AI Model2026
A natively multimodal model for text and image→text generation, long-context reasoning, and complex coding/agent workloads. Uses 320B total / 18B active params with a hybrid sparse+linear attention and manifold-constrained hyper-connections to reduce long-context serving cost.
Generates unified embeddings for text, images, video, visual documents and interleaved multimodal inputs with configurable output dimensions and Matryoshka truncation to trade accuracy for cost. Model weights and code are released under Apache-2.0; the 9B variant scores 80.6 on MMEB-v2.
AI Dataset2026
A 10‑billion‑document retrieval benchmark with per‑document 768‑dim unit‑norm dense embeddings and mGTE sparse embeddings, FineWeb text/metadata, and exact top‑1000 MS MARCO ground truth for ~120k queries. Built for large‑scale evaluation of dense/sparse/hybrid retrieval, filtered search, indexing, ANNS algorithms, and embedding compression.
AI Video2026
Provides Parallel Decoding Distillation (PDD) LoRA adapters that accelerate MiniMax-H3 video generation into few inference steps. Includes official 8-step Acc LoRAs for FL2VA and Ref2VA (rank=64, network_alpha=64, BF16), demo comparison videos, and example scripts using Diffusers' MiniMax-H3 ModularPipeline.
AI Dataset2026
Evaluates retrieval-augmented generation by decomposing user queries into sub-queries and answers into atomic claims, scoring retrieval by query coverage and generation by claim verifiability. Reference-free benchmark with 800 queries, inlined retrieved chunks, and answers from multiple RAG systems; runs locally without API keys.
AI Dataset2026
Contains 244 Cursor agent sessions recorded from Claude Fable‑5, formatted for training and research. Sessions include multi-turn assistant/tool interactions and are Teich-compatible; several rows exceed one million characters, so apply explicit tokenization and oversize policies before training.
AI Model2026
GGUF-quantized build of Qwen3.8-Flash-Next for image-text-to-text inference and local deployment. Ships with Unsloth Dynamic 3.0 quantization, thinking-mode controls (preserve_thinking, reasoning_effort), and native long-context support (262k, extensible to 1M with YaRN).
AI Model2026
Provides a GGUF-quantized build of GLM-5.3-Flash for local text-generation and inference. Key features: 320B total / 18B active parameters, hybrid sparse+linear attention, native multimodal pretraining and Unsloth Dynamic quantization. Best for developers running GGUF local inference workflows.
AI Model2026
A 770B-parameter Mixture-of-Experts instruct model from Tencent that natively supports 1,048,576-token contexts, Gated DSA attention, and speculative MTP decoding; open-sourced under Apache-2.0 with BF16 and FP8 weights for deployable inference.
AI Model2026
Provides per-tensor non-uniform GGUF quantizations of Qwen3.8-27B using GSQ and RCO, delivering high accuracy at 2.5–3.5 bits and including a BF16 vision projector for multimodal use. Optimized to run unmodified in llama.cpp, Ollama, and LM Studio.
AI Model2026
Drop-in abliterated (refusal-removed) build of GLM-5.3-Flash that bakes refusal-direction removal into block-FP8 safetensors, yielding an uncensored 320B (18B active) multimodal MoE model with a 1M-token context. Intended for red-teaming, interpretability, and robustness research; MIT license; not for production without added guardrails.
AI Model2026
Provides a cybersecurity-focused CRACK variant of GLM-5.3 FP8 that reduces refusals for offensive-security, red-team, exploit-development and malware-analysis queries while retaining native FP8 speed on Hopper GPUs; MIT-licensed for authorized security work.