Everyday-Language-3B

Everyday-Language-3B is a language model fine-tuned for generating natural, everyday English text. It builds upon a pre-trained 3 billion parameter base model (Llama-3.2-3B) and has been further trained on the Everyday-Language-Corpus dataset, a collection of over 8,700 examples of common phrases, questions, and statements encountered in daily interactions.

This fine-tuning process significantly improves the model's ability to produce coherent, contextually appropriate, and less repetitive text compared to its base version. It aims to better capture the nuances and patterns of typical conversational language.

Intended Uses & Limitations

Intended Uses:

• Generating natural language responses in conversational AI applications.
• Creating more human-like text for creative writing or content generation.
• Exploring the capabilities of language models in understanding and producing everyday language.
• Serving as a foundation for further fine-tuning on specific downstream tasks.

Limitations:

• Contextual Understanding: While improved, the model's contextual understanding is still limited by the size of its context window and the inherent complexities of language.
• Potential Biases: Like all language models, Everyday-Language-3B may inherit biases from its pre-training data and the fine-tuning dataset. These biases can manifest in the generated text, potentially leading to outputs that reflect societal stereotypes or unfair assumptions.
• Factuality: The model may generate text that is not factually accurate, especially when dealing with complex or nuanced topics. It's crucial to verify information generated by the model before relying on it.
• Repetition: Although significantly reduced due to fine-tuning, the model may still exhibit some repetition in longer generated text.
• Creativity: The model demonstrates limited creativity in generating text. While it can produce coherent and contextually appropriate responses in factual or informational domains, it struggles with tasks that require imagination, originality, and nuanced storytelling. It tends to produce predictable outputs and may have difficulty generating text that deviates significantly from patterns present in its training data. This limitation makes it less suitable for applications such as creative writing, poetry generation, or other tasks that demand a high degree of imaginative output.

Training Data

Everyday-Language-3B was fine-tuned on the Everyday-Language-Corpus dataset, which is publicly available on Hugging Face:

• Dataset: MultivexAI/Everyday-Language-Corpus
• Dataset Description: A collection of 8,787 synthetically generated examples of everyday English, structured as [S] {Sentence or Sentences} [E].
• Dataset Focus: Common phrases, questions, and statements used in typical daily interactions.

Final loss: 1.143400 after 3 Epochs