MadhuryaPasan/qwen3-1.7_expert_tools_v0_1
TEXT GENERATIONConcurrency Cost:1Model Size:2BQuant:BF16Ctx Length:32kPublished:Mar 16, 2026License:apache-2.0Architecture:Transformer Open Weights Warm
The MadhuryaPasan/qwen3-1.7_expert_tools_v0_1 is a 2 billion parameter Qwen3-based causal language model developed by MadhuryaPasan. It was fine-tuned using Unsloth and Huggingface's TRL library, enabling 2x faster training. This model is optimized for specific expert tool-use applications, leveraging its efficient training for focused performance.
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Model Overview
The MadhuryaPasan/qwen3-1.7_expert_tools_v0_1 is a 2 billion parameter model based on the Qwen3 architecture. Developed by MadhuryaPasan, this model was fine-tuned from unsloth/qwen3-1.7b-unsloth-bnb-4bit using the Unsloth library and Huggingface's TRL. A key characteristic of its development is the reported 2x faster training speed achieved through these optimizations.
Key Capabilities
- Efficient Fine-tuning: Leverages Unsloth for significantly faster training, making it efficient for specialized applications.
- Qwen3 Architecture: Built upon the Qwen3 base model, providing a robust foundation for language understanding and generation.
- Tool-Use Focus: The model name suggests an optimization for expert tool-use scenarios, indicating its potential in applications requiring interaction with external tools or APIs.
Good For
- Specialized Tool-Use Applications: Ideal for use cases where the model needs to interact with or utilize specific tools or functions.
- Resource-Efficient Deployment: Its 2 billion parameter size, combined with efficient training, makes it suitable for scenarios requiring a balance of performance and computational resources.
- Rapid Prototyping: The faster training capability can benefit developers looking to quickly iterate and deploy models for specific tasks.