saketh1201/Qwen3-4B-Inventory-SFT
TEXT GENERATIONConcurrency Cost:1Model Size:4BQuant:BF16Ctx Length:32kPublished:Apr 25, 2026License:apache-2.0Architecture:Transformer Open Weights Cold
The saketh1201/Qwen3-4B-Inventory-SFT is a 4 billion parameter Qwen3 model, developed by saketh1201, fine-tuned for specific tasks. This model was optimized for training speed using Unsloth and Huggingface's TRL library, making it efficient for deployment. It is designed for applications requiring a compact yet capable language model with a 32768 token context length.
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Model Overview
The saketh1201/Qwen3-4B-Inventory-SFT is a 4 billion parameter language model, developed by saketh1201. It is built upon the Qwen3 architecture and has been specifically fine-tuned from the unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit base model.
Key Characteristics
- Efficient Training: This model was trained significantly faster, achieving 2x speed improvements, by leveraging Unsloth and Huggingface's TRL library. This indicates an optimization for resource-efficient fine-tuning processes.
- Base Model: It originates from a Qwen3-4B-Instruct variant, suggesting a foundation in instruction-following capabilities.
- License: The model is released under the Apache-2.0 license, allowing for broad usage and distribution.
Use Cases
This model is particularly well-suited for scenarios where:
- Resource Efficiency is Key: Its optimized training process makes it a good candidate for environments with limited computational resources or for rapid iteration on fine-tuning tasks.
- Instruction-Following Tasks: Given its base as an instruction-tuned model, it can be applied to various tasks requiring adherence to specific prompts or instructions.
- Compact Deployment: With 4 billion parameters, it offers a balance between performance and a smaller footprint compared to larger models, making it suitable for edge devices or applications where latency is critical.