stack([batch_data[i+1:i+context_window+1] for i in ix]).long() return x, y MASTER_CONFIG.update({ 'batch_size': 8, 'context_window': 16 }) xs, ys = get_batches(dataset, 'train', MASTER_CONFIG['batch_size'], MASTER_CONFIG['context_window']) [(decode(xs[i].tolist()),...
Once the dataset is prepared, we need to ensure that the data is structured correctly to be used by the model. For this, we apply the appropriate chat template ( I have used the Llama-3.1 format.) using theget_chat_templatefunction. This function basically prepares the tokenizer with the ...
DatasetBLEU@4METEORROUGECIDEr YouCook28.815.937.3116.4 MSRVTT49.832.266.365.3 MSVD70.446.483.2154.2 We also release weights for the fine-tunedVASTViT-L model:weights. Get Started Set Up an Environment conda create python=3.8 -y -n howtocaption conda activate howtocaption conda install -y pytor...
In Azure AI Foundry portal, you're able to log, view, and analyze detailed evaluation metrics.In this article, you learn to create an evaluation run against model, a test dataset or a flow with built-in evaluation metrics from Azure AI Foundry UI. For greater flexibility, you can ...
This process can be iterative, but you’ll want to map each element (nodes and relationships in the graph data model) to your dataset. As each element is defined, Aura Workspace places a green check mark to show that the fields for the node or relationship were populated. ...
Next, we generate the embeddings using OpenAIEmbeddings and save them in a DeepLake vector store hosted in the cloud. Ideally, in a production environment, we could upload an entire website or course lesson to a DeepLake dataset, enabling searches across thousands or even millions of documents...
Data preparation is a crucial step for the entire integration process: Custom Data Set: If you need to improve the system’s accuracy with a custom dataset, prepare your data by splitting it into training and validation sets. Make sure your data is in the proper format for input and output...
Pipeline 2: Creating and populating a Deep Lake vector store with the first batch of documents while the Pipeline 1 team continues to retrieve and prepare the documents. Pipeline 3: Indexed-based RAG with LlamaIndex’s integrated OpenAI LLM performed on the first batch of vectorized documents....
We believe that models like Meta’s Llama 3.1, particularly the 405B model, have reached a frontier class comparable to GPT-4. Additional observations include: Primary use cases: Over the last 12 months, the most tangible use cases for generative AI have emerged in customer service and softwar...
Dolphin Llama 3 is a state-of-the-art LLM specifically designed for offline use. It is trained on an extensive dataset of 15 trillion tokens, equivalent to reading all of Wikipedia 2,500 times, allowing it to perform a wide range of tasks with remarkable accuracy. The model is available ...