Tag

Qwen3.8-27B-Distillation-40K

Contains 40,000 teacher-generated reasoning traces distilled from the Qwen3.8-27B model for supervised fine-tuning and analysis. Covers code, math, science and logic; each example pairs a <think> chain-of-thought with a final response and is distributed in JSONL/Parquet for SFT workflows.

The First Fable 5.1 Reasoning Data

Contains 5,000 coding and chain-of-thought reasoning traces generated by Fable 5.1 — ~150M tokens of step-by-step programming CoT. Deduplicated and filtered for high quality; intended for supervised fine-tuning and distillation to improve reasoning in smaller models.

MoreThought/Fable-5.1-Max-Reasoning-Filtered-1000x

A curated set of 1,000 high-quality chain-of-thought coding and reasoning traces generated by Fable 5.1, totaling ~30M tokens (109 MB). Designed for SFT/distillation to teach smaller models step-by-step programmatic reasoning and debugging.

Swift-Qwen3.8-27B

A reasoning‑efficient fine-tune of Qwen3.8-27B that penalizes overthinking tokens to shorten internal reasoning traces — about 58.3% fewer thinking tokens with <1% accuracy loss and ~1.95× speedup; designed for long-context, multimodal and quantized deployments.

OrcaSAQ-2-Cyber-27B-Uncensored-GGUF

Provides a high-fidelity mixed-precision (≈3-bit) GGUF quant of Qwen3.8-27B tailored for long-horizon agent workloads and cyber-focused red-teaming. Preserves reasoning, thinking mode, MTP speculative decoding and vision (via a separate mmproj); released as an uncensored/abliterated research build under Apache-2.0.

autotrust/GEV-26B-Decide

Provides calibrated probabilistic decisions (yes/no, 2–256 choice, 0–5 score) in one forward pass, with an optional adaptive-thinking mode that invokes Gemma‑4 when System 1 is uncertain; supports text+image, 256K context and vLLM serving, but adaptive thinking is much slower.

Thinking Inertia: LLMs Keep Thinking When Told Not To

Measures when LLMs produce visible reasoning despite instructions not to, introducing three response-level metrics (Empty-Thinking Rate, Question–Pre-answer Relevance, Explicit Inference Rate) and evaluating 'no-thinking' behavior across tasks, models, and prompting interventions.

VideoJev: System One Model for Video Understanding and Interaction

Converts pretrained multimodal LLMs into a System One decision interface that outputs structured, software-friendly decisions for video (temporal spans or multiple-choice answers); uses a shared scoring head and cached/joint inference to raise temporal-grounding mIoU and drastically speed up repeated queries.