BLIP-2 recorded 536,142 monthly downloads on Hugging Face as of 2024, establishing itself as a leading vision-language model with only 188 million trainable parameters. The model achieved 65.0% accuracy on zero-shot VQAv2 benchmarks while requiring 54 times fewer trainable parameters than Flamingo80B. Semantic Scholar reports 6,423 total citations with 855 highly influential citations as of December 2025.
Salesforce Research released BLIP-2 in January 2023, introducing the Querying Transformer architecture that bridges frozen image encoders with frozen language models. The system processes visual information through 32 query tokens with 768-dimensional representations across 12 hidden layers.
BLIP-2 Key Statistics
- BLIP-2 has 188 million trainable parameters in its Q-Former component, representing less than 2% of total model parameters
- Monthly downloads reached 536,142 for the blip2-opt-2.7b variant on Hugging Face as of 2024
- The model achieved 65.0% accuracy on zero-shot VQAv2, outperforming Flamingo80B by 8.7 percentage points
- Research citations totaled 6,423 on Semantic Scholar with 855 highly influential citations as of December 2025
- Memory requirements drop to 1.8 GB with int4 quantization, enabling deployment on consumer hardware
BLIP-2 Architecture and Parameter Efficiency
The Q-Former component contains 188 million trainable parameters initialized from BERT-base weights. The architecture processes images through 32 query tokens, each with 768-dimensional representations distributed across 12 hidden layers.
The system transforms an 11-billion-parameter language model into a multimodal foundation model while training fewer than 2% of total parameters. This approach delivers competitive performance without the computational overhead of full model fine-tuning.
| Architecture Component | Specification |
|---|---|
| Q-Former Trainable Parameters | 188 million |
| Number of Query Tokens | 32 |
| Query Dimension | 768 |
| Q-Former Hidden Layers | 12 layers |
| Output Query Representation | 32 × 768 |
| Pre-training Stages | 2 stages |
BLIP-2 Hugging Face Adoption Metrics
The blip2-opt-2.7b model recorded 536,142 monthly downloads on Hugging Face throughout 2024. The platform hosts 425 community likes and over 100 Spaces utilizing the model for various applications.
Developers built 38 adapter models on top of BLIP-2’s architecture, with 13 fine-tuned variants addressing specific use cases. The LAVIS library accumulated over 10,400 GitHub stars, reflecting sustained developer engagement.
| Adoption Metric | Value |
|---|---|
| Monthly Downloads (blip2-opt-2.7b) | 536,142 |
| Community Likes | 425+ |
| Hugging Face Spaces | 100+ |
| Adapter Models | 38 |
| Fine-tuned Variants | 13 |
| GitHub Stars (LAVIS) | 10,400+ |
BLIP-2 Benchmark Performance Analysis
BLIP-2 achieved 65.0% accuracy on zero-shot VQAv2, surpassing Flamingo80B’s 56.3% despite using 54 times fewer trainable parameters. The model recorded 52.3% accuracy on GQA and 121.6 CIDEr score on COCO Caption tasks.
Image-to-text retrieval performance reached 92.9% R@1 on Flickr30K, demonstrating superior semantic alignment between visual and textual representations. The NoCaps zero-shot CIDEr score of 121.6 exceeded the previous best of 113.2.
BLIP-2 Comparative Results
Direct comparisons with contemporary models reveal performance differentials across task categories. BLIP-2 maintains competitive accuracy while operating at a fraction of the computational cost.
| Benchmark Task | BLIP-2 | LLaVA-1.5-13B | Flamingo80B |
|---|---|---|---|
| VQAv2 Accuracy | 65.0% | 80.0% | 56.3% |
| GQA Accuracy | 52.3% | 67.4% | N/A |
| Flickr30K R@1 | 92.9% | 87.0% | N/A |
| Trainable Parameters | 188M | Full model | 10.2B |
BLIP-2 Memory Requirements and Optimization
Hardware requirements vary significantly based on numerical precision. The float32 configuration requires 14.43 GB total model size and 57.72 GB training memory with Adam optimizer.
