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

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-stinky_sharp_squid model is a 1.5 billion parameter instruction-tuned language model with a 32768 token context length. This model is part of the Qwen2.5 family, designed for general language understanding and generation tasks. Its instruction-tuned nature makes it suitable for following user prompts and performing various NLP applications.

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Model Overview

This model, ahmadmakk/Qwen2.5-Coder-1.5B-Instruct-Gensyn-Swarm-stinky_sharp_squid, is an instruction-tuned language model with 1.5 billion parameters and a substantial context length of 32768 tokens. It is based on the Qwen2.5 architecture, indicating its foundation in a robust and capable large language model family. The "Instruct" designation signifies that it has been fine-tuned to follow instructions effectively, making it versatile for a range of interactive AI applications.

Key Characteristics

  • Parameter Count: 1.5 billion parameters, offering a balance between performance and computational efficiency.
  • Context Length: A generous 32768 tokens, allowing it to process and generate longer sequences of text while maintaining coherence and understanding.
  • Instruction-Tuned: Optimized to understand and execute user instructions, which is crucial for conversational AI, task automation, and prompt-based generation.

Potential Use Cases

Given its instruction-following capabilities and moderate size, this model is well-suited for:

  • General Text Generation: Creating coherent and contextually relevant text based on prompts.
  • Instruction Following: Executing specific commands or answering questions as directed by the user.
  • Prototyping and Development: A good choice for developers looking for a capable instruction-tuned model that is less resource-intensive than larger alternatives.

Further details regarding its specific training data, performance benchmarks, and intended applications are marked as "More Information Needed" in the original model card.

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