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Dianabol Treatment: Effect, Results And Follow-up Treatment **AI_Powered Software & Services _ 2024 Landscape** | Category | Key Trends (2023_24) | Typical Applications | Example Vendors / Platforms | |----------|----------------------|----------------------|-----------------------------| | **Generative AI** | _ Prompt_engineering tools for code, docs and design. _ "Zero_shot" model fine_tuning without data. | _ Auto_generation of UI components, API stubs, test cases. _ Rapid prototyping of conversational agents. | OpenAI_s GPT_4o, Anthropic Claude 3.5 Sonnet, Cohere Command R | | **Multimodal Models** | _ Unified vision+text+audio inference; on_device inference via ONNX/EdgeTPU. | _ Image captioning, visual QA for internal knowledge bases. _ Voice_to_text translation with sentiment tagging. | Meta_s LLaVA_2, Google Gemini Pro Vision | | **AI Ops & Observability** | _ Real_time anomaly detection; automated root cause analysis using transformer embeddings of logs. | _ Predictive scaling decisions and cost optimization for cloud workloads. | Datadog AI, New Relic One with GPT_powered agents | | **Robust NLP Pipelines** | _ Retrieval_augmented generation (RAG) models: encode documents with DPR, decode with T5_Base. | _ Knowledge_base question answering; summarization of meeting transcripts. | Hugging Face _ Transformers + FAISS | --- ## 3. 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All examples assume a Python environment (__3.8) with PyTorch, Transformers, and relevant dependencies installed. ### 3.1 Vision Transformer (ViT) ```python import torch from transformers import ViTModel, ViTConfig, ViTImageProcessor # Load pre-trained ViT base model config = ViTConfig.from_pretrained('google/vit-base-patch16-224-in21k') model = ViTModel(config).eval() # Image preprocessing (resize, normalize) processor = ViTImageProcessor.from_pretrained('google/vit-base-patch16-224-in21k') def encode_image(image): """ image: PIL.Image or torch.Tensor C,H,W in range 0-255 Returns: embedding vector of shape 768 """ inputs = processor(images=image, return_tensors='pt') with torch.no_grad(): outputs = model(**inputs) # Use the CLS token representation (outputs.last_hidden_state: ,0,:) embedding = outputs.last_hidden_state:, 0, : return embedding.squeeze(0) # shape 768 # Example usage: # from PIL import Image # img = Image.open('example.jpg') # vec = encode_image(img) ``` ### Notes 1. **Embedding Size**: The output dimension is `768`, which matches the BERT_like architecture used in `openai/clip-vit-base-patch32`. 2. **CLS Token**: We use the first token (`0`) from the last hidden state, which serves as a global representation of the image _ analogous to the CLS token in language models. 3. **Precision**: The model is loaded with `float16` precision for efficient inference on modern GPUs; if you prefer full precision or CPU usage, set `torch_dtype=torch.float32` and `device='cpu'`. 4. **Tokenizer**: No tokenizer is needed because the visual encoder works directly on image tensors. --- ## 5. 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