nibauman/ObjNav-Qwen3.5-4B-SFT
VISIONConcurrency Cost:1Model Size:4.5BQuant:BF16Ctx Length:32kTool Calling:SupportedPublished:Apr 3, 2026License:apache-2.0Architecture:Transformer0.0K Open Weights Cold
The nibauman/ObjNav-Qwen3.5-4B-SFT is a 4.5 billion parameter Qwen3.5-based causal language model developed by nibauman. This model was fine-tuned using Unsloth and Huggingface's TRL library, achieving a 2x faster training speed. It is designed for general language tasks, leveraging its efficient training methodology.
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Model Overview
The nibauman/ObjNav-Qwen3.5-4B-SFT is a 4.5 billion parameter language model, fine-tuned by nibauman. It is based on the Qwen3.5-4B architecture and was developed with a focus on efficient training.
Key Characteristics
- Base Model: Qwen3.5-4B, a robust foundation for various language understanding and generation tasks.
- Efficient Fine-tuning: The model was fine-tuned using Unsloth and Huggingface's TRL library, resulting in a 2x faster training process compared to standard methods.
- Parameter Count: With 4.5 billion parameters, it offers a balance between performance and computational efficiency.
Good For
- General Language Tasks: Suitable for a wide range of applications requiring text generation, comprehension, and instruction following.
- Resource-Efficient Deployment: Its optimized training suggests potential for more efficient inference, making it a candidate for scenarios where computational resources are a consideration.
- Experimentation with Efficient Fine-tuning: Developers interested in models trained with Unsloth's speed benefits may find this model particularly relevant.