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
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 Dataset2026
Provides 16M+ instruction–response samples and ~81 GB (7,090 compressed GitHub repos) distilled from 68 open-source sources, organized into 8 categories for SFT, coding agents and reasoning research. Model-generated content; released as a curated MIT-licensed collection.
AI Dataset2026
Provides 100,891 JSON-formatted agent conversation examples where each assistant turn includes a short <think> internal reasoning trace before tool/function calls. Human-facing text and tool calls are preserved; intended to fine-tune models to produce concise, cost-efficient chain-of-thought for tool use.
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.
AI Coding2026
Wraps the pi coding agent in an IDE with an editor, terminals, and Git workspaces. Persistent project specs preserve intent across sessions, while isolated branches let agents work in parallel without dirtying your main checkout.
AI Agent2026
Gives coding agents practical guidance for designing and reviewing interfaces, covering accessibility, layout, writing, typography, color and UI polish. Skills can be used through the Skills CLI or as a Claude Code plugin.
AI Agent Papers2026
Acquires repository knowledge via a targeted QA loop before generating patches, decoupling knowledge acquisition from repair. A Questioner and Answerer produce evidence-grounded QA pairs that a Resolver uses to generate fixes; improves Pass@1 on SWE-bench Verified with modest overhead.
AI Model2026
GGUF-quantized builds of a 1B 'Thinking' MiniCPM5 model fine-tuned on Fable 5 (V2) for local runtimes; enhances tool/function-calling, coding and instruction-following, and supports long contexts (up to 128K tokens).