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  2. Module : Params/doc/examples/check for unknown parameters

    en.wikipedia.org/.../check_for_unknown_parameters

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  3. Neural scaling law - Wikipedia

    en.wikipedia.org/wiki/Neural_scaling_law

    is the number of parameters in the model. D {\displaystyle D} is the number of tokens in the training set. L {\displaystyle L} is the average negative log-likelihood loss per token ( nats /token), achieved by the trained LLM on the test dataset.

  4. T5 (language model) - Wikipedia

    en.wikipedia.org/wiki/T5_(language_model)

    T5 (Text-to-Text Transfer Transformer) is a series of large language models developed by Google AI introduced in 2019. [1] [2] Like the original Transformer model, [3] T5 models are encoder-decoder Transformers, where the encoder processes the input text, and the decoder generates the output text.

  5. Chinchilla (language model) - Wikipedia

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

    Based on the training of previously employed language models, it has been determined that if one doubles the model size, one must also have twice the number of training tokens. This hypothesis has been used to train Chinchilla by DeepMind. Similar to Gopher in terms of cost, Chinchilla has 70B parameters and four times as much data. [3]

  6. Generative pre-trained transformer - Wikipedia

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

    The semi-supervised approach OpenAI employed to make a large-scale generative system—and was first to do with a transformer model—involved two stages: an unsupervised generative "pretraining" stage to set initial parameters using a language modeling objective, and a supervised discriminative "fine-tuning" stage to adapt these parameters to ...

  7. Llama (language model) - Wikipedia

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

    The model was exclusively a foundation model, [6] although the paper contained examples of instruction fine-tuned versions of the model. [ 2 ] Meta AI reported the 13B parameter model performance on most NLP benchmarks exceeded that of the much larger GPT-3 (with 175B parameters), and the largest 65B model was competitive with state of the art ...

  8. Hyperparameter (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Hyperparameter_(machine...

    In machine learning, a hyperparameter is a parameter that can be set in order to define any configurable part of a model's learning process. Hyperparameters can be classified as either model hyperparameters (such as the topology and size of a neural network) or algorithm hyperparameters (such as the learning rate and the batch size of an optimizer).

  9. Identifiability - Wikipedia

    en.wikipedia.org/wiki/Identifiability

    Identifiability of the model in the sense of invertibility of the map is equivalent to being able to learn the model's true parameter if the model can be observed indefinitely long. Indeed, if {X t} ⊆ S is the sequence of observations from the model, then by the strong law of large numbers,

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