nibauman/ObjNav-claude-caveman-SFT
The nibauman/ObjNav-claude-caveman-SFT model is a 4.5 billion parameter language model, finetuned from Qwen/Qwen3.5-4B. Developed by nibauman, this model was trained using Unsloth and Huggingface's TRL library, achieving 2x faster training speeds. Its specific optimization for object navigation tasks is implied by its name, suggesting a focus on spatial reasoning and interaction within environments.
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Model Overview
nibauman/ObjNav-claude-caveman-SFT is a 4.5 billion parameter language model, finetuned by nibauman. It is based on the Qwen3.5-4B architecture and was developed with a focus on efficient training.
Key Characteristics
- Base Model: Finetuned from Qwen/Qwen3.5-4B.
- Training Efficiency: Training was accelerated by 2x using Unsloth and Huggingface's TRL library.
- Parameter Count: Features 4.5 billion parameters.
- Context Length: Supports a context length of 32768 tokens.
Potential Use Cases
Given its name, "ObjNav-claude-caveman-SFT," this model is likely specialized for tasks involving object navigation, spatial understanding, or interaction within simulated or real-world environments. Developers looking for a model with efficient training origins and potential capabilities in object-centric reasoning may find this model suitable.