Phi

This model was released on 2023-06-20 and added to Hugging Face Transformers on 2023-11-10.

Phi

Phi is a 1.3B parameter transformer model optimized for Python code generation. It focuses on “textbook-quality” training data of code examples, exercises and synthetic Python problems rather than scaling the model size or compute.

You can find all the original Phi checkpoints under the Phi-1 collection.

Tip

Click on the Phi models in the right sidebar for more examples of how to apply Phi to different language tasks.

The example below demonstrates how to generate text with Pipeline, AutoModel and from the command line.

import torch
from transformers import pipeline

pipeline = pipeline(task="text-generation", model="microsoft/phi-1.5", device=0, dtype=torch.bfloat16)
pipeline("pipeline('''def print_prime(n): """ Print all primes between 1 and n"""''')")

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-1")
model = AutoModelForCausalLM.from_pretrained("microsoft/phi-1", dtype=torch.float16, device_map="auto", attn_implementation="sdpa")

input_ids = tokenizer('''def print_prime(n):
   """
   Print all primes between 1 and n
   """''', return_tensors="pt").to(model.device)

output = model.generate(**input_ids, cache_implementation="static")
print(tokenizer.decode(output[0], skip_special_tokens=True))

echo -e "'''def print_prime(n): """ Print all primes between 1 and n"""'''" | transformers run --task text-classification --model microsoft/phi-1.5 --device 0

Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the Quantization overview for more available quantization backends.

The example below uses bitsandbytes to only quantize the weights to 4-bits.

import torch
from transformers import BitsAndBytesConfig, AutoTokenizer, AutoModelForCausalLM

bnb_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_quant_type="nf4", bnb_4bit_use_double_quant=True)
tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-1")
model = AutoModelForCausalLM.from_pretrained("microsoft/phi-1", dtype=torch.float16, device_map="auto", attn_implementation="sdpa", quantization_config=bnb_config)

input_ids = tokenizer('''def print_prime(n):
   """
   Print all primes between 1 and n
   """''', return_tensors="pt").to(model.device)

output = model.generate(**input_ids, cache_implementation="static")
print(tokenizer.decode(output[0], skip_special_tokens=True))

Notes

• If you’re using Transformers < 4.37.0.dev, set trust_remote_code=True in from_pretrained. Otherwise, make sure you update Transformers to the latest stable version.

  import torch
  from transformers import AutoTokenizer, AutoModelForCausalLM
  
  tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-1")
  model = AutoModelForCausalLM.from_pretrained(
      "microsoft/phi-1",
      dtype=torch.float16,
      device_map="auto",
      trust_remote_code=True,
      attn_implementation="sdpa")
  
  input_ids = tokenizer('''def print_prime(n):
     """
     Print all primes between 1 and n
     """''', return_tensors="pt").to(model.device)
  
  output = model.generate(**input_ids, cache_implementation="static")
  print(tokenizer.decode(output[0], skip_special_tokens=True))

PhiConfig

[[autodoc]] PhiConfig

PhiModel

[[autodoc]] PhiModel - forward

PhiForCausalLM

[[autodoc]] PhiForCausalLM - forward - generate

PhiForSequenceClassification

[[autodoc]] PhiForSequenceClassification - forward

PhiForTokenClassification

[[autodoc]] PhiForTokenClassification - forward