Repository: roboflow/maestro (language: python)

Issue:
add Florence-2 `evaluation` command
# Description

- `README.md` improvements
- Florence-2 `evaluation` command


Discussion / hints:

Fix patch:
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
new file mode 100644
index 0000000..bf39b30
--- /dev/null
+++ b/.pre-commit-config.yaml
@@ -0,0 +1,21 @@
+repos:
+-   repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v2.3.0
+    hooks:
+    -   id: check-yaml
+    -   id: end-of-file-fixer
+    -   id: trailing-whitespace
+-   repo: https://github.com/psf/black
+    rev: 24.8.0
+    hooks:
+    -   id: black
+        args: [--line-length=120]
+-   repo: https://github.com/pre-commit/mirrors-mypy
+    rev: v1.11.2
+    hooks:
+    -   id: mypy
+-   repo: https://github.com/PyCQA/flake8
+    rev: 7.1.1
+    hooks:
+    -   id: flake8
+        args: [--max-line-length=120]
diff --git a/README.md b/README.md
index ec1dfa1..26ea4f9 100644
--- a/README.md
+++ b/README.md
@@ -1,142 +1,61 @@
-
 <div align="center">
 
-  <h1>multimodal-maestro</h1>
-
-  <br>
+  <h1>maestro</h1>
 
-  [![version](https://badge.fury.io/py/maestro.svg)](https://badge.fury.io/py/maestro)
-  [![license](https://img.shields.io/pypi/l/maestro)](https://github.com/roboflow/multimodal-maestro/blob/main/LICENSE)
-  [![python-version](https://img.shields.io/pypi/pyversions/maestro)](https://badge.fury.io/py/maestro)
-  [![Gradio](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/Roboflow/SoM)
-  [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/roboflow/multimodal-maestro/blob/develop/cookbooks/multimodal_maestro_gpt_4_vision.ipynb)
+  <p>coming: when it's ready...</p>
 
 </div>
 
 ## 👋 hello
 
-Multimodal-Maestro gives you more control over large multimodal models to get the 
-outputs you want. With more effective prompting tactics, you can get multimodal models 
-to do tasks you didn't know (or think!) were possible. Curious how it works? Try our 
-[HF space](https://huggingface.co/spaces/Roboflow/SoM)!
+**maestro** is a tool designed to streamline and accelerate the fine-tuning process for 
+multimodal models. It provides ready-to-use recipes for fine-tuning popular 
+vision-language models (VLMs) such as **Florence-2**, **PaliGemma**, and 
+**Phi-3.5 Vision** on downstream vision-language tasks.
 
 ## 💻 install
 
-⚠️ Our package has been renamed to `maestro`. Install the package in a
-[**3.11>=Python>=3.8**](https://www.python.org/) environment.
+Pip install the supervision package in a
+[**Python>=3.8**](https://www.python.org/) environment.
 
 ```bash
 pip install maestro
 ```
 
-## 🔌 API
+## 🔥 quickstart
 
-🚧 The project is still under construction. The redesigned API is coming soon.
+### CLI
 
-![maestro-docs-Snap](https://github.com/roboflow/multimodal-maestro/assets/26109316/a787b7c0-527e-465a-9ca9-d46f4d63ea53)
+VLMs can be fine-tuned on downstream tasks directly from the command line with 
+`maestro` command:
 
-## 🧑‍🍳 prompting cookbooks
+```bash
+maestro florence2 train --dataset='<DATASET_PATH>' --epochs=10 --batch-size=8
+```
 
-| Description                                                     | Colab                                                                                                                                                                                                   |
-|:----------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
-| Prompt LMMs with Multimodal Maestro                             | [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/roboflow/multimodal-maestro/blob/develop/cookbooks/multimodal_maestro_gpt_4_vision.ipynb) |
-| Manually annotate ONE image and let GPT-4V annotate ALL of them | [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/roboflow/multimodal-maestro/blob/develop/cookbooks/grounding_dino_and_gpt4_vision.ipynb)  |
+### SDK
 
