EYEDOL/adtc-agri-sft-qwen2.5-1.5b

TEXT GENERATIONPricing:Input $0.04 / Cached $0.008 / Output $0.08Concurrent Unit Cost:1Model Size:1.5BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Aug 20, 2026Architecture:Transformer Featherless Exclusive Cold

EYEDOL/adtc-agri-sft-qwen2.5-1.5b is a 1.5 billion parameter language model based on the Qwen2.5 architecture, featuring a context length of 32768 tokens. This model is shared on the Hugging Face Hub, though specific development details, training data, and intended use cases are not explicitly provided in its current model card. It is designed for general language tasks, with its specific agricultural fine-tuning (sft-agri) suggesting potential specialization in agricultural domain applications.

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Model Overview

This model, EYEDOL/adtc-agri-sft-qwen2.5-1.5b, is a 1.5 billion parameter language model built upon the Qwen2.5 architecture. It supports a substantial context length of 32768 tokens, indicating its capability to process and generate longer sequences of text. The model is hosted on the Hugging Face Hub, making it accessible for various natural language processing tasks.

Key Characteristics

  • Architecture: Based on the Qwen2.5 model family.
  • Parameter Count: 1.5 billion parameters, offering a balance between performance and computational efficiency.
  • Context Length: Features a 32768-token context window, suitable for handling extensive textual inputs.
  • Domain Hint: The sft-agri in its name suggests it has undergone supervised fine-tuning (SFT) with an emphasis on agricultural data, potentially making it proficient in agricultural-specific language understanding and generation.

Intended Use Cases

While the model card does not explicitly detail specific use cases, its characteristics suggest it could be beneficial for:

  • General Text Generation: Capable of generating coherent and contextually relevant text.
  • Text Understanding: Processing and interpreting long documents due to its large context window.
  • Agricultural Applications: Potentially excels in tasks related to agricultural data, research, or communication, given its implied fine-tuning.

Further details regarding its development, training data, and specific performance metrics are marked as "More Information Needed" in its current model card.

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