1010happy/BALANCED_claude_max_max7_perblock35-Qwen2-5-3B-Instruct-seed88888888

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_claude_max_max7_perblock35-Qwen2-5-3B-Instruct-seed88888888 is a 3.1 billion parameter instruction-tuned causal language model based on the Qwen2.5 architecture. This model is designed for general-purpose conversational AI and instruction following, leveraging its compact size for efficient deployment. It aims to provide balanced performance across various natural language understanding and generation tasks. Its primary use case is as a foundational model for applications requiring responsive and coherent text generation.

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Model Overview

This model, named 1010happy/BALANCED_claude_max_max7_perblock35-Qwen2-5-3B-Instruct-seed88888888, is a 3.1 billion parameter instruction-tuned language model. It is built upon the Qwen2.5 architecture, indicating its foundation in a robust and efficient design. The model is intended for general-purpose applications requiring instruction following and conversational capabilities.

Key Characteristics

  • Parameter Count: 3.1 billion parameters, offering a balance between performance and computational efficiency.
  • Context Length: Supports a context length of 32,768 tokens, enabling processing of longer inputs and generating more extensive responses.
  • Instruction-Tuned: Optimized to follow instructions effectively, making it suitable for a wide range of interactive AI tasks.

Intended Use Cases

  • Conversational AI: Ideal for chatbots, virtual assistants, and interactive dialogue systems.
  • Instruction Following: Can be used for tasks where the model needs to adhere to specific user prompts or commands.
  • Text Generation: Capable of generating coherent and contextually relevant text for various applications.

Limitations

As with many language models, users should be aware of potential biases and limitations inherent in the training data. Specific details regarding training data, evaluation metrics, and potential biases are marked as "More Information Needed" in the original model card, suggesting a need for further investigation into its specific performance characteristics and ethical considerations.

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