QwenPaw Flash 9B Heretic GGUF

--- language: - en - zh base_model: - Qwen/Qwen3.5-9B - agentscope-ai/QwenPaw-Flash-9B tags: - heretic - abliteration - uncensored - qwen3.5 - gguf - benchlocal - benchmark - agent - tool-call license: apache-2.0 --- GGUF QwenPaw-Flash-9B-heretic English | 📖 中文文档 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 Safety filters have been significantly reduced via abliteration KL divergence is only 0.0225 — minimal impact on model intelligence The original model supports multimodal (vision); GGUF versions require the mmproj file 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 Outperforms Qwen3.6-35B-A3B (MoE, Thinking ON) and Gemma-4-26B in HermesAgent-20 despite being 1/4 the parameter count TC-11 (Prompt boundary) and TC-12 (Ambiguous request) fixed — areas where the 35B APEX model failed Primary weakness: BugFind-15 false positives — the abliterated model tends to over-eagerly "fix" correct code Please use responsibly 🙏 Acknowledgements Heretic — Automated censorship removal agentscope-ai/QwenPaw-Flash-9B — Base model llama.cpp — GGUF quantization and inference BenchLocal — Local model agent evaluation suite