Discover the Best AI Resources
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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.
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.
Provides 1.7M agent interaction traces in terminus-2 format for training and evaluating agentic LLMs and RL agents. Compiled from 219 source datasets across code repair, shell, math, competitive programming and general tasks; produced with the Harbor harness.
Provides step-by-step guides to integrate DeepSeek V4 models (deepseek-v4-pro and deepseek-v4-flash) into 22 popular AI agents and coding-assistant tools. Each entry shows installation, configuration, and first-run steps for tools like Claude Code, Qwen Code, Codex, Cline, Deep Code, and more.
Aggregates 750k+ Harbor-compatible agentic tasks from 100+ public sources (Parquet shards preserved). Includes tasks with and without verifiers for RL evaluation or SFT/datagen workflows, enabling reproducible trace generation.
Multilingual on-device translation model compressed to 1.25-bit via the Sherry quantization, supporting 33 languages and 1,056 directions in a 440MB package for offline mobile translation and demos.
Collection of 76 image-centric multimodal subdatasets (≈6.9M samples, ~39.56B estimated tokens) for training vision–language models, each published with a standardized conversation JSONL and dataset card. Media are referenced by path/URL and must be fetched separately; licensing is primarily CC-BY-4.0 with per-subdataset variations.
Terminal-native AI coding assistant optimized for the deepseek-v4 model. Provides configurable "thinking" modes and reasoning-intensity controls, agent skills for extensibility, MCP integration, and a shared config with a VSCode plugin.
Parallel Khasi–English sentence pairs for machine translation research focused on low-resource NLP in Northeast India. Provided as a small CSV (sentence_id, english_text, khasi_text) under CC BY‑NC 4.0 for non-commercial research use.
Provides 104.9M curated image–text pairs with precomputed embeddings, structured annotations and pre-encoded VAE latents for text-to-image pretraining and retrieval. Combines filtered web sources and synthetic samples with multi-model re-captioning, deduplication and safety filters; Apache-2.0.
Provides reusable Docker compose files, scripts and benchmarked configs to serve modern LLMs (Qwen, Gemma, etc.) on 1–2 NVIDIA RTX 3090/4090/5090 GPUs. Multi-engine (vLLM, llama.cpp, ik_llama), measured TPS/context tradeoffs, and validated single/dual‑GPU recipes.
Converts latent representations into high-resolution images by using a conditional pixel-space diffusion decoder that merges decoding and upsampling into a single generative step. Released checkpoints are 4-step distilled (2k and 2kto4k variants) and pair with specific VAE/encoder weights; license restricts use to non-commercial research.