diff --git a/src/rago/generation/__init__.py b/src/rago/generation/__init__.py
index bee32b4..a9d5906 100644
--- a/src/rago/generation/__init__.py
+++ b/src/rago/generation/__init__.py
@@ -12,6 +12,7 @@
 from rago.generation.hugging_face_inf import HuggingFaceInfGen
 from rago.generation.llama import LlamaGen
 from rago.generation.openai import OpenAIGen
+from rago.generation.phi import PhiGen
 from rago.generation.together import TogetherGen
 
 __all__ = [
@@ -25,5 +26,6 @@
     'HuggingFaceInfGen',
     'LlamaGen',
     'OpenAIGen',
+    'PhiGen',
     'TogetherGen',
 ]
diff --git a/src/rago/generation/phi.py b/src/rago/generation/phi.py
new file mode 100644
index 0000000..4abe24d
--- /dev/null
+++ b/src/rago/generation/phi.py
@@ -0,0 +1,94 @@
+"""Phi generation module."""
+
+from __future__ import annotations
+
+import warnings
+
+from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
+from typeguard import typechecked
+
+from rago.generation.base import GenerationBase
+
+
+@typechecked
+class PhiGen(GenerationBase):
+    """Phi Generation class."""
+
+    default_model_name: str = 'microsoft/phi-2'
+    default_temperature: float = 0.7
+    default_output_max_length: int = 500
+    default_api_params = {  # noqa: RUF012
+        'top_p': 0.9,
+        'num_return_sequences': 1,
+    }
+
+    def _validate(self) -> None:
+        """Raise an error if the initial parameters are not valid."""
+        if not self.model_name.startswith('microsoft/phi-'):
+            raise Exception(
+                f'The given model name {self.model_name} is not a Phi model '
+                'from Microsoft.'
+            )
+
+        if self.structured_output:
+            warnings.warn(
+                'Structured output is not supported yet in '
+                f'{self.__class__.__name__}.'
+            )
+
+    def _setup(self) -> None:
+        """Set up the object with the initial parameters."""
+        self.tokenizer = AutoTokenizer.from_pretrained(
+            self.model_name, trust_remote_code=True
+        )
+
+        device_map = 'auto' if self.device_name == 'cuda' else None
+
+        self.model = AutoModelForCausalLM.from_pretrained(
+            self.model_name,
+            torch_dtype='auto',
+            device_map=device_map,
+            trust_remote_code=True,
+        )
+
+        if self.tokenizer.pad_token is None:
+            self.tokenizer.pad_token = self.tokenizer.eos_token
+
+        self.model.generation_config = GenerationConfig.from_pretrained(
+            self.model_name
+        )
+        self.model.generation_config.pad_token_id = (
+            self.model.generation_config.eos_token_id
+        )
+
+    def generate(self, query: str, context: list[str]) -> str:
+        """Generate text using Phi model with context."""
+        full_prompt = f'{query}\nContext: {" ".join(context)}'
+
+        inputs = self.tokenizer(
+            full_prompt, return_tensors='pt', return_attention_mask=True
+        ).to(self.model.device)
+
+        model_params = dict(
+            max_new_tokens=self.output_max_length,
+            do_sample=True,
+            temperature=self.temperature,
+            top_p=self.default_api_params['top_p'],
+            num_return_sequences=self.default_api_params[
+                'num_return_sequences'
+            ],
+        )
+
+        self.logs['model_params'] = model_params
+
+        outputs = self.model.generate(
+            input_ids=inputs.input_ids,
+            attention_mask=inputs.attention_mask,
+            **model_params,
+        )
+
+        answer: str = self.tokenizer.decode(
+            outputs[0][inputs.input_ids.shape[1] :], skip_special_tokens=True
+        )
+
+        return answer.strip()
diff --git a/tests/test_generation.py b/tests/test_generation.py
index 171afc8..b0dddb1 100644
--- a/tests/test_generation.py
+++ b/tests/test_generation.py
@@ -15,6 +15,7 @@
     HuggingFaceInfGen,
     LlamaGen,
     OpenAIGen,
+    PhiGen,
     TogetherGen,
 )
 
@@ -91,6 +92,11 @@
     partial(
         GroqGen,
     ),
+    # model 10
+    partial(
+        PhiGen,
+        **dict(model_name='microsoft/phi-2'),
+    ),
 ]