1010happy/BALANCED_Teacher_r14_train_gptmini_all7-Qwen2-5-1-5B-seed896

TEXT GENERATIONPricing:Input $0.04 / Cached $0.008 / Output $0.08Concurrent Unit Cost:1Model Size:1.5BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Aug 8, 2026Architecture:Transformer Featherless Exclusive Cold

The 1010happy/BALANCED_Teacher_r14_train_gptmini_all7-Qwen2-5-1-5B-seed896 is a 1.5 billion parameter language model based on the Qwen2 architecture. This model is part of a series developed by 1010happy, likely exploring different training configurations or seeds. With a substantial 32768 token context length, it is designed for processing extensive textual inputs. Its specific differentiators and primary use cases are not detailed in the provided information, suggesting it may be a foundational or experimental model.

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Model Overview

This model, 1010happy/BALANCED_Teacher_r14_train_gptmini_all7-Qwen2-5-1-5B-seed896, is a 1.5 billion parameter language model. It is built upon the Qwen2 architecture, indicating a robust foundation for general language understanding and generation tasks. The model features a significant context window of 32768 tokens, allowing it to process and generate responses based on very long inputs.

Key Characteristics

  • Architecture: Qwen2-based, a modern and capable transformer architecture.
  • Parameter Count: 1.5 billion parameters, placing it in the smaller, more efficient category of LLMs.
  • Context Length: Supports an extended context of 32768 tokens, beneficial for tasks requiring extensive memory or long-form content processing.
  • Developer: Developed by 1010happy, as indicated by the model name prefix.

Intended Use Cases

While specific direct or downstream uses are not detailed in the provided model card, its characteristics suggest suitability for:

  • Research and Experimentation: Given the seed896 in its name, it likely represents a specific training run or experimental variant.
  • Long-form Text Processing: The large context window makes it suitable for summarization, question answering, or analysis of lengthy documents.
  • General Language Tasks: As a Qwen2-based model, it can be expected to perform well on a variety of natural language understanding and generation tasks, though specific fine-tuning details are not provided.

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