1010happy/BALANCED_Teacher_r14_train_gptmini_all7-Qwen2-5-3B-Instruct-seed896

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_all7-Qwen2-5-3B-Instruct-seed896 is a 3.1 billion parameter instruction-tuned language model based on the Qwen2 architecture, featuring a substantial 32768 token context length. This model is designed for general language understanding and generation tasks, leveraging its instruction-tuned nature to follow diverse prompts effectively. Its large context window makes it suitable for applications requiring extensive input processing and coherent long-form responses. The model's primary strength lies in its ability to handle complex instructions and generate relevant text across various domains.

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Model Overview

This model, named 1010happy/BALANCED_Teacher_r14_train_gptmini_all7-Qwen2-5-3B-Instruct-seed896, is an instruction-tuned language model built upon the Qwen2 architecture. It features approximately 3.1 billion parameters and supports a significant context length of 32768 tokens, enabling it to process and generate extensive text sequences. The model is designed to understand and respond to a wide array of instructions, making it versatile for various natural language processing tasks.

Key Characteristics

  • Architecture: Based on the Qwen2 model family.
  • Parameter Count: 3.1 billion parameters, offering a balance between performance and computational efficiency.
  • Context Length: Supports a 32768-token context window, beneficial for tasks requiring long-range dependencies or detailed information processing.
  • Instruction-Tuned: Optimized to follow human instructions and generate appropriate responses.

Potential Use Cases

Given its instruction-following capabilities and large context window, this model is well-suited for:

  • General Text Generation: Creating diverse content based on prompts.
  • Question Answering: Providing detailed answers from extensive documents.
  • Summarization: Condensing long texts while retaining key information.
  • Conversational AI: Engaging in extended dialogues with context retention.

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