AI Model2026
A Hugging Face model checkpoint that attaches a speculative decoding module to DeepSeek-V4-Flash, enabling million-token context handling with MoE architecture, FP4/FP8 mixed precision, and long-context inference optimizations.
AI Model2026
Detects and redacts personally identifiable information (PII) in user-typed text on-device, replacing sensitive values with stable placeholders before any data leaves the browser. Uses a small quantized ONNX token-classification model plus deterministic recognizers for structured identifiers, and applies a policy-driven keep-set for coarse geography.
AI Model2026
An uncensored GGUF build of GLM-5.2 that applies weight “abliteration” to remove refusal filters and produce a locally runnable text-generation model; includes quantization conversions and shard-merge instructions, intended for experimental research rather than production use.
Treats hybrid layer selection as a budget-constrained subset optimization and introduces FlashMorph: a pipeline that equips each transformer layer with a linear-attention branch, jointly optimizes layerwise gates on synthetic long-context retrieval data, then discretizes, distills, and finetunes—achieving strong long-context recall using only 20M selection tokens.
AI Model2026
Thinking-off fine-tune for coding-agent workflows that prioritizes fast next-step decisions, lower token usage and stable multi-turn tool calling. Highlights: MoE 35B base, MTP speculative decoding, SWE-bench 62.4% (300 cases). Best for local agent loops and automated debug cycles; requires disciplined harnessing and schema consistency.
Trains a transformer-based graph encoder with RL-guided adaptive masking so retrieved subgraphs embed relationships that better align with frozen LLM text encoders, improving GraphRAG performance with non-parametric retrievers on GraphQA benchmarks.
AI Model2026
Provides quantized GGUF weights and configs for Agents‑A1 — a 35B Mixture-of-Experts agent trained for long-horizon, tool-enabled reasoning; supports 262K-context serving and runtimes like vLLM and SGLang.
AI Model2026
Diffusion-based generative model for scene and video synthesis, providing full Diffusers checkpoints and scene LoRA for fast adaptation. Includes Stage‑1 nano (1.3B) and pro (5B) variants and modular transformer/VAE components.
Embodied AI2026
Provides a portable C++ inference runtime to deploy embodied AI models (vision–language–action and world–action) on heterogeneous robot hardware, enabling latency-first batch-1 closed-loop control. Key features include modular multi-rate layers, fused low-latency inference, and extensible head/IO plugins.
AI Model2026
Provides a large Mixture-of-Experts instruct LLM (295B total parameters, 21B active, 256K context) optimized for reasoning, long-context retention and agent workflows; open-sourced under Apache-2.0.
AI Model2026
Provides GGUF-quantized local-deploy weights for a 1B MiniCPM5-derived conversational LLM, embedding a 'thinking' chat template and supporting up to 128K-token context; ships Q4/Q5/Q8/F16 quant files (Q8_0 recommended) for llama.cpp, Ollama, and LM Studio.
AI Model2026
A 1B-parameter 'Thinking' language model fine-tuned on Fable 5 to improve coding and instruction-following; supports chain-of-thought style outputs, XML tool-call format, and up to 128K-token context, with GGUF builds for single-GPU local deployment.