microsoft
/

phi-1

@@ -43,8 +43,6 @@ where the model generates the code after the comments. (Note: This is a legitima
 * Direct adoption for production coding tasks is out of the scope of this research project. As a result, Phi-1 has not been tested to ensure that it performs adequately for production-level code. Please refer to the limitation sections of this document for more details.
-* If you are using `transformers<4.37.0`, always load the model with `trust_remote_code=True` to prevent side-effects.
 ## Sample Code
 ```python
@@ -53,8 +51,8 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
 torch.set_default_device("cuda")
-model = AutoModelForCausalLM.from_pretrained("microsoft/phi-1", torch_dtype="auto", trust_remote_code=True)
-tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-1", trust_remote_code=True)
 inputs = tokenizer('''def print_prime(n):
    """
@@ -73,6 +71,7 @@ print(text)
 * Replicate Scripts Online: As our model is trained on Python scripts found online, there is a small chance it may replicate such scripts, especially if they appear repetitively across different online sources.
 * Generate Inaccurate Code: The model frequently generates incorrect code. We suggest that users view these outputs as a source of inspiration rather than definitive solutions.
 * Unreliable Responses to Alternate Formats: Despite appearing to comprehend instructions in formats like Q&A or chat, our models often respond with inaccurate answers, even when seeming confident. Their capabilities with non-code formats are significantly more limited.
 * Limitations on Natural Language Comprehension. As a coding bot, Phi-1's main focus is to help with coding-related questions. While it may have some natural language comprehension capabilities, its primary function is not to engage in general conversations or demonstrate common sense like a general AI assistant. Its strength lies in providing assistance and guidance in the context of programming and software development.

 * Direct adoption for production coding tasks is out of the scope of this research project. As a result, Phi-1 has not been tested to ensure that it performs adequately for production-level code. Please refer to the limitation sections of this document for more details.
 ## Sample Code
 ```python
 torch.set_default_device("cuda")
+model = AutoModelForCausalLM.from_pretrained("microsoft/phi-1", torch_dtype="auto")
+tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-1")
 inputs = tokenizer('''def print_prime(n):
    """
 * Replicate Scripts Online: As our model is trained on Python scripts found online, there is a small chance it may replicate such scripts, especially if they appear repetitively across different online sources.
 * Generate Inaccurate Code: The model frequently generates incorrect code. We suggest that users view these outputs as a source of inspiration rather than definitive solutions.
 * Unreliable Responses to Alternate Formats: Despite appearing to comprehend instructions in formats like Q&A or chat, our models often respond with inaccurate answers, even when seeming confident. Their capabilities with non-code formats are significantly more limited.
 * Limitations on Natural Language Comprehension. As a coding bot, Phi-1's main focus is to help with coding-related questions. While it may have some natural language comprehension capabilities, its primary function is not to engage in general conversations or demonstrate common sense like a general AI assistant. Its strength lies in providing assistance and guidance in the context of programming and software development.