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2. Llama (language model) - Wikipedia

   en.wikipedia.org/wiki/Llama_(language_model)

   Starting with the foundation models from LLaMa 2, Meta AI would train an additional 500B tokens of code datasets, before an additional 20B token of long-context data, creating the Code Llama foundation models. This foundation model was further trained on 5B instruction following token to create the instruct fine-tune.

3. Transformer (deep learning architecture) - Wikipedia

   en.wikipedia.org/wiki/Transformer_(deep_learning...

   For many years, sequence modelling and generation was done by using plain recurrent neural networks (RNNs). A well-cited early example was the Elman network (1990). In theory, the information from one token can propagate arbitrarily far down the sequence, but in practice the vanishing-gradient problem leaves the model's state at the end of a long sentence without precise, extractable ...

4. Generative artificial intelligence - Wikipedia

   en.wikipedia.org/wiki/Generative_artificial...

   If AI-generated content is included in new data crawls from the Internet for additional training of AI models, defects in the resulting models may occur. [185] Training an AI model exclusively on the output of another AI model produces a lower-quality model.

5. Generative pre-trained transformer - Wikipedia

   en.wikipedia.org/wiki/Generative_pre-trained...

   Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.

6. Reliable ‘reasoning’ AI agents may be just around the corner ...

   www.aol.com/finance/reliable-reasoning-ai-agents...

   DeepSeek astonished the sector two weeks ago by releasing a reasoning model called R1 that could match o1’s performance in many tasks, despite the fact that it cost a fraction as much to train.

7. Training, validation, and test data sets - Wikipedia

   en.wikipedia.org/wiki/Training,_validation,_and...

   A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]