huggingface--transformers/docs/source/en/model_doc/phi.md

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*This model was published in HF papers on 2023-06-20 and contributed to Hugging Face Transformers on 2023-11-10.*
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# Phi

[Phi](https://huggingface.co/papers/2306.11644) 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](https://huggingface.co/collections/microsoft/phi-1-6626e29134744e94e222d572) 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.

<hfoptions id="usage">
<hfoption id="Pipeline">

```py
import torch
from transformers import pipeline

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

```

</hfoption>

<hfoption id="AutoModel">

```python
from transformers import AutoModelForCausalLM, AutoTokenizer


tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-1")
model = AutoModelForCausalLM.from_pretrained("microsoft/phi-1", 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))
```

</hfoption>
</hfoptions>

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

The example below uses [bitsandbytes](https://huggingface.co/docs/transformers/en/quantization/bitsandbytes) to only quantize the weights to 4-bits.

```python
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig


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", 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 [`~AutoModel.from_pretrained`]. Otherwise, make sure you update Transformers to the latest stable version.

    ```py
    import torch
    from transformers import AutoTokenizer, AutoModelForCausalLM

    tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-1")
    model = AutoModelForCausalLM.from_pretrained(
        "microsoft/phi-1",
        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