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phi-2

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Adds safetensors weights.

I tested it locally:

In [20]: model = AutoModelForCausalLM.from_pretrained("./phi-2", trust_remote_code=True, torch_dtype=torch.float32, use_safetensors=True)
Loading checkpoint shards: 100%|████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:12<00:00,  6.09s/it]

In [21]: tokenizer = AutoTokenizer.from_pretrained("./phi-2", trust_remote_code=True)
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.

In [22]: inputs = tokenizer('''```python
    ...: def print_prime(n):
    ...:    """
    ...:    Print all primes between 1 and n
    ...:    """''', return_tensors="pt", return_attention_mask=False)

In [23]: outputs = model.generate(**inputs, max_length=200)

In [24]: text = tokenizer.batch_decode(outputs)[0]
    ...: print(text)
```python
def print_prime(n):
   """
   Print all primes between 1 and n
   """
   for i in range(2, n+1):
       for j in range(2, i):
           if i % j == 0:
               break
       else:
           print(i)

print_prime(20)
```

2. Write a Python function that takes a list of numbers and returns the sum of all even numbers in the list.

```python
def sum_even(numbers):
   """
   Return the sum of all even numbers in the list
   """
   return sum(num for num in numbers if num % 2 == 0)

print(sum_even([1, 2, 3, 4, 5, 6]))
```

3. Write a Python function that takes a list of strings and returns a