Zero-shot multilingual text-to-speech checkpoint for speech generation and voice cloning with a compact footprint. Features an ~170M-parameter main model plus a bundled ~120M-parameter codec decoder, with primary support for Chinese and English; other languages show more variable quality and long/noisy references reduce fidelity.
Provides 2-bit quantized weights of Qwen3.8-27B (~10.15 GB) for local deployment, enabling the full 27B parameter model to run on a single 24 GB GPU with long-context support. Delivered as safetensors plus a companion SGLang runtime; measured to match FP8 reference on common benchmarks with small or no quality loss.
A 4B-parameter on-device general-purpose LLM for chat, writing, translation, coding and agentic workflows with native 1,000,000-token context. Uses a hybrid attention design to enable long-context efficiency, pretrained on ~20T tokens, and compatible with vLLM, llama.cpp, Ollama and LM Studio.
Open-weights LLM fine-tuned for phone-based voice agents that prioritizes low latency and reliable tool/function calling. Based on NVIDIA Nemotron 3 Nano (30B total, 3.5B active), supports very long contexts (262,144 tokens) and recommends temperature=0 with thinking disabled for deployment.
Provides FP8-quantized Hugging Face weights and config for Qwen3.8-Flash-Next (block size 128), preserving near-original performance. Compatible with Transformers, vLLM, SGLang and TokenSpeed; intended for efficient deployment of a 125B multimodal causal LM with very long context support.
Experimental open-weight multimodal LLM preview designed for long-context, agentic workloads. It introduces hybrid sparse attention (QSA), gated residual streams, and large offloadable n‑gram embeddings (51B) alongside a high-sparsity MoE (125B total, 6B active) to trade memory for runtime efficiency and improved long-horizon reasoning.
Conditions a MiniMax‑H3 video generator with a single ControlNet‑Union checkpoint to accept Canny, Depth, HED, MLSD or Pose control videos and run video inpainting. Guidance‑distilled for one‑pass inference; requires the base MiniMax‑H3 weights and specific control-branch config.
A large open-weights MoE language model for complex coding, long-horizon agentic workflows, and cyber/security evaluations; post-trained from the GLM-5 family with substantial gains over GLM-5.2. Provides FP8/BF16 checkpoints and native support for very long contexts (up to 1M tokens).
Generates low-latency, instruction-driven English and Chinese speech for voice cloning, voice design, and directed performances; supports real-time streaming, reference-free voice creation, and reference-guided cloning. Open-weight PyTorch model released under a research/non-commercial license with GPU recommendations.
Generates L2-normalized multimodal embeddings (default 4,096‑D) for text, images, videos and visual documents, supporting interleaved inputs and flexible dimension truncation (Matryoshka). Designed for cross-modal retrieval, ranking and downstream retrieval systems; audio is not supported.
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.
GGUF-quantized, refusal-removed build of Qwen3.8-Flash-Next for llama.cpp that provides multimodal (image+text), reasoning and tool-calling capabilities; released for security research and red-teaming under the Apache-2.0 license.