Canadian-developed. Open weights. Focused on defensive infrastructure. Vinci-Cyber-30B-1.0 is a narrow, verifier-grounded Infrastructure-as-Code security specialist of approximately 28.87B parameters, developed by SimpleDirect, a Canadian AI Lab, from IBM Granite 4.1 30B using a small verifier-grounded cybersecurity training corpus. That scope is the claim, and it is deliberately narrow. It reads a Terraform or OpenTofu file and a finding against it, and answers with an edit that applies — or with NOEDIT when the file is already correct. Thirteen Terraform/OpenTofu files, each carrying one real security misconfiguration, none of them in the training corpus. Plus 22 files that are already…
Open weights
apache-2.0
28.9B parameters
131,072 tokens
transformers
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.)…
Open weights
apache-2.0
27.8B parameters
262,144 tokens
mlx
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…
Open weights
apache-2.0
27.8B parameters
262,144 tokens
transformers
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…
Open weights
other
27.8B parameters
262,144 tokens
transformers
A
Model · Text generation
Ahmed
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.)…
Open weights
apache-2.0
27.8B parameters
262,144 tokens
mlx
Open weights
27.8B parameters
transformers
