kubilaygulacdi/qwen3-4b-ext-01
TEXT GENERATIONConcurrent Unit Cost:1Model Size:4BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Jul 7, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
The kubilaygulacdi/qwen3-4b-ext-01 is a 4 billion parameter Qwen3-based causal language model developed by kubilaygulacdi. It was finetuned from unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit using Unsloth and Huggingface's TRL library, enabling 2x faster training. This model is optimized for efficient deployment and performance due to its accelerated training methodology.
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Model Overview
The kubilaygulacdi/qwen3-4b-ext-01 is a 4 billion parameter language model based on the Qwen3 architecture. Developed by kubilaygulacdi, this model is a finetuned version of unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit.
Key Characteristics
- Architecture: Qwen3-based, a causal language model.
- Parameter Count: 4 billion parameters, offering a balance between performance and computational efficiency.
- Training Efficiency: The model was trained with Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process compared to standard methods.
- Context Length: Supports a context length of 32768 tokens, allowing for processing longer inputs.
Good For
- Applications requiring efficient training: Ideal for developers looking to quickly iterate and finetune models due to its Unsloth-accelerated training.
- Resource-constrained environments: The 4 billion parameter size makes it suitable for deployment where computational resources are limited.
- Instruction-following tasks: As it is finetuned from an instruction-tuned model, it is likely well-suited for various instruction-based applications.