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@@ -11,11 +11,15 @@ reasoning tasks. The objective is to enable the model to interpret natural langu
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  spatial relationships, directions, and locations and output actionable responses.
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  The task addresses limitations in current LLMs, which often fail to perform precise spatial reasoning,
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  such as determining relationships between points on a map, planning routes, or identifying locations
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- based on bounding boxes. For this fine-tuning task the step-game dataset was used.
 
 
 
 
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  This dataset follows the template of story, question and answer as depicted below.
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- # Training Data
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  # Training Method
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  spatial relationships, directions, and locations and output actionable responses.
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  The task addresses limitations in current LLMs, which often fail to perform precise spatial reasoning,
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  such as determining relationships between points on a map, planning routes, or identifying locations
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+ based on bounding boxes.
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+
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+ # Training Data
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+
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+ For this fine-tuning task the step-game dataset was used.
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  This dataset follows the template of story, question and answer as depicted below.
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+ ![Description of StepGame Training Data](https://huggingface.co/sareena/spatial_lora_mistral/blob/main/stepgame.jpg)
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  # Training Method
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