nibauman/ObjNav-Qwen3.5-4B-SFT-gemini
The nibauman/ObjNav-Qwen3.5-4B-SFT-gemini is a 4.5 billion parameter causal language model, fine-tuned by nibauman from the Qwen/Qwen3.5-4B base model. It features a 32,768 token context length and was trained using Unsloth and Huggingface's TRL library for accelerated performance. This model is optimized for specific tasks related to object navigation, leveraging its fine-tuned capabilities for enhanced performance in such applications.
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Model Overview
The nibauman/ObjNav-Qwen3.5-4B-SFT-gemini is a 4.5 billion parameter language model, fine-tuned by nibauman. It is based on the Qwen/Qwen3.5-4B architecture and features a substantial context length of 32,768 tokens, allowing it to process extensive inputs.
Key Characteristics
- Base Model: Fine-tuned from Qwen/Qwen3.5-4B.
- Parameter Count: 4.5 billion parameters.
- Context Length: Supports a 32,768 token context window.
- Training Efficiency: Training was accelerated by 2x using the Unsloth library in conjunction with Huggingface's TRL library.
- License: Distributed under the Apache-2.0 license.
Potential Use Cases
This model is specifically fine-tuned, suggesting its optimization for particular tasks. While the exact nature of "ObjNav" is not detailed in the provided README, the naming implies potential applications in:
- Object Navigation: Tasks requiring understanding and processing information related to object navigation.
- Specialized Language Understanding: Scenarios where a model fine-tuned for specific domain knowledge, potentially related to object interaction or spatial reasoning, would be beneficial.
Developers looking for a Qwen3.5-4B variant with enhanced performance for specialized tasks, particularly those benefiting from accelerated fine-tuning methods, may find this model suitable.