+Alternatively, you can fine-tune VLMs using the Python SDK, which accepts the same 
+arguments as the CLI example above:
 
-## 🚀 example
+```python
+from maestro.trainer.common import MeanAveragePrecisionMetric
+from maestro.trainer.models.florence_2 import train, TrainingConfiguration
 
-```
-Find dog.
+config = TrainingConfiguration(
+    dataset='<DATASET_PATH>',
+    epochs=10,
+    batch_size=8,
+    metrics=[MeanAveragePrecisionMetric()]
+)
 
->>> The dog is prominently featured in the center of the image with the label [9].
+train(config)
 ```
 
-<details close>
-<summary>👉 read more</summary>
-
-<br>
-
-- **load image**
-
-  ```python
-  import cv2
-  
-  image = cv2.imread("...")
-  ```
-
-- **create and refine marks**
-
-  ```python
-  import maestro
-  
-  generator = maestro.SegmentAnythingMarkGenerator(device='cuda')
-  marks = generator.generate(image=image)
-  marks = maestro.refine_marks(marks=marks)
-  ```
-
-- **visualize marks**
-
-  ```python
-  mark_visualizer = maestro.MarkVisualizer()
-  marked_image = mark_visualizer.visualize(image=image, marks=marks)
-  ```
-  ![image-vs-marked-image](https://github.com/roboflow/multimodal-maestro/assets/26109316/92951ed2-65c0-475a-9279-6fd344757092)
-
-- **prompt**
-
-  ```python
-  prompt = "Find dog."
-  
-  response = maestro.prompt_image(api_key=api_key, image=marked_image, prompt=prompt)
-  ```
-  
-  ```
-  >>> "The dog is prominently featured in the center of the image with the label [9]."
-  ```
-
-- **extract related marks**
-
-  ```python
-  masks = maestro.extract_relevant_masks(text=response, detections=refined_marks)
-  ```
-  
-  ```
-  >>> {'6': array([
-  ...     [False, False, False, ..., False, False, False],
-  ...     [False, False, False, ..., False, False, False],
-  ...     [False, False, False, ..., False, False, False],
-  ...     ...,
-  ...     [ True,  True,  True, ..., False, False, False],
-  ...     [ True,  True,  True, ..., False, False, False],
-  ...     [ True,  True,  True, ..., False, False, False]])
-  ... }
-  ```
-
-</details>
-
-![multimodal-maestro](https://github.com/roboflow/multimodal-maestro/assets/26109316/c04f2b18-2a1d-4535-9582-e5d3ec0a926e)
-
-## 🚧 roadmap
-
-- [ ] Rewriting the `maestro` API.
-- [ ] Update [HF space](https://huggingface.co/spaces/Roboflow/SoM).
-- [ ] Documentation page.
-- [ ] Add GroundingDINO prompting strategy.
-- [ ] CovVLM demo.
-- [ ] Qwen-VL demo.
-
-## 💜 acknowledgement
-
-- [Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding
-in GPT-4V](https://arxiv.org/abs/2310.11441) by Jianwei Yang, Hao Zhang, Feng Li, Xueyan
-Zou, Chunyuan Li, Jianfeng Gao.
-- [The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision)](https://arxiv.org/abs/2309.17421)
-by Zhengyuan Yang, Linjie Li, Kevin Lin, Jianfeng Wang, Chung-Ching Lin, Zicheng Liu, 
-Lijuan Wang
-
 ## 🦸 contribution
 
-We would love your help in making this repository even better! If you noticed any bug, 
-or if you have any suggestions for improvement, feel free to open an 
+We would love your help in making this repository even better! We are especially 
+looking for contributors with experience in fine-tuning vision-language models (VLMs). 
+If you notice any bugs or have suggestions for improvement, feel free to open an 
 [issue](https://github.com/roboflow/multimodal-maestro/issues) or submit a 
 [pull request](https://github.com/roboflow/multimodal-maestro/pulls).
diff --git a/maestro/cli/__init__.py b/maestro/cli/__init__.py
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/maestro/cli/__init__.py
@@ -0,0 +1,1 @@
+
diff --git a/maestro/cli/env.py b/maestro/cli/env.py
new file mode 100644
index 0000000..b95525e
--- /dev/null
+++ b/maestro/cli/env.py
@@ -0,0 +1,2 @@
+DISABLE_RECIPE_IMPORTS_WARNINGS_ENV = "DISABLE_RECIPE_IMPORTS_WARNINGS"
+DEFAULT_DISABLE_RECIPE_IMPORTS_WARNINGS_ENV = "False"
diff --git a/maestro/cli/introspection.py b/maestro/cli/introspection.py
new file mode 100644
index 0000000..fc6aef9
--- /dev/null
+++ b/maestro/cli/introspection.py
@@ -0,0 +1,37 @@
+import os
+
+import typer
+
+from maestro.cli.env import DISABLE_RECIPE_IMPORTS_WARNINGS_ENV, \
+    DEFAULT_DISABLE_RECIPE_IMPORTS_WARNINGS_ENV
+from maestro.cli.utils import str2bool
+
+
+def find_training_recipes(app: typer.Typer) -> None:
+    try:
+        from maestro.trainer.models.florence_2.entrypoint import florence_2_app
+
+        app.add_typer(florence_2_app, name="florence2")
+    except Exception:
+        _warn_about_recipe_import_error(model_name="Florence 2")
+
+    try:
+        from maestro.trainer.models.paligemma.entrypoint import paligemma_app
+
+        app.add_typer(paligemma_app, name="paligemma")
+    except Exception:
+        _warn_about_recipe_import_error(model_name="PaliGemma")
+
+
+def _warn_about_recipe_import_error(model_name: str) -> None:
+    disable_warnings = str2bool(
+        os.getenv(
+            DISABLE_RECIPE_IMPORTS_WARNINGS_ENV,
+            DEFAULT_DISABLE_RECIPE_IMPORTS_WARNINGS_ENV,
+        )
+    )
+    if disable_warnings:
+        return None
+    warning = typer.style("WARNING", fg=typer.colors.RED, bold=True)
+    message = "🚧 " + warning + f" cannot import recipe for {model_name}"
+    typer.echo(message)
diff --git a/maestro/cli/main.py b/maestro/cli/main.py
new file mode 100644
index 0000000..b600e3a
--- /dev/null
+++ b/maestro/cli/main.py
@@ -0,0 +1,15 @@
+import typer
+
+from maestro.cli.introspection import find_training_recipes
+
+app = typer.Typer()
+find_training_recipes(app=app)
+
+
+@app.command(help="Display information about maestro")
+def info():
+    typer.echo("Welcome to maestro CLI. Let's train some VLM! 🏋")
+
+
+if __name__ == "__main__":
+    app()
diff --git a/maestro/cli/utils.py b/maestro/cli/utils.py
new file mode 100644
index 0000000..0751fef
--- /dev/null
+++ b/maestro/cli/utils.py
@@ -0,0 +1,2 @@
+def str2bool(value: str) -> bool:
+    return value.lower() in {"y", "t", "yes", "true"}
diff --git a/maestro/trainer/__init__.py b/maestro/trainer/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/maestro/trainer/common/__init__.py b/maestro/trainer/common/__init__.py
new file mode 100644
index 0000000..0bfe6b2
--- /dev/null
+++ b/maestro/trainer/common/__init__.py
@@ -0,0 +1,1 @@
+from maestro.trainer.common.utils.metrics import MeanAveragePrecisionMetric
diff --git a/maestro/trainer/common/configuration/__init__.py b/maestro/trainer/common/configuration/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/maestro/trainer/common/configuration/env.py b/maestro/trainer/common/configuration/env.py
new file mode 100644
index 0000000..eeb9105
--- /dev/null
+++ b/maestro/trainer/common/configuration/env.py
@@ -0,0 +1,5 @@
+SEED_ENV = "SEED"
+DEFAULT_SEED = "42"
+CUDA_DEVICE_ENV = "CUDA_DEVICE"
+DEFAULT_CUDA_DEVICE = "cuda:0"
+HF_TOKEN_ENV = "HF_TOKEN"
diff --git a/maestro/trainer/common/data_loaders/__init__.py b/maestro/trainer/common/data_loaders/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/maestro/trainer/common/data_loaders/datasets.py b/maestro/trainer/common/data_loaders/datasets.py
