arefehRajabian/qwen3_4b_SFT_16bit
TEXT GENERATIONPricing:Input $0.4 / Cached $0.08 / Output $0.8Concurrent Unit Cost:1Model Size:4BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Oct 6, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
The arefehRajabian/qwen3_4b_SFT_16bit is a 4 billion parameter Qwen3 model, developed by arefehRajabian, that has been fine-tuned using Unsloth and Huggingface's TRL library. This fine-tuning process enabled 2x faster training compared to standard methods. It is designed for general language generation tasks, leveraging its Qwen3 architecture and efficient training to provide a capable base for various applications.
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Overview
The arefehRajabian/qwen3_4b_SFT_16bit is a 4 billion parameter language model, developed by arefehRajabian, based on the Qwen3 architecture. This model was fine-tuned from the unsloth/Qwen3-4B-Instruct-2507-unsloth-bnb-4bit base model, utilizing the Unsloth framework and Huggingface's TRL library.
Key Capabilities
- Efficient Training: Achieved 2x faster training speeds due to the integration of Unsloth, making the fine-tuning process highly optimized.
- Qwen3 Architecture: Benefits from the robust capabilities of the Qwen3 model family, providing a strong foundation for various natural language processing tasks.
- Instruction Following: As an instruction-tuned model, it is designed to understand and execute user prompts effectively.
Good For
- General Language Generation: Suitable for a wide range of text generation applications, including content creation, summarization, and conversational AI.
- Research and Development: Provides a fine-tuned Qwen3 model that can serve as a base for further experimentation and specialized applications.
- Resource-Efficient Deployment: Its 4 billion parameter size makes it a viable option for scenarios where computational resources are a consideration, especially given its optimized training.