Studies train-time knowledge injection via hypernetworks that generate fixed LoRA adapters from large fact corpora, empirically characterizing power-law scaling across hypernetwork depth, width, and target model size and reporting improved OOD generalization.
AI Model2026
Provides GGUF-format quantized shards of Laguna S 2.1 for local or self-hosted inference—packaged for llama.cpp/llama-server and usable with vLLM/Transformers runtimes; targeted at long-context, agentic coding workloads.
AI Model2026
Compact 3B-scale agentic LLM for multi-step tool use and reasoning, using a Looped Transformer to increase capacity without adding parameters; built for local deployment with configurable "thinking" modes and benchmark gains vs larger open models.
AI Model2026
A 250B-parameter mixture-of-experts LLM that activates 15B parameters per token to lower inference cost for agentic tasks—tool calling, long-context reasoning, and coding. Uses a hybrid softmax+linear-attention stack with 1M-token context and supports English, Korean, and Japanese; requires H200/B200-class GPUs to run efficiently.
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 Model2026
An open-weight LLM focused on deep reasoning, native agentic tool use, and repository-scale code understanding — Mixture-of-Experts architecture with an extended context window and permissive licensing.