nibauman/ObjNav-Qwen3.5-4B-SFT-claude
The nibauman/ObjNav-Qwen3.5-4B-SFT-claude model is a 4.5 billion parameter language model, finetuned by nibauman from Qwen/Qwen3.5-4B. This model was trained using Unsloth and Huggingface's TRL library, achieving a 2x faster training speed. With a context length of 32768 tokens, it is optimized for specific tasks related to object navigation, leveraging its efficient training methodology.
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Model Overview
nibauman/ObjNav-Qwen3.5-4B-SFT-claude is a 4.5 billion parameter language model, finetuned by nibauman from the base Qwen/Qwen3.5-4B architecture. This model distinguishes itself through its efficient training process, having been trained 2x faster using the Unsloth library in conjunction with Huggingface's TRL library. It features a substantial context length of 32768 tokens, making it suitable for processing longer sequences of information.
Key Characteristics
- Base Model: Finetuned from Qwen/Qwen3.5-4B.
- Parameter Count: 4.5 billion parameters.
- Context Length: Supports a context window of 32768 tokens.
- Training Efficiency: Achieved 2x faster training speeds through the use of Unsloth and Huggingface's TRL library.
Potential Use Cases
This model is particularly well-suited for applications requiring efficient processing and understanding of long contexts, especially in domains where its finetuning for object navigation (ObjNav) can be leveraged. Its optimized training process suggests potential for rapid iteration and deployment in specific research or development scenarios.