AI Model2026
230M-parameter multilingual instruction-tuned text-only LLM for on-device agentic pipelines and data extraction; 32K context, 19T-token pretraining, optimized for fast CPU/edge inference (e.g., 213 tok/s on Galaxy S25 Ultra, 42 tok/s on Raspberry Pi 5); not for heavy reasoning or complex code generation.
AI Audio2026
Generates English speech locally from text into 24 kHz waveforms with a fixed synthetic male voice. Complete text-to-waveform TTS under ~4M parameters (≈16 MB FP32), supports CPU/CUDA inference, deterministic seeds, long-text chunking and an ONNX export path under Apache-2.0 license.
AI Model2026
A self-improving, agentic coding LLM tailored for terminal-style coding agents and tool-calling, provided as 35B MoE GGUF weights with very large context support. Trained with reinforcement learning to jointly generate task scaffolds and solutions; designed for local inference and OpenAI-compatible tool endpoints.
AI Model2026
Provides a GGUF-quantized local build of Ornith-1.0's 9B dense model for offline inference and terminal-focused coding agents. Supports OpenAI-compatible tool-calling, a 256K context window, and runs via llama.cpp or Ollama on a single high-memory GPU.
AI Model2026
Mixture-of-Experts LLM designed for million-token contexts, combining hybrid compressed attention, FP4/FP8 quantization-aware training for MoE experts, and multi-mode 'thinking' (Non-think/Think High/Think Max); includes a speculative-decoding extension for faster inference.
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.
AI Model2026
Performs zero-shot classification and regression on mixed numerical and categorical tabular data by treating training rows as in-context examples and predicting in a single forward pass. Uses alternating row/column attention and row compression; limited to 10 classes and model weights are non-commercial.
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.
AI Model2026
A large-scale MoE language model for agentic coding and long-context tasks, natively supporting 1M-token context and dynamically activating tens of billions of parameters per token. Uses sparse attention and zero-computation experts to allocate compute per-token; model weights planned for release.
AI Model2026
Provides GGUF/llama.cpp quantized variants of Qwen3.6-27B for local multimodal inference, tuned via online RL to cut average 'thinking' tokens by ≈50% while preserving answer quality; offers Q4_K_M/Q8_0/f16 builds and a separate mmproj for vision input.