The int4 quantization option reduces memory footprint to 1.8 GB while preserving core functionality. This 4-bit configuration enables deployment on consumer-grade hardware through Bitsandbytes integration.
| Precision Type | Total Model Size | Training Memory (Adam) |
|---|---|---|
| float32 | 14.43 GB | 57.72 GB |
| float16/bfloat16 | 7.21 GB | 28.86 GB |
| int8 | 3.61 GB | 14.43 GB |
| int4 | 1.8 GB | 7.21 GB |
BLIP-2 Research Citation Impact
Semantic Scholar recorded 6,423 total citations for BLIP-2 as of December 2025. The paper accumulated 855 highly influential citations, with 1,272 citations in methods sections and 1,056 in background sections.
The publication ranked among top-cited AI papers of 2023, accumulating over 3,000 citations within its first year. This citation velocity positioned BLIP-2 alongside GPT-4 and LLaVA in multimodal AI research breakthroughs.
BLIP-2 Model Variants and Configurations
Four primary model configurations pair the Q-Former with different language model backends. The blip2-opt-2.7b variant uses OPT with 2.7 billion parameters, while blip2-flan-t5-xxl employs Flan-T5 with 11 billion parameters.
The modular architecture supports pairing with ViT-L/14 from CLIP or ViT-g/14 from EVA-CLIP as vision encoders. Research confirms that stronger image encoders and more capable language models both improve downstream performance.
| Model Variant | LLM Backend | LLM Parameters |
|---|---|---|
| blip2-opt-2.7b | OPT | 2.7 billion |
| blip2-opt-6.7b | OPT | 6.7 billion |
| blip2-flan-t5-xl | Flan-T5 | 3 billion |
| blip2-flan-t5-xxl | Flan-T5 | 11 billion |
BLIP-2 Pre-training Methodology
Three jointly optimized objectives establish multimodal alignment during the first pre-training stage. Image-Text Contrastive learning maximizes mutual information between image and text representations.
Image-Text Matching performs binary classification for fine-grained alignment, while Image-Grounded Text Generation conditions text output on extracted visual features. Ablation studies show ITM provides the largest R@1 improvement of 5-6% on retrieval tasks.
| Pre-training Objective | Function |
|---|---|
| Image-Text Contrastive (ITC) | Maximizes mutual information between representations |
| Image-Text Matching (ITM) | Binary classification for fine-grained alignment |
| Image-Grounded Text Generation (ITG) | Conditions text generation on visual features |
BLIP-2 Application Domains
Image captioning deployments span accessibility tools and content management systems. Visual question answering powers customer service automation and educational platforms, while image-text retrieval enhances e-commerce search and digital asset management.
Medical imaging applications demonstrated particular promise, with fine-tuned variants achieving 71.5% accuracy in early gastric cancer diagnosis tasks. Domain-specific adaptations through the Q-Former module enable knowledge transfer without full model retraining.
| Application Area | Use Case Examples |
|---|---|
| Image Captioning | Accessibility tools, content management |
| Visual Question Answering | Customer service, educational platforms |
| Image-Text Retrieval | E-commerce search, digital asset management |
| Multimodal Chatbots | Conversational AI with image understanding |
| Medical Image Analysis | Specialized diagnostic applications |
FAQ
How many parameters does BLIP-2 have?
BLIP-2 has 188 million trainable parameters in its Q-Former component. Total model size ranges from 4 billion parameters for the OPT-2.7B variant to larger configurations depending on the language model backend selected.
What is BLIP-2 used for?
BLIP-2 powers image captioning, visual question answering, image-text retrieval, and multimodal chatbots. Applications include accessibility tools, e-commerce search, customer service automation, and medical image analysis with specialized fine-tuning.
How much memory does BLIP-2 require?
Memory requirements range from 14.43 GB for float32 precision to 1.8 GB with int4 quantization. The 4-bit configuration enables deployment on consumer-grade hardware while preserving core functionality.
What accuracy does BLIP-2 achieve on benchmarks?
BLIP-2 achieved 65.0% accuracy on zero-shot VQAv2, 52.3% on GQA, and 92.9% R@1 on Flickr30K image-to-text retrieval. The model outperformed Flamingo80B by 8.7 percentage points on VQAv2 despite using 54 times fewer trainable parameters.
How many research citations does BLIP-2 have?
Semantic Scholar recorded 6,423 total citations for BLIP-2 as of December 2025, with 855 highly influential citations. The paper accumulated over 3,000 citations within its first year, ranking among top-cited AI publications of 2023.