1010happy/Teacher_r14_train_gptmini-Qwen2-5-3B-Instruct-seed10

TEXT GENERATIONPricing:Input $0.32 / Cached $0.064 / Output $1.6Concurrent Unit Cost:1Model Size:3.1BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Aug 2, 2026Architecture:Transformer Featherless Exclusive Cold

The 1010happy/Teacher_r14_train_gptmini-Qwen2-5-3B-Instruct-seed10 model is a 3.1 billion parameter instruction-tuned causal language model based on the Qwen2.5 architecture. It features a 32768 token context length, making it suitable for processing extensive inputs. This model is designed for general-purpose language understanding and generation tasks, leveraging its instruction-following capabilities. Its primary strength lies in its ability to respond to diverse prompts effectively within its parameter class.

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Model Overview

This model, 1010happy/Teacher_r14_train_gptmini-Qwen2-5-3B-Instruct-seed10, is an instruction-tuned causal language model with approximately 3.1 billion parameters. It is built upon the Qwen2.5 architecture and supports a substantial context length of 32768 tokens, enabling it to handle complex and lengthy inputs.

Key Characteristics

  • Architecture: Based on the Qwen2.5 family, known for its performance in various language tasks.
  • Parameter Count: 3.1 billion parameters, offering a balance between capability and computational efficiency.
  • Context Length: A significant 32768 tokens, allowing for deep contextual understanding and generation.
  • Instruction-Tuned: Designed to follow instructions effectively, making it versatile for a wide range of NLP applications.

Potential Use Cases

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

  • General text generation and completion.
  • Question answering and summarization tasks.
  • Conversational AI and chatbot development.
  • Processing and understanding long documents or dialogues.

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