AI Infra2026
Provides an end-to-end platform to evaluate, observe, protect, and optimize LLM and AI agent deployments. Integrates OpenTelemetry tracing, 50+ evaluation metrics, agent simulations, an OpenAI‑compatible gateway, and guardrails; self‑hostable under Apache 2.0.
AI Model2026
Unifies video, audio, image and text understanding for enterprise Q&A, summarization, transcription and document intelligence. The NVFP4 quantized variant reduces footprint to ~20.9GB for more efficient single‑GPU deployment and is tuned for NVIDIA runtimes (vLLM, TensorRT).
AI Dataset2026
Supervised fine-tuning dataset of 7,716 reasoning-focused Q&A examples distilled from the DeepSeek‑V4‑Flash teacher; provided as a cleaned JSONL train split for distillation and SFT experiments.
AI Dataset2026
1,000 JSONL samples containing full chain-of-thought reasoning traces and final answers produced by DeepSeek‑V4‑Pro for use in student-model distillation and quality checks. Prompts sampled from Jackrong/GLM-5.1-Reasoning-1M-Cleaned; Apache‑2.0 licensed.
AI Model2026
Provides unquantized BF16 weights of Qwen3.6-27B with the base model's MTP head grafted in for high-fidelity, uncensored text (and multimodal) generation. Includes deployment guidance and hardware-tuned variants for A100/H100 and Blackwell-class GPUs.
AI Dataset2026
Contains full chain-of-thought traces and final answers generated by DeepSeek-V4-Pro for use as distillation supervision. Key features: full CoT exposure, ~1,000 mixed-domain samples (JSONL/Parquet), Apache-2.0 license — suitable for training student models but watch for source contamination.
AI Model2026
Provides multiple GGUF-quantized exports of Carnice V2 (a merged BF16 SFT of Qwen3.6-27B) optimized for llama.cpp and Hermes-style agent traces, with quant tiers targeted at 16–24GB local GPUs and agentic inference.
AI Dataset2026
Cleaned dataset of reasoning-distillation examples derived from Claude Opus 4.7 outputs — 4,807 retained JSON chat rows after removing simulated-thinking, duplicates, and missing fields. Packaged for model distillation and reasoning evaluation; Apache-2.0 packaging with upstream Anthropic usage constraints.
AI Model2026
GGUF-format, DS4-optimized quantized weights for DeepSeek-V4-Flash, offering q2 (≈80.8 GiB) and q4 (≈153.3 GiB) variants plus an optional small MTP file for speculative decoding. Built for the DS4 inference engine; MIT-licensed.
AI Model2026
Open-source Mixture-of-Experts LLM designed for extremely long-context (up to 1M tokens) text generation and agentic workflows; uses a hybrid attention + MTP design to reduce KV-cache footprint while enabling 42B active parameters and FP8 mixed-precision training.
AI Model2026
Unified omnimodal foundation model for text, image, video and audio understanding and agentic workflows, with support for up to 1M-token context. Combines a sparse MoE LLM backbone, dedicated vision/audio encoders, multi-token prediction, and a hybrid sliding-window + global attention design to reduce KV-cache overhead.
AI Model2026
Provides a GGUF-quantized build of NVIDIA's Nemotron 3 Nano Omni 30B (Reasoning) for local inference — enables multimodal (video/audio/image/text) reasoning, transcription, and document understanding on compatible runtimes such as llama.cpp, Ollama, vLLM, and TensorRT-LLM.