AI Dataset2026
A Chinese public-transit route-planning dataset for training and benchmarking LLMs that generate structured transit routes from origin–destination pairs. Releases include a large CPT corpus, SFT train/test splits, and a 30K real-world benchmark; anonymized and real testsets are provided for privacy-aware, fair evaluation.
AI Dataset2026
Provides 19,331 multi-turn ChatML Hermes reasoning traces for LoRA fine-tuning of local models to behave as Hermes agents. Includes train/valid/test splits, VRAM-tiered variants (nano→spark), ~138K tool-call annotations, and Parquet format under Apache-2.0.
AI Model2026
Mixture-of-Experts LLM tuned for mathematical and coding reasoning, with ~760M active / 8.4B total parameters and post-training for improved stepwise reasoning. Optimized for inference efficiency (vLLM/transformers forks) so it can run in computation-constrained or local deployments; Apache-2.0 licensed.
AI Model2026
Merges Unsloth UD XL quantized GGUF of Qwen3.6-27B with compact Q8_0 MTP heads to enable multi-token (speculative) decoding on llama.cpp builds that support MTP; aimed at image-text-to-text usage with reduced MTP overhead.
Preview of an MoE model family (V4-Pro: 1.6T params, 49B active; V4-Flash: 284B, 13B active) built for 1M-token contexts. A hybrid attention design cuts single-token inference FLOPs to 27% and KV cache to 10% versus V3.2 at million-token length.
AI Model2026
A reasoning-enhanced Mixture-of-Experts (MoE) LLM fine-tuned for multimodal image-text-to-text tasks and long-context reasoning; built on Qwen3.6-35B-A3B with LoRA and released as an experimental GGUF community model.
AI Dataset2026
Provides 173M DNA/RNA sequences (≈1.1 trillion nucleotides) assembled specifically for pretraining genomic foundation models. Includes eukaryote, prokaryote, and mRNA configs plus a 10B‑token eukaryote subset for faster experiments; formatted for streaming and tokenized with Carbon's 6‑mer setup.
AI Model2026
Distilled dev checkpoint of an image foundation model that natively unifies raw pixels and text tokens for text-to-image, image editing, long-text rendering, and subject-driven personalization at up to 2048×2048. The Dev variant targets faster (28-step) inference for iterative use and research.
AI Image2026
Generates and edits high-resolution images (up to 2048×2048) from text and reference images, plus subject-driven personalization. Implements a pixel-level unified transformer that encodes raw pixels and text in one token space and includes a reasoning-driven prompt agent for layout and text rendering.
AI Model2026
Processes text and images to produce conversational, reasoning-focused multilingual outputs for agentic workflows. Built as a sparse MoE decoder (25B active / 218B total parameters) with 128K context and available in BF16/FP8/W4A4 quantizations to balance quality and deployability.
AI Model2026
A 30B mixture-of-experts multilingual translation model supporting 33 languages and instruction-following translation. Offers MoE architecture, fast-thinking mode, and quantized/deployment-ready variants for production translation and subtitle tasks.
AI Model2026
A family of multilingual translation models optimized for real-world, instruction-following translation across 33 languages. The 1.8B model targets on-device use with extreme quantization (≈440 MB via AngelSlim), while 7B/30B variants trade size for higher accuracy.