AI Model2022
A compact pretrained bidirectional Transformer distilled from BERT base for masked language modeling and downstream NLP fine-tuning. Trained via knowledge distillation on BookCorpus and English Wikipedia, it reduces size/latency (≈67M params) while preserving BERT-like representations.
AI Dataset2022
Contains 40,000 teacher-generated reasoning traces distilled from the Qwen3.8-27B model for supervised fine-tuning and analysis. Covers code, math, science and logic; each example pairs a <think> chain-of-thought with a final response and is distributed in JSONL/Parquet for SFT workflows.
AI Deploy2024
Applies quantization, pruning, distillation, speculative decoding and sparsity to compress and accelerate deep learning models, producing exportable checkpoints ready for deployment on TensorRT‑LLM, TensorRT, vLLM and similar inference runtimes.
AI Model2024
Generates images from text prompts using a 12-billion-parameter rectified-flow transformer trained with guidance distillation for more efficient sampling. Distributed with diffusers/ComfyUI support and multiple conditioning/editing variants; weights released under a non-commercial license.
AI Agent2026
Turns heterogeneous traces (chats, docs, emails, transcripts) into versioned, inspectable agent 'Skills' that capture both Persona and Work behaviors; supports multi-source collection, incremental merges and corrections, and installation across multiple agent hosts.
AI Dataset2026
Provides agentic instruction‑tuning trajectories for software‑engineering tasks, formatted for supervised fine‑tuning and agent training. Contains multi‑file edits, tests, docs and structured agent traces (≈5,115 records, 1.9 GiB). Intended for commercial use; licensed CC‑BY 4.0 with additional permissive licenses.
AI Dataset2026
Provides ~5M model-generated reasoning chains (within 5k sequence length) with structured fields for supervised fine-tuning, reasoning distillation, and instruction tuning. Includes separate fields for prompt, reasoning trace, final answer and a ChatML view; streaming access recommended for large-scale use.
AI Dataset2026
50,000 distilled conversational traces (≈120M tokens) generated from GLM-5.2 for high-reasoning text generation and QA, covering STEM, programming, creative and support dialogues; Apache-2.0 licensed.
AI Dataset2026
Provides behavior-preserving next-step training traces from Claude Fable 5 for supervised fine-tuning and analysis of instruction-following, tool-calling, and coding agents. Runtime-normalized, independently verified, and supplied as Parquet/JSONL with 13,357 cumulative rows from 2,443 accepted trajectories.
AI Dataset2026
Behavior-preserving dataset of GLM 5.2 coding and debugging agent trajectories for supervised fine-tuning and analysis; contains 1,821 cumulative next-step rows from 207 verified trajectories with multi-turn tool use, build-test-fix loops, and runtime-normalized traces.
AI Dataset2026
Provides verified, model-attested end-to-end agent coding and debugging trajectories (JSONL). Each whole-session trace was produced by moonshotai/kimi-k3 on the pi/openrouter runtime, passed acceptance tests and independent model screening — useful for SFT, distillation, and analyzing tool-use behavior.
AI Dataset2026
Provides newline-delimited JSON agent session traces (5 files) generated with Teich for moonshotai/kimi-k3, including recovered and embedded tool-schema snapshots so traces remain training-ready even when tools weren't invoked; includes guidance for Teich data preparation and conversion.