Aniket200325/Qwen-2.5-7B-Coder-Merged

TEXT GENERATIONPricing:Input $0.4 / Cached $0.08 / Output $0.8Concurrent Unit Cost:1Model Size:7.6BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Jul 25, 2026Architecture:Transformer Featherless Exclusive Cold

Aniket200325/Qwen-2.5-7B-Coder-Merged is a 7.6 billion parameter language model based on the Qwen 2.5 architecture. This model is a merged variant, indicating potential enhancements or specialized fine-tuning for coding tasks. It is designed to leverage its 32768 token context length for processing and generating extensive code sequences. The model's primary strength lies in its application to code-related use cases, benefiting from its foundational architecture and merged configuration.

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Overview

Aniket200325/Qwen-2.5-7B-Coder-Merged is a 7.6 billion parameter language model built upon the Qwen 2.5 architecture. This model is presented as a merged version, suggesting it incorporates various optimizations or fine-tunings, likely aimed at enhancing its performance in specific domains. It features a substantial context window of 32768 tokens, enabling it to handle and process large volumes of text or code effectively.

Key Characteristics

  • Model Architecture: Based on the Qwen 2.5 family, known for its strong performance across various language tasks.
  • Parameter Count: 7.6 billion parameters, offering a balance between capability and computational efficiency.
  • Context Length: A significant 32768 tokens, which is beneficial for tasks requiring extensive contextual understanding, such as long-form code generation or complex problem-solving.
  • Merged Variant: The "Coder-Merged" designation implies specialized training or merging techniques focused on coding capabilities, although specific details are not provided in the model card.

Potential Use Cases

Given its architecture and implied specialization, this model is likely well-suited for:

  • Code Generation: Creating new code snippets, functions, or entire programs.
  • Code Completion: Assisting developers by suggesting relevant code as they type.
  • Code Refactoring: Improving existing code structures and efficiency.
  • Debugging Assistance: Identifying potential errors or suggesting fixes in code.
  • Technical Documentation: Generating explanations or documentation for codebases.

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