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
Utilizes a pre-trained CLIP-ViT-L/14 or similar high-resolution transformer to extract spatial features. Photo7B rar
If you are looking for a specific .rar archive containing the weights, code, or data for this model, please ensure you are downloading from authorized repositories like Hugging Face or GitHub to avoid security risks. 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. or data for this model
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: