Update README.md

README.md

@@ -5,7 +5,9 @@ license: apache-2.0
 
 ![polyglot](polyglot.png)
 
-This model is a Mixture of Experts approach to a multilingual model.
+Polyglot-4x7b is a Mixture of Experts approach to a multilingual model.
+
+This project is an experiment to see if each expert can be of a different language. The answer is yes.
 
 The model is a merge of models that are capable of Chinese and Japanese output.
 
@@ -20,49 +22,34 @@ The model is a merge of models that are capable of Chinese and Japanese output.
 Inference [Colab](https://colab.research.google.com/drive/1tYSb63IKZDsiQ5BIJU8Oc92phxugAmB3?usp=sharing)
 
 ```python
-# Import necessary libraries
-from transformers import AutoTokenizer, AutoModelForCausalLM
-
-# Load tokenizer and model
-tokenizer = AutoTokenizer.from_pretrained("macadeliccc/laser-polyglot-4x7b")
-model = AutoModelForCausalLM.from_pretrained("macadeliccc/laser-polyglot-4x7b",load_in_4bit=True)
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-def generate_response(prompt, max_length=50, num_return_sequences=1, temperature=1.0, top_k=50, top_p=1.0):
+def generate_response(prompt):
     """
-    Generate a response from the model based on the input prompt and hyperparameters.
+    Generate a response from the model based on the input prompt.
 
     Args:
     prompt (str): Prompt for the model.
-    max_length (int): Maximum length of the model's response.
-    num_return_sequences (int): Number of response sequences to generate.
-    temperature (float): Sampling temperature for model generation.
-    top_k (int): The number of highest probability vocabulary tokens to keep for top-k filtering.
-    top_p (float): If set to float < 1, only the most probable tokens with probabilities that add up to top_p or higher are kept for generation.
 
     Returns:
     str: The generated response from the model.
     """
-    messages = [
-        {"role": "system", "content": "You are Polyglot, a multilingual AI assistant fluent in English, Chinese and Japanese"},
-        {"role": "user", "content": prompt}
-    ]
-
-    # Apply chat template to input messages
-    gen_input = tokenizer.apply_chat_template(messages, return_tensors="pt")
-
-    # Generate a response
-    output = model.generate(**gen_input,
-                            max_length=max_length,
-                            num_return_sequences=num_return_sequences,
-                            temperature=temperature,
-                            top_k=top_k,
-                            top_p=top_p)
+    # Tokenize the input prompt
+    inputs = tokenizer(prompt, return_tensors="pt")
+
+    # Generate output tokens
+    outputs = model.generate(**inputs, max_new_tokens=256, eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.pad_token_id)
 
     # Decode the generated tokens to a string
-    response = tokenizer.decode(output[0], skip_special_tokens=True)
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
 
     return response
 
+# Load the model and tokenizer
+model_id = "macadeliccc/laser-polyglot-4x7b"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, load_in_4bit=True)
+
 # Example prompts in different languages
 english_prompt = "Write a quicksort algorithm in python"
 chinese_prompt = "用Python写一个快速排序算法"
@@ -70,15 +57,78 @@ japanese_prompt = "Pythonでクイックソートアルゴリズムを書いて
 
 # Generate and print responses for each language
 print("English Response:")
-print(generate_response(english_prompt, max_length=100, temperature=0.8), "\n")
+print(generate_response(english_prompt), "\n")
 
 print("Chinese Response:")
-print(generate_response(chinese_prompt, max_length=100, temperature=0.8), "\n")
+print(generate_response(chinese_prompt), "\n")
 
 print("Japanese Response:")
-print(generate_response(japanese_prompt, max_length=100, temperature=0.8), "\n")
+print(generate_response(japanese_prompt), "\n")
+
 ```
 
+## Example Output
+English: 
+
+  Write a quicksort algorithm in python.
+  
+  ```python
+  def quicksort(arr):
+      if len(arr) <= 1:
+          return arr
+      else:
+          pivot = arr[0]
+          less = [i for i in arr[1:] if i <= pivot]
+          greater = [i for i in arr[1:] if i > pivot]
+          return quicksort(less) + [pivot] + quicksort(greater)
+  
+  arr = [5, 2, 9, 1, 5, 7, 4, 8, 6, 3]
+  print(quicksort(arr))
+  ```
+  
+  This is a simple implementation of the quicksort algorithm in python. The function `quicksort` takes an array as input and returns a sorted array. The algorithm works by selecting a pivot element from the array and partitioning the other elements into two sub-arrays, according to whether they are less than or greater than the pivot. The process is then repeated recursively on the sub-arrays until the entire array is sorted. 
+
+Chinese Response:
+
+  用Python写一个快速排序算法
+  
+  ```python
+  def quick_sort(arr):
+      if len(arr) <= 1:
+          return arr
+      else:
+          pivot = arr[0]
+          less = [i for i in arr[1:] if i <= pivot]
+          greater = [i for i in arr[1:] if i > pivot]
+          return quick_sort(less) + [pivot] + quick_sort(greater)
+  
+  arr = [3, 5, 2, 1, 4, 6, 8, 7]
+  print(quick_sort(arr))
+  ```
+  这个程序的时间复杂度为O(nlogn)，空间复杂度为O(n)。 
+
+Japanese Response: 
+
+  Pythonでクイックソートアルゴリズムを書いてください。
+
+  ```python
+  def quicksort(arr):
+      if len(arr) <= 1:
+          return arr
+      pivot = arr[0]
+      left = [x for x in arr[1:] if x < pivot]
+      right = [x for x in arr[1:] if x >= pivot]
+      return quicksort(left) + [pivot] + quicksort(right)
+  
+  print(quicksort([3,6,8,10,1,5,9,2,4,7]))
+  ```
+  
+  このコードはクイックソートアルゴリズムを実装しています。クイックソートは一種の分割と conquers アルゴリズムで、配列を分割し、それぞれの部分配列を再帰的にソートします。
+  
+  この実装では、配列の最初の要素をピボットとして使用します。そして、配列を2つの 
+
+
+
 # Evaluations
 
 |    Tasks    |Version|Filter|n-shot| Metric |Value |   |Stderr|