wexhi/Qwen3-4B-TIR
TEXT GENERATIONConcurrency Cost:1Model Size:4BQuant:BF16Ctx Length:32kPublished:Dec 23, 2025License:apache-2.0Architecture:Transformer Open Weights Warm
wexhi/Qwen3-4B-TIR is a 4 billion parameter Qwen3 model developed by wexhi, fine-tuned from unsloth/qwen3-4b-base-unsloth-bnb-4bit. This model was trained significantly faster using Unsloth and Huggingface's TRL library, making it efficient for deployment. It is designed for general language tasks, leveraging its optimized training process for improved performance.
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Overview
wexhi/Qwen3-4B-TIR is a 4 billion parameter Qwen3 model, developed by wexhi. It is a fine-tuned version of the unsloth/qwen3-4b-base-unsloth-bnb-4bit base model, indicating its foundation in the Qwen3 architecture.
Key Capabilities
- Optimized Training: This model was trained with a focus on speed, utilizing Unsloth and Huggingface's TRL library to achieve 2x faster training times.
- Efficient Deployment: The use of Unsloth suggests potential benefits in terms of memory efficiency and inference speed, making it suitable for resource-constrained environments.
- General Language Understanding: As a Qwen3 variant, it is expected to perform well across a range of natural language processing tasks.
Good For
- Rapid Prototyping: Its faster training process makes it ideal for quick experimentation and iteration in development cycles.
- Applications requiring efficient models: The optimization from Unsloth can be beneficial for deployment where computational resources are a concern.
- Tasks leveraging Qwen3's strengths: Suitable for various text generation, summarization, and question-answering tasks where the Qwen3 architecture excels.