feat: implenent basic SFT pipeline based on synthetic data generator (#…

…1059)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

docs/sections/getting_started/quickstart.md

@@ -28,7 +28,23 @@ To install the latest release with `hf-inference-endpoints` extra of the package
 pip install distilabel[hf-inference-endpoints] --upgrade
 ```
 
-## Define a pipeline
+## Use a generic pipeline
+
+To use a generic pipeline for an ML task, you can use the `InstructionResponsePipeline` class. This class is a generic pipeline that can be used to generate data for supervised fine-tuning tasks. It uses the `InferenceEndpointsLLM` class to generate data based on the input data and the model.
+
+```python
+from distilabel.pipeline import InstructionResponsePipeline
+
+pipeline = InstructionResponsePipeline()
+dataset = pipeline.run()
+```
+
+The `InstructionResponsePipeline` class will use the `InferenceEndpointsLLM` class with the model `meta-llama/Meta-Llama-3.1-8B-Instruct` to generate data based on the system prompt. The output data will be a dataset with the columns `instruction` and `response`. The class uses a generic system prompt, but you can customize it by passing the `system_prompt` parameter to the class.
+
+!!! note
+    We're actively working on building more pipelines for different tasks. If you have any suggestions or requests, please let us know! We're currently working on pipelines for classification, Direct Preference Optimization, and Information Retrieval tasks.
+
+## Define a Custom pipeline
 
 In this guide we will walk you through the process of creating a simple pipeline that uses the [`InferenceEndpointsLLM`][distilabel.models.llms.InferenceEndpointsLLM] class to generate text. The [`Pipeline`][distilabel.pipeline.Pipeline] will load a dataset that contains a column named `prompt` from the Hugging Face Hub via the step [`LoadDataFromHub`][distilabel.steps.LoadDataFromHub] and then use the [`InferenceEndpointsLLM`][distilabel.models.llms.InferenceEndpointsLLM] class to generate text based on the dataset using the [`TextGeneration`](https://distilabel.argilla.io/dev/components-gallery/tasks/textgeneration/) task.
 

src/distilabel/pipeline/__init__.py

@@ -18,5 +18,14 @@
     routing_batch_function,
     sample_n_steps,
 )
+from distilabel.pipeline.templates import (
+    InstructionResponsePipeline,
+)
 
-__all__ = ["Pipeline", "RayPipeline", "routing_batch_function", "sample_n_steps"]
+__all__ = [
+    "Pipeline",
+    "RayPipeline",
+    "InstructionResponsePipeline",
+    "routing_batch_function",
+    "sample_n_steps",
+]

src/distilabel/pipeline/templates/__init__.py

@@ -0,0 +1,15 @@
+# Copyright 2023-present, Argilla, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# 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.
+
+from .instruction import InstructionResponsePipeline  # noqa: F401

src/distilabel/pipeline/templates/instruction.py

@@ -0,0 +1,129 @@
+# Copyright 2023-present, Argilla, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# 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.
+
+from typing import Optional
+
+from distilabel.distiset import Distiset
+from distilabel.llms.base import LLM
+from distilabel.llms.huggingface import InferenceEndpointsLLM
+from distilabel.pipeline import Pipeline
+from distilabel.steps.tasks import MagpieGenerator
+
+MODEL = "meta-llama/Meta-Llama-3.1-8B-Instruct"
+
+
+class InstructionResponsePipeline:
+    """Generates instructions and responses for a given system prompt.
+
+    This example pipeline can be used for a Supervised Fine-Tuning dataset which you
+    could use to train or evaluate a model. The pipeline generates instructions using the
+    MagpieGenerator and responses for a given system prompt. The pipeline then keeps only
+    the instruction, response, and model_name columns.
+
+    References:
+        - [Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing](https://arxiv.org/abs/2406.08464)
+
+    Example:
+
+        Generate instructions and responses for a given system prompt:
+
+        ```python
+        from distilabel.pipeline import InstructionResponsePipeline
+
+        pipeline = InstructionResponsePipeline()
+
+        distiset = pipeline.run()
+        ```
+
+        Customizing the pipeline further:
+
+        ```python
+        from distilabel.pipeline import InstructionResponsePipeline
+
+        pipeline = InstructionResponsePipeline(
+            system_prompt="You are a creative AI Assistant for writing science fiction.",
+            llm=InferenceEndpointsLLM(
+                model_id="meta-llama/Meta-Llama-3.2-3B-Instruct",
+                tokenizer_id="meta-llama/Meta-Llama-3.2-3B-Instruct",
+                generation_kwargs={"max_new_tokens": 512, "temperature": 0.7},
+            ),
+            num_rows=500,
+            batch_size=2,
+            n_turns=2,
+        )
+        ```
+    """
+
+    def __init__(
+        self,
+        llm: Optional[LLM] = None,
+        system_prompt: str = "You are a creative AI Assistant writer.",
+        hf_token: Optional[str] = None,
+        n_turns: int = 1,
+        num_rows: int = 10,
+        batch_size: int = 1,
+    ) -> None:
+        if llm is None:
+            self.llm: LLM = InferenceEndpointsLLM(
+                model_id=MODEL,
+                tokenizer_id=MODEL,
+                magpie_pre_query_template="llama3",
+                generation_kwargs={
+                    "temperature": 0.9,
+                    "do_sample": True,
+                    "max_new_tokens": 2048,
+                    "stop_sequences": [
+                        "<|eot_id|>",
+                        "<|start_header_id|>",
+                        "assistant",
+                        " \n\n",
+                    ],
+                },
+                api_key=hf_token,
+            )
+        else:
+            self.llm = llm
+
+        self.pipeline: Pipeline = self._get_magpie_pipeline(
+            system_prompt=system_prompt,
+            n_turns=n_turns,
+            num_rows=num_rows,
+            batch_size=batch_size,
+        )
+
+    def run(self, **kwargs) -> Distiset:
+        """Runs the pipeline and returns a Distiset."""
+        return self.pipeline.run(**kwargs)
+
+    def _get_magpie_pipeline(
+        self, system_prompt: str, n_turns: int, num_rows: int, batch_size: int
+    ) -> Pipeline:
+        """Returns a pipeline that generates instructions and responses for a given system prompt."""
+        with Pipeline(name="sft") as pipeline:
+            MagpieGenerator(
+                llm=self.llm,
+                n_turns=n_turns,
+                num_rows=num_rows,
+                batch_size=batch_size,
+                system_prompt=system_prompt,
+            )
+
+        return pipeline
+
+    def _get_output_columns(self, n_turns: int) -> list:
+        """Returns the output mappings for the pipeline."""
+        if n_turns == 1:
+            return ["instruction", "response", "model_name"]
+        else:
+            return ["instruction", "conversation", "model_name"]