anuruab/qwen2.5-3b-ncert-finetuned

TEXT GENERATIONPricing:Input $0.32 / Cached $0.064 / Output $1.6Concurrent Unit Cost:1Model Size:3.1BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Aug 31, 2026Architecture:Transformer Featherless Exclusive Cold

The anuruab/qwen2.5-3b-ncert-finetuned model is a 3.1 billion parameter language model based on the Qwen2.5 architecture. This model has been fine-tuned, likely for specific educational content related to NCERT, given its name. It is designed for tasks that benefit from a smaller, specialized model with a substantial 32768 token context length. Its primary strength lies in its fine-tuned nature, suggesting optimized performance for its target domain.

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Model Overview

The anuruab/qwen2.5-3b-ncert-finetuned is a 3.1 billion parameter language model built upon the Qwen2.5 architecture. This model is a fine-tuned variant, indicated by its name, suggesting specialization for content related to NCERT (National Council of Educational Research and Training) materials. It leverages a significant context window of 32768 tokens, allowing it to process and generate longer sequences of text while maintaining coherence.

Key Characteristics

  • Architecture: Based on the Qwen2.5 model family.
  • Parameter Count: 3.1 billion parameters, offering a balance between performance and computational efficiency.
  • Context Length: Supports a substantial 32768 tokens, beneficial for tasks requiring extensive contextual understanding.
  • Specialization: The 'ncert-finetuned' suffix implies a targeted fine-tuning process, likely enhancing its performance and relevance for educational content or queries related to NCERT syllabi.

Potential Use Cases

This model is potentially well-suited for applications requiring:

  • Educational Content Generation: Creating summaries, explanations, or question-answering based on NCERT textbooks.
  • Academic Assistance: Aiding students or educators with queries related to specific subjects covered by NCERT.
  • Specialized Text Processing: Tasks where domain-specific knowledge from NCERT materials is crucial for accurate responses.

Further details regarding its specific training data, evaluation metrics, and intended use cases are not provided in the current model card, suggesting that users should conduct their own evaluations for specific applications.

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