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+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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+http://www.apache.org/licenses/LICENSE-2.0
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+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
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+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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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.*
+<div style="float: right;">
+    <div class="flex flex-wrap space-x-1">
+        <img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
+        <img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
+        <img alt="Tensor parallelism" src="https://img.shields.io/badge/Tensor%20parallelism-06b6d4?style=flat&logoColor=white">
+    </div>
+</div>
+
+# 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