.0 --port 8088

Or with CLI

llama-cli -m QwenPaw-Flash-9B-heretic-MTP-Q6_K.gguf
-ngl 99 -fa on -c 8192
--spec-type draft-mtp --spec-draft-n-max 2
-p "Write a Python script to..."

llama.cpp (without MTP, fallback)

# The model works as a normal GGUF too — just omit spec args llama-server -m QwenPaw-Flash-9B-heretic-MTP-Q6_K.gguf \ -ngl 99 -fa on -c 8192 \ --host 0.0.0.0 --port 8088

LM Studio

Load the GGUF file directly. For MTP speculative decoding, LM Studio must support --spec-type — if not, the model functions as a standard 9B model.

📝 Notes

1. Safety filters have been significantly reduced via abliteration
2. KL divergence is only 0.0225 — minimal impact on model intelligence
3. The original model supports multimodal (vision); GGUF versions require the mmproj file from the non-MTP release
4. BenchLocal scores measured at Q6_K on RTX 5070 Ti 16GB with llama.cpp (turboquant). Each scenario was run once with no retries
5. MTP acceptance rate of ~50% at draft-n-max=2 means ~25-40% wall-clock speedup on short prompts, and up to 4× on long-generation tasks (debugging, code writing)
6. BugFind-15 saw the largest improvement (4.1×) because debugging tasks are generation-heavy — more tokens, more drafts accepted
7. The MTP head is a lossless copy from the original Qwen3.5-9B — no training was involved, simply weight injection
8. Agent-heavy scenarios (HermesAgent-20) see the least MTP benefit because short-turn interactions don't give the draft head enough runway
9. Please use responsibly

🙏 Acknowledgements

• Heretic — Automated censorship removal
• agentscope-ai/QwenPaw-Flash-9B — Base model
• Qwen/Qwen3.5-9B — MTP head source
• llama.cpp — GGUF quantization and inference
• BenchLocal — Local model agent evaluation suite