Endy-Qwen3.6-CyberSec-35B-A3B (abliterated, vision) — fp16 weights
QLoRA fine-tune of huihui-ai/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated, specialized for coding, IT and cybersecurity, kept uncensored. Full-precision (fp16) merged weights.
- Architecture:
qwen3_5_moe— MoE (~35B total, ~3B active) + linear-attention (DeltaNet) + native MTP + vision (multimodal). Merged directly so the MTP head + vision tower are intact. - GGUF quantizations (Q2_K…Q8_0 + mmproj for llama.cpp): endystrike/Endy-Qwen3.6-CyberSec-35B-A3B-GGUF
- Use these fp16 weights to run with transformers / vLLM (incl. image input) or to produce your own quantizations.
Training
QLoRA (Unsloth, 4-bit NF4, r32 α64 on q/k/v/o_proj), 2 epochs, train_on_responses_only, on 90,470 coding+cybersecurity chat examples (+914 val). Checkpoint step 2250 selected by validation loss. LoRA merged directly into the fp16 base (preserving MTP + vision), keeping the model multimodal.
Datasets (examples used, licenses)
| Dataset | Examples | Domain | License | Teacher |
|---|---|---|---|---|
| AlicanKiraz0/Cybersecurity-Dataset-Fenrir-v2.1 | 39,286 | cybersecurity | Apache-2.0 | — |
| Trendyol/Trendyol-Cybersecurity-Instruction-Tuning-Dataset | 22,677 | cybersecurity | Apache-2.0 | — |
| WithinUsAI/fable_5_distillation_merged_cleaned_25k | 12,464 | coding | Apache-2.0 | Claude Fable 5 |
| Jackrong/DeepSeek-V4-Distill-8000x | 6,292 | coding | MIT | DeepSeek-V4 |
| lordx64/agentic-distill-fable-5-sft | 4,593 | agentic coding | AGPL-3.0 | Claude Fable 5 |
| CyberNative/Code_Vulnerability_Security_DPO | 4,111 | secure coding | Apache-2.0 | DeepSeek-Coder-33B |
| beyoru/Deepseek-v4-pro-max-distill-1500x | 946 | coding | Apache-2.0 | DeepSeek-V4 |
| WithinUsAI/claude_mythos_distilled (stripped) | 101 | reasoning | Apache-2.0 | declared synthetic |
| Total | 90,470 |
License
AGPL-3.0 (one training dataset, lordx64/agentic-distill-fable-5-sft, is AGPL-3.0 copyleft → the derived model inherits it).
Disclaimers
- Parts of the base lineage were distilled from proprietary models (Claude Opus 4.7 / Fable 5, DeepSeek V4) by third-party authors; their usage policies may restrict training competing models on their outputs. Disclosed, not waived.
- Uncensored/abliterated — outputs are unfiltered. Intended for authorized security research, pentesting, secure-coding and education. You are responsible for lawful use.
- Not affiliated with or endorsed by Qwen, Anthropic, DeepSeek, or the dataset authors.