Ashish-kharde1/Qwen3-Micro-Reasoner
TEXT GENERATIONConcurrency Cost:1Model Size:4BQuant:BF16Ctx Length:32kPublished:Dec 11, 2025License:apache-2.0Architecture:Transformer Open Weights Warm
Ashish-kharde1/Qwen3-Micro-Reasoner is a 4 billion parameter Qwen3 model developed by Ashish-kharde1, fine-tuned from unsloth/qwen3-4b-unsloth-bnb-4bit. This model was trained using Unsloth and Huggingface's TRL library, achieving 2x faster training. It is designed for general language tasks, leveraging its efficient training methodology.
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Ashish-kharde1/Qwen3-Micro-Reasoner Overview
This model is a 4 billion parameter Qwen3 variant, developed by Ashish-kharde1. It was fine-tuned from the unsloth/qwen3-4b-unsloth-bnb-4bit base model, indicating an optimization for efficient resource usage and faster training.
Key Characteristics
- Architecture: Based on the Qwen3 model family.
- Parameter Count: 4 billion parameters, offering a balance between performance and computational efficiency.
- Training Efficiency: Leverages Unsloth and Huggingface's TRL library, resulting in a 2x faster training process compared to standard methods.
- License: Distributed under the Apache-2.0 license, allowing for broad use and modification.
Potential Use Cases
- Resource-constrained environments: Its efficient training and moderate parameter count make it suitable for deployment where computational resources are limited.
- Rapid prototyping: The faster training time can accelerate development cycles for various NLP applications.
- General language understanding and generation: Capable of handling a wide range of text-based tasks due to its Qwen3 foundation.
- Further fine-tuning: Serves as a solid base for additional domain-specific fine-tuning, benefiting from its optimized training history.