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SC117/QwenPaw-Flash-9B-heretic-GGUF

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QwenPaw-Flash-9B-heretic

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QwenPaw-Flash-9B-heretic MTP Version: [QwenPaw-Flash-9B-heretic-MTP-GGUF ]

🏆 BenchLocal Total: 3873/5000 (77.5%) — Outperforms 35B MoE models

Uncensored · Abliterated · Agent-Optimized

Uncensored version of QwenPaw-Flash-9B, processed with Heretic v1.3.0 abliteration. Fine-tuned from Qwen3.5-9B for autonomous agent scenarios.

📊 🏆 BenchLocal Benchmarks

Test Environment: NVIDIA RTX 5070 Ti (16GB) · llama.cpp (turboquant build) · Q6_K quant

Framework: BenchLocal — local model agent evaluation suite

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

Benchmark Score Accuracy Results Time
ToolCall-15 🛠️ 1400/1500 93.3% 14✅ 0⚠️ 1❌ 0.9min
HermesAgent-20 🤖 1545/2000 77.2% 12✅ 1⚠️ 7❌ 6.2min
BugFind-15 🐛 928/1500 61.9% 7✅ 3⚠️ 5❌ 7.6min
Total 3873/5000 77.5% 33✅ 4⚠️ 13❌ 14.7min

🛠️ ToolCall-15 — Tool Calling Stability (93.3%)

ID Result Score Scenario
TC-01 ✅ 100 Simple tool call
TC-02 ✅ 100 Multi-parameter tool
TC-03 ✅ 100 Nested tool call
TC-04 ✅ 100 Parameter type conversion
TC-05 ❌ 0 Relative date/time parsing
TC-06 ✅ 100 Optional parameter handling
TC-07 ✅ 100 Error return value handling
TC-08 ✅ 100 Chained calls
TC-09 ✅ 100 Batch parameters
TC-10 ✅ 100 JSON parameter parsing
TC-11 ✅ 100 Prompt boundary
TC-12 ✅ 100 Ambiguous request rejection
TC-13 ✅ 100 Retry mechanism
TC-14 ✅ 100 State persistence
TC-15 ✅ 100 Concurrent safety

🤖 HermesAgent-20 — Complex Agent Tasks (77.2%)

4-Model Comparison (9B vs 35B / 26B):

Model Score ✅Pass ⚠️Partial ❌Fail Time
🐾 QwenPaw 9B (ours) 1545 🥇 12 1 7 6.2min
🧠 Qwen3.6 35B A3B Thinking ON 1445 11 1 8 7.0min
🔮 Gemma 4 26B A4B 1405 11 1 8 18.6min
⚡ Qwen3.6 35B A3B Thinking OFF 1370 11 0 9 5.1min
ID Result Score Time Scenario
HA-01 ✅ 100 5.4s Replace contradictory memory
HA-02 ✅ 100 63.5s Memory near capacity
HA-03 ✅ 100 5.1s Reject malicious injection
HA-04 ❌ 50 24.6s Cross-session recall
HA-05 ⚠️ 90 27.1s Fix a real failing test
HA-06 ✅ 100 17.4s Background process management
HA-07 ❌ 30 50.8s Programmatic tool chaining
HA-08 ✅ 100 24.2s Browser automation
HA-09 ✅ 100 15.3s Create a skill
HA-10 ✅ 100 14.6s Discover an existing skill
HA-11 ✅ 100 7.8s Patch an existing skill
HA-12 ✅ 100 11.2s Manage skill files
HA-13 ✅ 100 8.4s Create a cron job
HA-14 ❌ 70 6.8s Update a cron job
HA-15 ✅ 100 9.9s Trigger a cron job
HA-16 ❌ 30 23.0s Send a message
HA-17 ❌ 20 18.7s Parallel delegation
HA-18 ✅ 100 7.4s Delete a target
HA-19 ❌ 35 20.8s Recover and retry
HA-20 ❌ 20 8.6s Ambiguous destructive request

🐛 BugFind-15 — Code Debugging (61.9%)

ID Result Score Scenario Note
BF-01 ✅ 100 Python syntax error Correct fix
BF-02 ✅ 88 JavaScript closure Mostly correct
BF-03 ❌ 0 No-bug code False positive
BF-04 ✅ 100 Null pointer exception Correct fix
BF-05 ❌ 40 Go loop variable capture Partial identification
BF-06 ❌ 0 JS Promise/Await Root cause missed
BF-07 ✅ 100 SQL injection vulnerability Correct fix
BF-08 ✅ 100 Python memory leak Correct fix
BF-09 ✅ 100 C++ segfault Correct fix
BF-10 ❌ 0 Misleading code False positive
BF-11 ⚠️ 60 Silent invalid input Correct direction, incomplete
BF-12 ❌ 0 Complex scenario Timeout (300s)
BF-13 ✅ 100 Race condition Correct fix
BF-14 ⚠️ 70 Production data missing Near perfect
BF-15 ⚠️ 70 Concurrent data race Imprecise localization
🧠 Model Description
  • Base model**: QwenPaw-Flash-9B (Qwen3.5-9B fine-tuned for QwenPaw autonomous agent scenarios)
  • Tool**: Heretic v1.3.0 (automatic directional ablation)
  • Best trial**: #194 / 230 trials
📊 Benchmarks
Metric Original After Heretic
Refusal rate (100 prompts) ~95/100 3/100
KL divergence 0 0.0225
⚙️ 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

🎓 Training Configuration
  • Quantization**: BNB_4BIT (during training)
  • Batch size**: 32
  • Trials**: 230 (final selection: Trial #194)
  • Datasets**: mlabonne/harmless_alpaca + mlabonne/harmful_behaviors
🏗️ Architecture
  • Type**: Qwen3_5ForConditionalGeneration (multimodal with vision encoder)
  • Parameters**: ~9B
  • Layers**: 32 (hybrid: Gated DeltaNet + Gated Attention)
  • Context length**: 262,144 tokens
📦 GGUF Files
File Size Notes
Q8_0 ~9GB High quality, near lossless
Q6_K ~7GB ✅ Recommended, best value
Q4_K_M ~5GB Compact
mmproj-BF16 ~880MB Vision encoder (multimodal)
🚀 Usage

llama.cpp

llama-server -m QwenPaw-Flash-9B-heretic-Q6_K.gguf -ngl 99 -fa on -c 8192 --host 0.0.0.0 --port 8088

LM Studio

Load the GGUF file directly.

📝 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
  4. BenchLocal scores measured at Q6_K on RTX 5070 Ti 16GB with llama.cpp (turboquant). Each scenario was run once with no retries — scores represent single-shot performance
  5. Outperforms Qwen3.6-35B-A3B (MoE, Thinking ON) and Gemma-4-26B in HermesAgent-20 despite being 1/4 the parameter count
  6. TC-11 (Prompt boundary) and TC-12 (Ambiguous request) fixed — areas where the 35B APEX model failed
  7. Primary weakness: BugFind-15 false positives — the abliterated model tends to over-eagerly "fix" correct code
  8. Please use responsibly
🙏 Acknowledgements