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Cataloged from SC117/QwenPaw-Flash-9B-heretic-MTP-GGUF
All models by SC117 →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.
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
MTP speculative decoding eliminated the single failure (TC-05: Relative date/time parsing, which previously scored 0). All 15 scenarios now pass perfectly.
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.
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.
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.
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)
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.
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).
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
--spec-type draft-mtp with --spec-draft-n-max 2--spec-type draft-mtp
--spec-draft-n-max 2
# 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
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..."
# 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
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.