#language-modeling
#language-modeling

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How to Build a Tiny Language Model (TLM) in Ruby: A Step-by-Step Guide | HackerNoon

Creating a language model in Ruby can illustrate core concepts without complex resources of large models.

#speech-translation

Future of Speech Translation with SEAMLESSEXPRESSIVELM | HackerNoon

SEAMLESSEXPRESSIVELM improves expressive speech-to-speech translation by unifying semantic and acoustic modeling.

SEAMLESSEXPRESSIVELM Transforms Speech Translation by Preserving Semantics and Speaker Vocal Style | HackerNoon

SEAMLESSEXPRESSIVELM enhances speech-to-speech translation by integrating both semantics and speaker style efficiently.

Future of Speech Translation with SEAMLESSEXPRESSIVELM | HackerNoon

SEAMLESSEXPRESSIVELM improves expressive speech-to-speech translation by unifying semantic and acoustic modeling.

SEAMLESSEXPRESSIVELM Transforms Speech Translation by Preserving Semantics and Speaker Vocal Style | HackerNoon

SEAMLESSEXPRESSIVELM enhances speech-to-speech translation by integrating both semantics and speaker style efficiently.

morespeech-translation

#machine-learning

Mamba: A New Player in Language Modeling Outperforms Big Names | HackerNoon

Mamba architecture demonstrates competitive performance in language modeling without using attention mechanisms.

How Selective State Space Models Boost Mamba's Performance | HackerNoon

Selective state space models (SSMs) enhance performance significantly compared to traditional models, confirming selection as a key improvement strategy.

Mamba: A New Player in Language Modeling Outperforms Big Names | HackerNoon

Mamba architecture demonstrates competitive performance in language modeling without using attention mechanisms.

How Selective State Space Models Boost Mamba's Performance | HackerNoon

Selective state space models (SSMs) enhance performance significantly compared to traditional models, confirming selection as a key improvement strategy.

moremachine-learning

RWKV Project Open-Sources LLM Eagle 7B

Eagle 7B is an open-sourced large language model trained on 1.1 trillion text tokens in over 100 languages, outperforming other 7B-parameter LLMs.

The RWKV architecture behind Eagle 7B combines the benefits of Transformers and RNNs, boasting an energy-efficient design and no maximum input context length.

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