Meta: Llama 3.2 11B Vision Instruct
Llama 3.2 11B Vision Instruct: see, read, and create.
Llama 3.2 11B Vision Instruct is a powerful model that combines visual understanding with text generation. It can analyze images, read text within them, and produce detailed descriptions, stories, or instructions based on what it sees.
Use it when you need to describe a photo, generate captions, extract information from documents, or create content inspired by visual inputs. It excels at tasks that require both seeing and reasoning, like explaining a chart or writing a story from a picture.
What makes it distinctive is its ability to handle high-resolution images and follow complex instructions about them. It can count objects, read signs, and understand spatial relationships, making it versatile for creative and practical applications.
- Image captioning and description
- Visual question answering
- Document and chart analysis
- Creative writing from images
- Content moderation and tagging
It can process both images and text together, answering questions about visual content.
It handles detailed images, reading small text and recognizing fine objects.
It follows complex prompts about images, like counting or describing relationships.
- Provide clear, high-resolution images for best results.
- Be specific in your instructions about what to look for.
- Use natural language questions for visual Q&A.
- Combine image and text prompts for richer outputs.
- May misinterpret abstract or ambiguous images.
- Struggles with very low-quality or heavily distorted visuals.
- Cannot generate images, only text about them.
Write a short story inspired by this surrealist painting, focusing on the central figure.
List the ingredients visible on the counter and suggest a recipe using them.
Based on this street photo, write a brief travel guide for the location.
Fixes the random generation for reproducible results with the same prompt.
Limits token choices to a cumulative probability, balancing diversity and coherence.
Sets the maximum length of the generated text response.
Controls randomness: lower values make output more focused, higher values make it more creative.
Encourages new topics by penalizing tokens that have been used at all.
Reduces repetition by penalizing tokens that have already appeared.
Preço sob consulta
Os preços exibidos são o que você paga na AllInOne AI. Sem surpresas de markup do provedor.
Can this model generate images?
No, it only understands and describes images, producing text outputs.
What image formats does it support?
It works with common formats like JPEG and PNG, ideally high-resolution.
How many objects can it count in an image?
It can count dozens of objects but may be less accurate with very dense scenes.