Update README.md

README.md

@@ -63,7 +63,8 @@ fine-tuning, making it ideal for working with limited GPU resources. LoRA is esp
 adaptation when the dataset is moderately sized and instruction formatting is consistent as in the case of this dataset of stepGame. 
 In previous experiments with spatial reasoning fine-tuning, LoRA performed better than prompt tuning. While prompt tuning resulted in close to 0% accuracy on both the StepGame and
 MMLU evaluations, LoRA preserved partial task performance (18% accuracy) and retained some general knowledge ability (46% accuracy on 
-MMLU geography vs. 52% before training).
+MMLU geography vs. 52% before training). I used a learning rate of 2e-4, batch size of 8, and trained for 2 epochs. 
+This setup preserved general reasoning ability while improving spatial accuracy.
 
 # Evaluation
 
@@ -71,6 +72,19 @@ MMLU geography vs. 52% before training).
 
 # Usage and Intended Uses
 
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model = AutoModelForCausalLM.from_pretrained("sareena/spatial_lora_mistral")
+tokenizer = AutoTokenizer.from_pretrained("sareena/spatial_lora_mistral")
+
+inputs = tokenizer("Q: The couch is to the left of the table. The lamp is on the couch. Where is the lamp?", return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=50)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+
+```
+
+
 # Prompt Format
 
 # Expected Output Format