divyesh6208/Qwen3.5-2B-finetuned
VISIONPricing:Input $0.32 / Cached $0.064 / Output $1.6Concurrent Unit Cost:1Model Size:2.3BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Oct 6, 2026License:apache-2.0Architecture:Transformer0.0K Open Weights Featherless Exclusive Cold
The divyesh6208/Qwen3.5-2B-finetuned is a 2.3 billion parameter language model developed by divyesh6208, finetuned from unsloth/Qwen3.5-2B. This model was trained using Unsloth and Huggingface's TRL library, achieving 2x faster training speeds. It is designed for general language tasks, leveraging its efficient training methodology to provide a capable model within its parameter class.
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divyesh6208/Qwen3.5-2B-finetuned Overview
This model is a 2.3 billion parameter language model, finetuned by divyesh6208 from the base unsloth/Qwen3.5-2B model. It was developed with a focus on efficient training, utilizing the Unsloth library and Huggingface's TRL library, which enabled a 2x faster training process.
Key Capabilities
- Efficient Training: Benefits from Unsloth's optimizations, leading to significantly reduced training times.
- General Language Understanding: As a finetuned Qwen3.5-2B model, it is capable of handling a variety of natural language processing tasks.
- Compact Size: With 2.3 billion parameters, it offers a balance between performance and computational resource requirements.
Good for
- Resource-constrained environments: Its smaller size makes it suitable for deployment where computational resources are limited.
- Rapid Prototyping: The faster training speed allows for quicker iteration and experimentation with finetuning.
- Applications requiring a capable yet efficient language model: Ideal for tasks where a larger model might be overkill or too resource-intensive.