AI Model2026
A GGUF-format 9B model derived from Qwen3.5, fine-tuned for agentic coding, tool-calling, reasoning and vision-capable multimodal prompts. Optimized for local 8‑bit inference on 16GB-class machines; community experimental release for research use.
AI Model2026
Research-focused text-to-image foundation model that prioritizes training efficiency: a 3.8B-parameter architecture trained on an 800M image-text corpus with mixed-resolution learning, FLUX.2 VAE, RL tuning, and a distilled 4-step Lens-Turbo for fast high-resolution generation.
AI Model2026
A 4-step distilled variant of Microsoft's Lens foundational text-to-image model for fast, high-resolution image synthesis. Optimized for mixed-resolution inference up to 1440×1440, GPT-OSS text features and FLUX.2 latents, intended for low-latency prototyping and research under an MIT license.
AI Model2026
Reasoning-enhanced 27B dense LLM fine-tuned from Qwen3.6-27B and released in GGUF format for image-text-to-text and long-context reasoning. Augmented with Trace Inversion reconstructed chains, three-stage SFT curriculum and MTP/vision support; community research release.
AI Model2026
Provides a 1-billion-parameter English pretrained language-model checkpoint that uses a dual-timescale Hierarchical Reasoning Model to increase effective compute depth. It's a PrefixLM pre-alignment checkpoint with composite-prefix modes for chain-of-thought style outputs; not instruction-tuned and requires downstream SFT/RL for assistant use.
AI Model2026
W4A4-quantized build of a 25B-parameter multimodal LLM that produces text from image+text inputs and supports conversational tool use. Trades very small quality differences for much lower GPU memory and latency so inference can run on smaller accelerators (vLLM support).
AI Video2026
Generates minute-scale, 720p videos from a single image using a 2.6B image-to-video diffusion transformer with precise 6‑DoF camera control and an optional LTX‑2 refiner; designed for long-context, memory-efficient modeling but requires large refiner checkpoints (~41 GB).
AI Model2026
A GGUF-format 9B LLM fine-tuned for code generation and agentic tool-calling that uses Multi-Token Prediction (MTP) and draft heads to increase throughput and long-range planning. Intended for local inference and research/experimental coding workflows; Apache‑2.0 license.
AI Dataset2026
Dataset of 5,000 reconstructed chain-of-thought samples produced by trace‑inversion from Claude‑opus‑4.7 summaries — packaged for SFT/DPO fine‑tuning. Key features: reconstructed CoT traces, multilingual prompts, gzip .jsonl format. Best used for reasoning distillation and model-level supervision; synthetic traces may need extra verification.
AI Dataset2026
Provides 9,000 reconstructed chain-of-thought (CoT) SFT examples produced by trace inversion from Claude Opus 4.6 outputs for fine-tuning reasoning-capable LLMs. Multilingual, packaged as .jsonl.gz and SFT/DPO-ready; verify numeric/code cases before training.
AI Model2026
Provides a locally runnable 26.9B Qwen3.6 checkpoint that surgically reduces refusal behavior in weight space while preserving capability; ships bfloat16 safetensors and a GGUF quant ladder for local runtimes and red-team evaluation.
AI Audio2026
Converts long-form multi-speaker audio/video into a compact, speaker-aware transcript with timestamps and anonymous speaker labels in one pass. Combines ASR and diarization in a single model, supports custom prompts/hotwords, and targets meetings, podcasts, interviews and long recordings.