1010happy/BALANCED_Teacher_r14_train_gptmini-Qwen2-5-3B-Instruct-seed51485

TEXT GENERATIONPricing:Input $0.32 / Cached $0.064 / Output $1.6Concurrent Unit Cost:1Model Size:3.1BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Aug 8, 2026Architecture:Transformer Featherless Exclusive Cold

The 1010happy/BALANCED_Teacher_r14_train_gptmini-Qwen2-5-3B-Instruct-seed51485 is a 3.1 billion parameter instruction-tuned causal language model based on the Qwen2-5-3B-Instruct architecture. This model is designed for general language understanding and generation tasks, leveraging its instruction-following capabilities. Its primary application is in scenarios requiring a compact yet capable model for various NLP applications.

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Model Overview

This model, 1010happy/BALANCED_Teacher_r14_train_gptmini-Qwen2-5-3B-Instruct-seed51485, is an instruction-tuned causal language model with approximately 3.1 billion parameters. It is built upon the Qwen2-5-3B-Instruct architecture, indicating its foundation in the Qwen series of models known for their strong performance in various language tasks.

Key Characteristics

  • Architecture: Based on the Qwen2-5-3B-Instruct family.
  • Parameter Count: Features 3.1 billion parameters, offering a balance between performance and computational efficiency.
  • Context Length: Supports a substantial context window of 32768 tokens, enabling processing of longer inputs and generating coherent, extended outputs.

Intended Use Cases

Given its instruction-tuned nature and moderate parameter count, this model is suitable for a range of general-purpose natural language processing tasks where a balance of capability and resource efficiency is desired. Potential applications include:

  • Instruction following and response generation.
  • Text summarization and question answering.
  • Content creation and dialogue systems.

Limitations

The provided model card indicates that specific details regarding its development, training data, evaluation, and potential biases or risks are currently marked as "More Information Needed." Users should exercise caution and conduct their own evaluations before deploying the model in critical applications, as its full characteristics and limitations are not yet comprehensively documented.

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