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Update README.md
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README.md
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@@ -18,23 +18,36 @@ Given the nature of the training data, phi-1.5 is best suited for prompts using
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#### QA format:
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```markdown
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Write
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Answer:
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```
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where the model generates the text after "Answer:".
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#### Chat format:
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```markdown
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Alice: I don't know why, I'm struggling to maintain focus while studying. Any suggestions?
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Bob: Have you tried using a timer? It can help you stay on track and avoid distractions.
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```
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where the model generates the text after "Bob:".
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#### Code format:
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~~~python
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```python
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def print_prime(n):
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"""
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@@ -43,18 +56,15 @@ def print_prime(n):
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primes = []
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for num in range(2, n+1):
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is_prime = True
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for i in range(2, int(num
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if num % i == 0:
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is_prime = False
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break
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if is_prime:
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primes.append(num)
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print(primes)
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print_prime(20)
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```
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where the model generates the text after the comments. (Note: This is a legitimate and correct use of the else statement in Python loops.)
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**Notes**
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* 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.
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@@ -92,7 +102,6 @@ The model is licensed under the [Research License](https://huggingface.co/micros
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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torch.set_default_device('cuda')
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model = AutoModelForCausalLM.from_pretrained("microsoft/phi-1_5", trust_remote_code=True, torch_dtype="auto")
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tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-1_5", trust_remote_code=True, torch_dtype="auto")
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inputs = tokenizer('''```python
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Print all primes between 1 and n
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"""''', return_tensors="pt", return_attention_mask=False)
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outputs = model.generate(**inputs, max_length=500)
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text = tokenizer.batch_decode(outputs)[0]
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print(text)
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```
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#### QA format:
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```markdown
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Write a detailed analogy between mathematics and a lighthouse.
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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.
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```
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where the model generates the text after "Answer:".
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#### Chat format:
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```markdown
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Alice: Alice: I don't know why, I'm struggling to maintain focus while studying. Any suggestions?
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Bob: Have you tried using a timer? It can help you stay on track and avoid distractions.
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Alice: That's a good idea. I'll give it a try.
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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.
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Alice: That makes sense. I'll try that too.
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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.
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Alice: Thanks for the advice, guys. I feel more motivated now.
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Charlie: No problem, Alice. We're all in this together.
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Bob: Yeah, and remember that it's okay to ask for help if you need it. We're here to support each other.
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```
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where the model generates the text after the first "Bob:".
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#### Code format:
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```python
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def print_prime(n):
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"""
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primes = []
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for num in range(2, n+1):
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is_prime = True
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for i in range(2, int(math.sqrt(num))+1):
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if num % i == 0:
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is_prime = False
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break
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if is_prime:
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primes.append(num)
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print(primes)
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```
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where the model generates the text after the comments.
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**Notes**
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* 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.
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("microsoft/phi-1_5", trust_remote_code=True, torch_dtype="auto")
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tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-1_5", trust_remote_code=True, torch_dtype="auto")
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inputs = tokenizer('''```python
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Print all primes between 1 and n
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"""''', return_tensors="pt", return_attention_mask=False)
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outputs = model.generate(**inputs, max_length=200)
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text = tokenizer.batch_decode(outputs)[0]
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print(text)
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```
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