iamthehimansh/LlamaVision-llama-3.3-1b

LlamaVision-llama-3.2-1b

Model Overview

LlamaVision is a custom multimodal vision-language model that bridges visual understanding with advanced text generation. It utilizes a CLIP-ViT-Large-Patch14 vision encoder and a Llama-3.2-1B language backbone, connected via a trainable linear projector. The model is designed to describe images in detail and answer questions about visual content based on provided prompts.

Architecture

The model follows a modular architecture:

• Vision Encoder: CLIP ViT-L/14 (OpenAI), which encodes images into 1024-dimensional visual features.
• Language Backbone: Llama-3.2-1B (Meta), an auto-regressive transformer optimized for efficient inference and multilingual dialogue.
• Multimodal Projector: A custom linear projection layer that maps visual embeddings into the Llama-3.2-1B hidden space (2048 dimensions).

Training Details

• Dataset: Fine-tuned on a subset of the jackyhate/text-to-image-2M dataset, specifically utilizing high-resolution image-prompt pairs for alignment.
• Objective: Causal Language Modeling (CLM) loss, where the model learns to predict descriptive text conditioned on visual embeddings.
• Precision: Trained using bfloat16 for memory efficiency and numerical stability.

Intended Use

• Direct Use: Automated image captioning and detailed scene description.
• Downstream Use: Visual Question Answering (VQA) and multimodal assistants.
• Out-of-Scope: Not intended for high-stakes medical diagnosis, surveillance, or any activity prohibited by the Llama 3.2 Acceptable Use Policy.

How to Get Started

You can load and use the model with the following Python code :

import torch
from transformers import AutoTokenizer, CLIPImageProcessor,AutoModel

device = "cuda" if torch.cuda.is_available() else "cpu"
model_path = "iamthehimansh/LlamaVision-llama-3.3-1b"
external_image_path = r"./image/bahubali_poster.jpg" 

model = AutoModel.from_pretrained(
    "iamthehimansh/LlamaVision-llama-3.3-1b", 
    trust_remote_code=True
)
# LlamaVisionModel.from_pretrained(model_path)
model.to(device)
model.eval()

tokenizer = AutoTokenizer.from_pretrained(model_path)
image_processor = CLIPImageProcessor.from_pretrained(model_path)

query="Explain this image "
res= model.genrate_with_vision(external_image_path, query,tokenizer,image_processor)
print(res)

Citation and Acknowledgments

If you use this model, please cite the base components:

• Llama 3.2: Meta Platforms, Inc.
• CLIP: OpenAI
• Dataset: jackyhate/text-to-image-2M

MODEL STATISTICS

• Total Parameters: 1,545,289,728
• Trainable Parameters: 6,295,552
• Non-Trainable: 1,538,994,176