AI Model2026
A 3B-parameter causal LLM tuned for verifiable multi-step reasoning in math, coding and STEM using a Spectrum-to-Signal post-training pipeline (SFT, RL, offline self-distillation); not recommended for tool-calling/agent tasks.
AI Model2026
Runs a full 27B-class Qwen3.6-derived language model in a ~3.9 GB 1-bit GGUF pack for on-device inference with a 262K-token context; true 1.125 bits/weight binary representation, DSpark speculative drafter, and llama.cpp (CUDA/Metal/CPU) support.
Stabilizes on-policy policy distillation by dynamically constructing a proximal teacher that controls gradient variance. Provides theoretical global convergence and monotonic improvement bounds, and shows improved training stability, sample efficiency, and final performance on mathematical reasoning tasks with zero extra compute overhead.
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
A multiple-choice benchmark for evaluating language-model arithmetic: 1,000 continuation-style elementary word problems (4 choices, balanced labels) organized by topic, grade band, and difficulty. Designed for base-model continuation log-likelihood scoring; released under Apache-2.0.
AI Dataset2026
A curated collection of 49,772 teacher-generated chat traces from qwen3.8-max-preview for supervised fine-tuning and off-policy distillation. Preserves visible chain-of-thought blocks, emphasizes math/code/reasoning mixes, and includes provenance and licensing cautions tied to Alibaba Cloud Model Studio.
Performs full-parameter post-training of trillion-parameter MoE DeepSeek-V4 models on an Ascend NPU SuperPOD, using a hierarchical optimization of model parallelism, communication orchestration, and kernel execution to increase Model FLOPs Utilization. Also builds CPT/SFT pipelines with solver-verified synthetic data for Operations Research, reporting strong zero-shot Pass@1 results.
AI Dataset2026
Provides a 57,937-row, quality-filtered multi-teacher SFT distillation corpus combining outputs from Qwen3.8-Max, GLM-5.2 and Kimi K3 across math, code, reasoning, tool-use and dialogue. Includes 24 parquet training views (including a pre-tokenized GLM-4.7 view), configurable sampling weights (sft_balanced), and explicit tool-call trajectories for agent training.
AI Dataset2026
Provides large-scale mathematical problem-solving, rewriting, and dialogue data organized into five Parquet-backed subsets for reasoning-oriented language-model training. Subsets support streaming access, Dataset Viewer inspection, and per-subset provenance metadata; licensed Apache 2.0.
Selectively admits dense token-level teacher supervision only after a prompt-level verifier audit, routing prompts that fail the audit to verifier-grounded trajectory supervision instead — reducing harmful updates from confidently wrong teachers and improving teacher GPU utilization.
AI Dataset2026
Provides a bilingual Chinese–English corpus for LLM training covering pretraining, capability-oriented midtraining (16K–256K long contexts), and supervised fine-tuning. Includes ~4.2T pretrain tokens, ~600B midtrain tokens, and ~4.57M SFT samples; sources span web, PDFs/OCR, code, math, QA, and agentic trajectories under mixed upstream licenses.
AI Dataset2026
Provides ~86K verifiable-reward RL training samples across Math, Knowledge (STEM), Long-Context, and Code for post-training LLMs; each sample includes a ground-truth and verifier-friendly JSONL format for stable reward signals. Note: code tasks require an external sandbox to execute tests.