Meta: Llama 3.2 11B Vision Instruct
By Meta Llama
Released: 2024-09-25
Meta Llama Llama 3 2 11B Vision Instruct, developed by Meta, features 11B parameters and 131k-token context window. Llama 3.2 11B Vision is a multimodal model with 11 billion parameters, designed to handle tasks combining visual and textual data. It excels in tasks such as image captioning and visual question answering, bridging the gap between language generation and visual reasoning. Pre-trained on a massive dataset of image-text pairs, it performs well in complex, high-accuracy image analysis. Its ability to integrate visual understanding with language processing makes it an ideal solution for industries requiring comprehensive visual-linguistic AI applications, such as content creation, AI-driven customer service, and research. Click here for the [original model card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/MODEL_CARD_VISION.md). Usage of this model is subject to [Meta's Acceptable Use Policy](https://www.llama.com/llama3/use-policy/). Priced affordably at $0.05/1M tokens.
Visit Meta: Llama 3.2 11B Vision InstructAI-Powered
Leverages advanced AI technology to deliver cutting-edge capabilities and results.
Fast & Efficient
Optimized performance ensures quick results without compromising on quality.
Purpose-Built
Specifically designed for llms tasks and workflows.
Meta Llama Model Timeline
164k tokens context
1,049k tokens context
328k tokens context
131k tokens context
131k tokens context
131k tokens context
131k tokens context
131k tokens context
60k tokens context
131k tokens context
33k tokens context
16k tokens context
131k tokens context
10k tokens context
131k tokens context
8k tokens context
8k tokens context
8k tokens context
Specifications
AI Evaluation
Combines language understanding with search capabilities. Excels at finding and synthesizing information from various sources.
Pros
- Budget-friendly at $0.05/1M tokens
- 131k token context window
- Advanced logical reasoning
- Image and visual analysis
Cons
- API integration required
- May need prompt tuning
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