Update README.md

README.md

@@ -16,9 +16,7 @@ The process is outlined in this [notebook](https://github.com/cognitivecomputati
 
 #### Notes: 
 
-  This dolphin is not suited for code creation tasks, but performs very well on creation and math based tasks. 
-  
-  DPO being used in only a portion of the merge seems to be causing issues with code creation.
+DPO being used in only a portion of the merge seems to be causing issues with code creation.
 
 ## Code Example
 
@@ -47,7 +45,7 @@ def generate_response(prompt):
     return response
 
 # Load the model and tokenizer
-model_id = "macadeliccc/laser-dolphin-mixtral-2x7b-dpo"
+model_id = "macadeliccc/laser-dolphin-mixtral-4x7b-dpo"
 tokenizer = AutoTokenizer.from_pretrained(model_id)
 model = AutoModelForCausalLM.from_pretrained(model_id, load_in_4bit=True)
 
@@ -58,6 +56,29 @@ print("Response:")
 print(generate_response(prompt), "\n")
 ```
 
+## Example output
+
+can you write me a quicksort algorithm in python?
+
+Sure, here's a quicksort algorithm implemented in Python:
+
+```python
+def quicksort(arr):
+    if len(arr) <= 1:
+        return arr
+    pivot = arr[len(arr) // 2]
+    left = [x for x in arr if x < pivot]
+    middle = [x for x in arr if x == pivot]
+    right = [x for x in arr if x > pivot]
+    return quicksort(left) + middle + quicksort(right)
+```
+
+This implementation uses the median of the array as the pivot. 
+It first checks if the array has one or fewer elements, in which case it is already sorted and can be returned as is.
+Otherwise, it selects the pivot as the middle element of the array. Then, it partitions the array into three sub-arrays: elements less than the pivot, elements equal to the pivot, and elements greater than the pivot. 
+It recursively sorts the left and right sub-arrays and concatenates the results with the middle sub-array to obtain the final sorted array. 
+
+
 ## GGUF
 
 Q4_K_M and Q5_K_M quants are available [here](https://huggingface.co/macadeliccc/laser-dolphin-mixtral-4x7b-dpo-GGUF)