AI Model2026
A reasoning-enhanced Mixture-of-Experts (MoE) LLM fine-tuned for multimodal image-text-to-text tasks and long-context reasoning; built on Qwen3.6-35B-A3B with LoRA and released as an experimental GGUF community model.
AI Model2026
7B multilingual translation model optimized for instruction-following and low-latency deployment across 33 languages; provides quantized/FP8/GGUF builds and integrations (vLLM, llama.cpp) for server and on-device inference.
AI Model2026
A GGUF-quantized build of Qwen3.6-35B packaged by unsloth for local and accelerated inference. Adds MTP speculative decoding guidance and deployment notes for llama.cpp, vLLM, SGLang and long-context/multimodal use cases.
AI Model2026
Generates production-ready offensive-security artifacts from prompts—Nuclei templates, CVE PoCs, exploit scripts and pentest tooling—fine-tuned on bug-bounty reports and CVE writeups and quantized for consumer/server GPU deployment.
AI Model2026
Early pretraining checkpoint of a compact multilingual causal LM aimed at low-memory deployment and Indic language support. Explores a Shared KV cache mode that can cut KV-cache memory by ~50% for inference; results are provisional (not a final, fully trained model).
AI Agent2026
Runs shared, self-hosted AI agents in isolated Kubernetes sandboxes accessible from Slack or an API. Provides durable workflows, reusable tool plugins, and network-edge credential injection (iron-proxy) so agents can execute real work securely and audibly for teams.
AI Model2026
Provides a locally runnable 26.9B Qwen3.6 checkpoint that surgically reduces refusal behavior in weight space while preserving capability; ships bfloat16 safetensors and a GGUF quant ladder for local runtimes and red-team evaluation.
AI Train2026
End-to-end Python framework for training and serving NVIDIA's Cosmos world models (Cosmos3), integrating distributed training (FSDP/TP/CP/PP), DCP/safetensors checkpoints, dataset adapters, multiple inference backends, online serving, and agent skills.
AI Model2026
A 1.08B-parameter causal LLM engineered for on-device text generation with native long-context (131k tokens) and built-in Think/No-Think modes. It emphasizes tool-calling support, lightweight deployment formats (BF16, GGUF, MLX), and RL+OPD post-training for stronger reasoning and code generation.
AI Model2026
Processes images and text to produce structured, reasoning-rich text outputs for high-throughput agentic workflows. Sparse MoE design (198B total, ~11B active per token), 256k context window and selectable reasoning levels—optimized for single-pass parsing, verification, and multi-step automation.
AI Model2026
Quantized NVFP4 build of the Qwen3.6-35B MoE language model, optimized with NVIDIA Model Optimizer to cut model size and GPU memory by ~3.06× for inference. Designed for vLLM and NVIDIA GPU deployments (Hopper/Blackwell).
AI Model2026
Hybrid LFM2.5 text-generation model optimized for on-device assistants and agentic workflows — 8.3B total / 1.5B active parameters with 131,072-token context. Prioritizes low-latency, high-throughput inference and multilingual instruction-following; not optimized for pure heavy programming or knowledge-heavy QA without retrieval.