AI Model2026
An open-weight LLM checkpoint post-trained for agentic deep search: Qwen-compatible reasoning and tool-call formats optimized for web browsing, multi-source evidence aggregation, long-horizon planning and recovery from failed interactions; typically paired with the AxisAgentic harness.
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, Qwen-derived thinking model optimized for agentic deep web search and long-horizon planning. Provides Qwen-compatible reasoning and tool-call formats for English/Chinese browsing, multi-source evidence aggregation, source verification, and recovery from failed environment interactions.
AI Model2026
A sparsely activated Mixture-of-Experts (MoE) causal language model with 16B total parameters and 2.8B active parameters per token, released with end-to-end checkpoints and training recipe; trained on AMD Instinct GPUs and licensed for research use.
Converts image-content removal into a contrastive on-policy self-distillation signal: the EMA teacher produces next-token distributions with and without image content, uses their log-probability differences to sharpen visual-grounded candidates, and distills that full-distribution target into the student—no external teacher or extra inference cost.
AI Model2026
A text-only open-weight MOE code model (35B total, 3B active) fine-tuned with SFT+RL for agentic coding; achieves strong agentic-code benchmarks, supports 262k context and deployment via Transformers/vLLM; vision weights are not included.
Empirically studies how transformer-based native multimodal pre-training scales under fixed compute, deriving compute- and data-allocation power laws and an efficiency frontier for model size, token count, and data mixture; evaluates cross-modal transfer and multimodal in-context learning.
AI Model2026
Delivers image and video understanding plus a built-in event‑gated streaming gate — a unified 4B multimodal foundation model that uses codec-aligned tokenization to cut visual tokens by >75% and yield up to 3.5× wall‑clock inference speedup for streaming and long‑horizon video tasks.
AI Model2026
Generates text from text, image, or audio inputs using a native multimodal, Mixture-of-Experts autoregressive transformer (276B total / 12B active) with up to 1M-token context; targeted at conversational, agentic, coding and multimodal applications.
AI Model2026
Open-weight multimodal Mixture-of-Experts LLM with native vision and a 1,048,576-token context window. 2.8T parameters (104B activated), MXFP4 quantization, released for agentic long-horizon coding, knowledge work, and vision-in-the-loop workflows.
AI Model2026
GGUF-quantized build of Moonshot AI's Kimi K3 for local inference: MXFP4-aware quantization, image-text-to-text pipeline support, native vision and a 1,048,576-token context window. Intended for local GGUF runtimes (vLLM, SGLang, TokenSpeed) with Kimi K3 license constraints.
AI Model2026
A 350M-parameter multilingual bidirectional masked-language encoder with an 8,192-token context window, intended for fine-tuning on classification, token-level tasks, retrieval/reranking and semantic-similarity; optimized for long-context CPU inference and on-device use.