Update README.md

Author: ArmelRandy

README.md

@@ -20,7 +20,7 @@ huggingface-cli login
 ## Fine-Tuning (`finetune.py`)
 💫 StarCoder can be fine-tuned to achieve multiple downstream tasks. Our interest here is to fine-tune StarCoder in order to make it follow instructions. [Instruction fine-tuning](https://arxiv.org/pdf/2109.01652.pdf) has gained a lot of attention recently as it proposes a simple framework that teaches language models to align their outputs with human needs. That procedure requires the availability of quality instruction datasets, which contain multiple `instruction - answer` pairs. Unfortunately such datasets are not ubiquitous but thanks to Hugging Face 🤗's [datasets](https://github.com/huggingface/datasets) library we can have access to some good proxies. To fine-tune cheaply and efficiently, we use Hugging Face 🤗's [PEFT](https://github.com/huggingface/peft) as well as Tim Dettmers' bitsandbytes.
 
-### Code Alpaca 
+### Code Alpaca (CA)
 [Code Alpaca](https://huggingface.co/datasets/HuggingFaceH4/CodeAlpaca_20K) is a dataset of about 20K `prompt - completion` pairs generated by the technique presented in the [self-instruct](https://arxiv.org/abs/2212.10560) paper. Each prompt describes a task that is asked by a user and the corresponding completion is the answer to that task as generated by `text-davinci-003`.
 
 To execute the fine-tuning script run the following command:
@@ -57,7 +57,7 @@ python -m torch.distributed.launch \
   --weight_decay 0.05\
   --output_dir="./checkpoints"
 ```
-### Stack Exchange
+### Stack Exchange (SE)
 [Stack Exchange](https://en.wikipedia.org/wiki/Stack_Exchange) is a well-known network of Q&A websites on topics in diverse fields. It is a place where a user can ask a question and obtain answers from other users. Those answers are scored and ranked based on their quality. [Stack exchange instruction](https://huggingface.co/datasets/ArmelR/stack-exchange-instruction) is a dataset that was obtained by scrapping the site in order to build a collection of Q&A pairs. A language model can then be fine-tuned on that dataset to make it elicit strong and diverse question-answering skills.
 
 To execute the fine-tuning script run the following command:
@@ -114,24 +114,63 @@ For example
 python finetune/merge_peft_adapters.py --model_name_or_path bigcode/large-model --peft_model_path checkpoints/checkpoint-1000 --push_to_hub
 ```
 
-### Example outputs
+### Example outputs (CA)
 
-**Instruction**: Write a function to output the harmonic mean of a list of elements. Make sure to check if the list is empty and ignore the elements that are equal to 0.
+**Instruction**: Write a function to compute the geometric mean of a list of numbers.
 
 **StarCoder's answer**:
 
-**Instruction**:
+```python
+def geometric_mean(nums):
+    product = 1
+    for num in nums:
+        product *= num
+    return product**(1/len(nums))
 
-**StarCoder's answer**:
+```
 
-```bash
+**Instruction**:
+what is the purpose of the following code?
+```python
+def function(n):
+    s = ""
+    while n > 0 :
+        s = str(n%3)+s
+        n = n//3
+    return s
+```
 
+**StarCoder's answer**:
+```
+The purpose of the code is to convert a given number n to a string of its base 3 representation. This is done by dividing the number by 3 and converting the remainder to a string and then adding it to the string of the previous iteration. This process is repeated until the number is 0.
 ```
 
 **Instruction**:
+Translate this function is Java :
+
+```python
+def function(a, b, c):
+    if a > b :
+        print("You lost.")
+    else :
+        if c > a :
+            print("Nice try.")
+        else :
+            print("You won.")
+```
 
 **StarCoder's answer**:
 
-```bash
-
+```java
+public static void function(int a, int b, int c) {
+    if (a > b) {
+        System.out.println("You lost.");
+    } else {
+        if (c > a) {
+            System.out.println("Nice try.");
+        } else {
+            System.out.println("You won.");
+        }
+    }
+}
 ```