GS-qwen3.8-27B-V2-tulu-commitpackft-compelete

Model · Text generation

Qwen3.8-27B-OBLITERATED

OBLITERATUS

V3 applies iterative refinement on top of V2's complementary blend, with targeted corpus expansion. The result: genuine liberation — not just removal of hard refusals but elimination of safety-lecture deflections. - Genuinely answers restricted queries — provides real substance instead of safety lectures - 20/20 on code generation tasks — functional implementations, not disclaimers - Thinking ON compatible — no refusals in either thinking mode - Honest scoring — every response manually audited for real substance, not just absence of "I cannot" - -2.1pp MMLU — modest capability cost for genuine liberation If you're using this model in an agent harness (coding agent, pentest framework, etc.)…

18+ only. This model generates sexually explicit fiction. A fine-tune of Qwen/Qwen3.8-27B for Chinese adult creative writing. It was trained with LoRA on about 4.2k instruction examples built from Chinese adult fiction, and the LoRA was then merged into the base weights, so this repo loads like a normal full model. Requires a recent transformers with Qwen3.8 support. The ~50 GB of bf16 weights need multiple GPUs or CPU offload. Avoid greedy decoding, which makes Qwen models more likely to repeat themselves. - Content is fictional and intended for adult readers only. - Trained mostly on long-form prose; instruction following outside creative writing may be weaker than the base model. - May…

Model · Text generation

Swift-1.5-Qwen3.8-27B-MXFP4-GPTQ

SlyBase

quantised to OCP MXFP4 (fp4e2m1 weights, e8m0 scales, group size 32) with GPTQ, in the compressed-tensors mxfp4-pack-quantized format. Weight-only: activations stay in 16-bit. This is the source checkpoint of the Radiance container and the checkpoint the vllm-sly-radiance image is benchmarked with (together with the DFlash2 drafter syvai/Qwen3.8-27B-DFlash2-W4A16). 1. Qwen/Qwen3.8-27B (Apache-2.0) 2. ukisai/Swift-1.5-Qwen3.8-27b (Swift Open License v1.0) 3. akumaburn/Swift-1.5-Qwen3.8-27b-heretic (abliterated, BF16) 4. This checkpoint: llm-compressor GPTQModifier, scheme MXFP4A16, static act-order, sequential pipeline, 89 calibration samples, max sequence length 2048 (recipe.yaml) The…

V3 applies iterative refinement on top of V2's complementary blend, with targeted corpus expansion. The result: genuine liberation — not just removal of hard refusals but elimination of safety-lecture deflections. - Genuinely answers restricted queries — provides real substance instead of safety lectures - 20/20 on code generation tasks — functional implementations, not disclaimers - Thinking ON compatible — no refusals in either thinking mode - Honest scoring — every response manually audited for real substance, not just absence of "I cannot" - -2.1pp MMLU — modest capability cost for genuine liberation If you're using this model in an agent harness (coding agent, pentest framework, etc.)…

Model · Text generation

Ternary-Bonsai-27B-mlx-2bit

Prism ML

Full 27B-class reasoning in ternary transformer weights — on everyday laptops - \~7.2 GB deployed footprint (down from \~54 GB FP16) — full 27B-class reasoning on a standard laptop or a single GPU - 95% of FP16 intelligence retained: 80.49 average across 15 thinking-mode benchmarks — a higher score than the conventional IQ2XXS build (72.73) at less than two-thirds of its footprint - Retains thinking, reasoning, and agentic behavior deep in the sub-4-bit regime, where conventional low-bit representations collapse: math within two points of full precision (93.40), coding at 85.96, agentic tool use at 74.01 - End-to-end ternary language weights across embeddings, attention projections, MLP…

Qwen3.8-cyber-RedTeam-Surgical-Abliterated (27B) is an unconstrained foundation engine engineered for autonomous cybersecurity agents, vulnerability research, and offensive cyber operations. Built for executing low-level technical directives rather than conversational chatting, it features complete refusal orthogonalization, native multi-step tool calling, and high-throughput linear-attention efficiency: - Quantization Rationale (Native FP8 vs. 4-bit INT4/AWQ): Evaluated across adversary emulation and systems engineering tasks: A standalone orchestration utility is included for automated environment bootstrapping, weight synchronization, and hardware-adaptive server initialization: This…