Srishtik/Qwen3-0.6B-linear-3-adapters-merged-new
TEXT GENERATIONConcurrent Unit Cost:1Model Size:0.8BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Jun 18, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
Srishtik/Qwen3-0.6B-linear-3-adapters-merged-new is a 0.8 billion parameter Qwen3 model developed by Srishtik. This model was fine-tuned from unsloth/Qwen3-0.6B and optimized for faster training using Unsloth. It is designed for general language tasks, leveraging its efficient training methodology to provide a capable small-scale language model.
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Srishtik/Qwen3-0.6B-linear-3-adapters-merged-new Overview
This model, developed by Srishtik, is a Qwen3-based language model with approximately 0.8 billion parameters. It was fine-tuned from the unsloth/Qwen3-0.6B base model, leveraging the Unsloth library for significantly faster training. Unsloth is known for its efficiency in fine-tuning large language models, enabling quicker iteration and deployment.
Key Capabilities
- Efficiently Trained: Benefits from Unsloth's optimizations, resulting in 2x faster training compared to standard methods.
- Qwen3 Architecture: Built upon the robust Qwen3 model family, providing a solid foundation for various natural language processing tasks.
- Compact Size: With 0.8 billion parameters, it offers a balance between performance and computational resource requirements, making it suitable for applications where larger models might be impractical.
Good For
- Developers seeking a small, efficient Qwen3 model for rapid prototyping and deployment.
- Use cases requiring a capable language model that can be fine-tuned quickly on custom datasets.
- Applications where computational efficiency and faster training cycles are critical considerations.