Photo7b — Rar

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:

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

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

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. A lightweight MLP (Multi-Layer Perceptron) or a C-Abstractor

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

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 If you are looking for a specific

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