Photo7b | Rar

Focuses on "feature alignment" using massive image-text pairs (e.g., LAION-5B). The goal is to teach the LLM what objects look like without updating the LLM weights.

The model is fine-tuned on high-quality, multimodal instruction-following datasets (like LLaVA-Instruct). In this stage, both the projector and the LLM weights may be updated to handle conversational context. 3. Key Capabilities

Explaining complex scenes or reading text within images (OCR). Photo7B rar

Built upon the LLaMA-2-7B or Mistral-7B architecture, providing a strong foundation for linguistic reasoning and zero-shot capabilities.

Applying logic to unseen images based on textual prompts. High-Resolution Support: Optimized to process images at pixels to capture small details. 4. Technical Specifications Specification Parameters Context Window 2048 - 4096 Tokens Visual Tokens 576 tokens per image Precision FP16 / BF16 In this stage, both the projector and the

A lightweight MLP (Multi-Layer Perceptron) or a C-Abstractor that maps visual tokens into the language model's embedding space. 2. Training Methodology The model is typically trained in two distinct stages:

Photo7B is a 7-billion parameter multimodal model designed to bridge the gap between high-resolution visual perception and natural language reasoning. By leveraging a decoupled vision encoder and a robust language backbone, Photo7B achieves state-of-the-art performance on benchmarks requiring fine-grained image detail and complex instructional following. 1. Architecture Overview In this stage

Utilizes a pre-trained CLIP-ViT-L/14 or similar high-resolution transformer to extract spatial features.