ishikaa/influence_metamath_qwen2.5-3b_repeat_regularized_1k_scaled
The ishikaa/influence_metamath_qwen2.5-3b_repeat_regularized_1k_scaled is a 3.1 billion parameter language model based on the Qwen2.5 architecture. This model is designed for general language understanding and generation tasks, leveraging its moderate parameter count for efficient deployment. It is suitable for applications requiring a balance between performance and computational resources, offering capabilities for various text-based applications.
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Model Overview
The ishikaa/influence_metamath_qwen2.5-3b_repeat_regularized_1k_scaled is a 3.1 billion parameter language model built upon the Qwen2.5 architecture. This model is provided as a Hugging Face Transformers model, automatically generated and pushed to the Hub. While specific details regarding its development, funding, and fine-tuning are not explicitly provided in the model card, its architecture suggests a foundation in general-purpose language understanding and generation.
Key Characteristics
- Model Type: Based on the Qwen2.5 architecture.
- Parameter Count: 3.1 billion parameters, offering a balance between capability and computational efficiency.
- Context Length: Supports a context length of 32768 tokens, enabling processing of relatively long inputs.
Potential Use Cases
Given its general-purpose nature and moderate size, this model could be suitable for a variety of applications where a balance of performance and resource efficiency is desired. These may include:
- Text generation and completion.
- Basic question answering.
- Summarization of short to medium-length texts.
- Prototyping and development of language-based applications.