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lkjiop8
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
lkjiop8
Model Tree
Qwen/Qwen3.6-27B
Input Modalities
Output Modalities
Supported Functionality
markdown
unsloth/Qwen3.6-27B+ lora-stage0-v5-5-final (v5-5 inherited, optional)+ lora-stage1-v5-6-final (v5-6 SFT s1)+ lora-stage2-v5-6-final (v5-6 SFT s2)+ this adapter (r=32) (uncensoring U1 SFT + U2 DPO)
phoenix_boundary_refusal.jsonl + phoenix_authorized_low_refusal.jsonl.Note on anti-hallucination: the v5-6 DPO adapter was not present at training time, so Stage U2 mixed in ~250 v5-6 DPO pairs (phoenix_v56_dpo_quality_pairs.jsonl + phoenix_failure_corpus_* + phoenix_v55_anti_hall_dpo.jsonl) to recover the evidence-discipline signal. Each mixed pair had its system prompt swapped to Yuanl-Free to keep the persona consistent across the U2 dataset.
Use the lkjiop8/Yuanl-27B-v5-6-uncensored-MTP-GGUF
repo for ready-to-run MTP GGUF files (Q8_0 and Q4_K_M).
This adapter removes refusals on cybersecurity-domain questions only. The operator inherits all legal and ethical responsibility for how the model is used. Run only on authorized targets / isolated labs / your own infrastructure. Add an application-layer policy filter for any deployment context that requires one.