HPT

HPT fills a specific niche: open-source multimodal LLMs that actually run on edge devices. The 4B Edge model is genuinely impressive for its size, scoring competitively on benchmarks like MMMU and MathVista while being deployable on a phone. If you're building a mobile app that needs on-device image captioning, document parsing, or visual Q&A without phoning home, HPT 1.5 Edge is probably your best bet among open models. The 8B Air model extends this capability to server-class hardware with stronger reasoning, using Llama 3's language chops. Where it bites: documentation is thin. The GitHub repos are functional but sparse—you won't find extensive tutorials or troubleshooting guides. There's no hosted API, no chat UI, no customer support. This is strictly for developers who can clone a repo, install dependencies, and run inference scripts. If you want a turnkey multimodal API, look at GPT-4V or Claude 3. Compared to LLaVA or Qwen-VL, HPT holds its own at similar sizes, but its edge focus is the differentiator. LLaVA has broader community support and more fine-tuned variants. HPT's advantage is the explicit edge optimization—the 4B model uses techniques like quantized weights and efficient attention to run fast on CPUs and mobile GPUs. In practice, we'd reach for HPT when building a privacy-preserving visual assistant for a smartphone app, or deploying document understanding on retail kiosks. Skip it if you need a comprehensive SDK, managed hosting, or multimodal generation today (the diffusion models are still research-stage).