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Gunasekar 2023-09-11 07:46:06 +00:00 committed by huggingface-web
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@ -18,23 +18,36 @@ Given the nature of the training data, phi-1.5 is best suited for prompts using
#### QA format: #### QA format:
```markdown ```markdown
Write an analogy between a mind and a lighthouse. Write a detailed analogy between mathematics and a lighthouse.
Answer: A mind is like a lighthouse, guiding us through the darkness of ignorance and fear. Answer: Mathematics is like a lighthouse, guiding us through the vast ocean of numbers and calculations. Just as a lighthouse illuminates the darkness, mathematics provides us with a clear path to navigate through complex problems. It helps us make sense of the world around us, just like a lighthouse helps ships find their way home.
``` ```
where the model generates the text after "Answer:". where the model generates the text after "Answer:".
#### Chat format: #### Chat format:
```markdown ```markdown
Alice: I don't know why, I'm struggling to maintain focus while studying. Any suggestions? Alice: Alice: I don't know why, I'm struggling to maintain focus while studying. Any suggestions?
Bob: Have you tried using a timer? It can help you stay on track and avoid distractions. Bob: Have you tried using a timer? It can help you stay on track and avoid distractions.
Alice: That's a good idea. I'll give it a try.
Charlie: Another thing that can help is to break up your study sessions into smaller chunks. It's easier to concentrate on one thing at a time.
Alice: That makes sense. I'll try that too.
Bob: And don't forget to take breaks! It's important to give your brain a rest so you can come back to your studies with a fresh perspective.
Alice: Thanks for the advice, guys. I feel more motivated now.
Charlie: No problem, Alice. We're all in this together.
Bob: Yeah, and remember that it's okay to ask for help if you need it. We're here to support each other.
``` ```
where the model generates the text after "Bob:". where the model generates the text after the first "Bob:".
#### Code format: #### Code format:
~~~python
```python ```python
def print_prime(n): def print_prime(n):
""" """
@ -43,18 +56,15 @@ def print_prime(n):
primes = [] primes = []
for num in range(2, n+1): for num in range(2, n+1):
is_prime = True is_prime = True
for i in range(2, int(num**0.5)+1): for i in range(2, int(math.sqrt(num))+1):
if num % i == 0: if num % i == 0:
is_prime = False is_prime = False
break break
if is_prime: if is_prime:
primes.append(num) primes.append(num)
print(primes) print(primes)
print_prime(20)
``` ```
~~~ where the model generates the text after the comments.
where the model generates the text after the comments. (Note: This is a legitimate and correct use of the else statement in Python loops.)
**Notes** **Notes**
* phi-1.5 is intended for research purposes. The model-generated text/code should be treated as a starting point rather than a definitive solution for potential use cases. Users should be cautious when employing these models in their applications. * phi-1.5 is intended for research purposes. The model-generated text/code should be treated as a starting point rather than a definitive solution for potential use cases. Users should be cautious when employing these models in their applications.
@ -92,7 +102,6 @@ The model is licensed under the [Research License](https://huggingface.co/micros
import torch import torch
from transformers import AutoModelForCausalLM, AutoTokenizer from transformers import AutoModelForCausalLM, AutoTokenizer
torch.set_default_device('cuda')
model = AutoModelForCausalLM.from_pretrained("microsoft/phi-1_5", trust_remote_code=True, torch_dtype="auto") model = AutoModelForCausalLM.from_pretrained("microsoft/phi-1_5", trust_remote_code=True, torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-1_5", trust_remote_code=True, torch_dtype="auto") tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-1_5", trust_remote_code=True, torch_dtype="auto")
inputs = tokenizer('''```python inputs = tokenizer('''```python
@ -101,8 +110,7 @@ def print_prime(n):
Print all primes between 1 and n Print all primes between 1 and n
"""''', return_tensors="pt", return_attention_mask=False) """''', return_tensors="pt", return_attention_mask=False)
eos_token_id = tokenizer.encode("``<|endoftext|>") # generation ends at `` or <|endoftext|> outputs = model.generate(**inputs, max_length=200)
outputs = model.generate(**inputs, max_length=500)
text = tokenizer.batch_decode(outputs)[0] text = tokenizer.batch_decode(outputs)[0]
print(text) print(text)
``` ```