Nsnssnnaj7/codepal-qwen-1.5b

Hugging Face
TEXT GENERATIONPricing:Input $0.04 / Cached $0.008 / Output $0.08Concurrent Unit Cost:1Model Size:1.5BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Mar 22, 2026Architecture:Transformer Featherless Exclusive Warm

Nsnssnnaj7/codepal-qwen-1.5b is a 1.5 billion parameter language model with a 32768 token context length. This model is part of the Qwen family, developed by Nsnssnnaj7. While specific training details are not provided, its name suggests an optimization for code-related tasks, aiming to serve as a foundational model for code generation and understanding applications.

Loading preview...

Model Overview

Nsnssnnaj7/codepal-qwen-1.5b is a 1.5 billion parameter language model, likely based on the Qwen architecture, developed by Nsnssnnaj7. It features a substantial context length of 32768 tokens, indicating its capability to process and generate longer sequences of text or code. The "codepal" designation suggests a specialization in programming-related tasks, positioning it as a potential tool for developers.

Key Capabilities

  • Large Context Window: With a 32768 token context length, the model can handle extensive codebases or complex problem descriptions, which is beneficial for understanding and generating coherent, context-aware code.
  • Code-Oriented Design: The model's name implies an optimization for code generation, completion, and potentially debugging or explanation tasks, making it suitable for various software development workflows.

Good For

  • Code Generation: Assisting developers in writing new code snippets or entire functions.
  • Code Completion: Providing intelligent suggestions during coding to improve efficiency.
  • Contextual Understanding: Analyzing and interpreting large blocks of code or technical documentation due to its extended context window.

Limitations

As per the provided model card, specific details regarding training data, evaluation metrics, biases, risks, and intended use cases are currently marked as "More Information Needed." Users should exercise caution and conduct thorough testing for their specific applications until more comprehensive documentation becomes available.