ahmadmakk/Qwen2.5-Coder-1.5B-Instruct-Gensyn-Swarm-melodic_dappled_caterpillar

TEXT GENERATIONPricing:Input $0.04 / Cached $0.008 / Output $0.08Concurrent Unit Cost:1Model Size:1.5BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Dec 1, 2025Architecture:Transformer Featherless Exclusive Cold

The ahmadmakk/Qwen2.5-Coder-1.5B-Instruct-Gensyn-Swarm-melodic_dappled_caterpillar is a 1.5 billion parameter instruction-tuned language model based on the Qwen2.5 architecture. This model is designed for general language understanding and generation tasks. Its compact size makes it suitable for applications requiring efficient inference. Further details on its specific training and optimization are not provided in the available documentation.

Loading preview...

Model Overview

This model, ahmadmakk/Qwen2.5-Coder-1.5B-Instruct-Gensyn-Swarm-melodic_dappled_caterpillar, is a 1.5 billion parameter instruction-tuned language model. It is built upon the Qwen2.5 architecture, indicating a foundation in a robust and capable base model. The model is designed to follow instructions and generate human-like text, making it versatile for various natural language processing tasks.

Key Characteristics

  • Parameter Count: 1.5 billion parameters, offering a balance between performance and computational efficiency.
  • Architecture: Based on the Qwen2.5 family, known for its strong general-purpose language capabilities.
  • Instruction-Tuned: Optimized to understand and respond to user instructions, enhancing its usability for interactive applications.
  • Context Length: Supports a context window of 32768 tokens, allowing it to process and generate longer sequences of text.

Potential Use Cases

Given its instruction-tuned nature and moderate size, this model could be suitable for:

  • Text Generation: Creating various forms of content, from creative writing to summaries.
  • Question Answering: Responding to queries based on provided context or general knowledge.
  • Code Assistance: While named "Coder", specific coding capabilities are not detailed, but instruction-tuning often benefits code-related tasks.
  • Chatbots and Conversational AI: Engaging in dialogue and following user prompts.

Further details regarding its specific training data, performance benchmarks, and intended applications are not available in the current model card.

Finetunes
1 models