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
1B-parameter text-generation model tuned for conversational and agentic workflows with a focus on security and vulnerability-detection; suited for low-cost or on-prem/edge deployments and terminal-agent integrations.
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.
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.