new file mode 100644
index 0000000..b79cfec
--- /dev/null
+++ b/maestro/trainer/common/data_loaders/datasets.py
@@ -0,0 +1,50 @@
+import json
+import os
+from typing import List, Dict, Any, Tuple
+
+from PIL import Image
+from transformers.pipelines.base import Dataset
+
+
+class JSONLDataset:
+    def __init__(self, jsonl_file_path: str, image_directory_path: str):
+        self.jsonl_file_path = jsonl_file_path
+        self.image_directory_path = image_directory_path
+        self.entries = self._load_entries()
+
+    def _load_entries(self) -> List[Dict[str, Any]]:
+        entries = []
+        with open(self.jsonl_file_path, "r") as file:
+            for line in file:
+                data = json.loads(line)
+                entries.append(data)
+        return entries
+
+    def __len__(self) -> int:
+        return len(self.entries)
+
+    def __getitem__(self, idx: int) -> Tuple[Image.Image, Dict[str, Any]]:
+        if idx < 0 or idx >= len(self.entries):
+            raise IndexError("Index out of range")
+
+        entry = self.entries[idx]
+        image_path = os.path.join(self.image_directory_path, entry["image"])
+        try:
+            image = Image.open(image_path)
+            return (image, entry)
+        except FileNotFoundError:
+            raise FileNotFoundError(f"Image file {image_path} not found.")
+
+
+class DetectionDataset(Dataset):
+    def __init__(self, jsonl_file_path: str, image_directory_path: str):
+        self.dataset = JSONLDataset(jsonl_file_path, image_directory_path)
+
+    def __len__(self):
+        return len(self.dataset)
+
+    def __getitem__(self, idx):
+        image, data = self.dataset[idx]
+        prefix = data["prefix"]
+        suffix = data["suffix"]
+        return prefix, suffix, image
diff --git a/maestro/trainer/common/data_loaders/jsonl.py b/maestro/trainer/common/data_loaders/jsonl.py
new file mode 100644
index 0000000..3630e11
--- /dev/null
+++ b/maestro/trainer/common/data_loaders/jsonl.py
@@ -0,0 +1,31 @@
+from __future__ import annotations
+
+import random
+from typing import List
+
+from torch.utils.data import Dataset
+
+from maestro.trainer.common.utils.file_system import read_jsonl
+
+
+class JSONLDataset(Dataset):
+    # TODO: implementation could be better - avoiding loading
+    #  whole files to memory
+
+    @classmethod
+    def from_jsonl_file(cls, path: str) -> JSONLDataset:
+        file_content = read_jsonl(path=path)
+        random.shuffle(file_content)
+        return cls(jsons=file_content)
+
+    def __init__(self, jsons: List[dict]):
+        self.jsons = jsons
+
+    def __getitem__(self, index):
+        return self.jsons[index]
+
+    def __len__(self):
+        return len(self.jsons)
+
+    def shuffle(self):
+        random.shuffle(self.jsons)
diff --git a/maestro/trainer/common/utils/__init__.py b/maestro/trainer/common/utils/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/maestro/trainer/common/utils/file_system.py b/maestro/trainer/common/utils/file_system.py
new file mode 100644
index 0000000..da8e5a9
--- /dev/null
+++ b/maestro/trainer/common/utils/file_system.py
@@ -0,0 +1,59 @@
+import json
+import os
+from glob import glob
+from typing import Union, List
+
+
+def read_jsonl(path: str) -> List[dict]:
+    file_lines = read_file(
+        path=path,
+        split_lines=True,
+    )
+    return [json.loads(line) for line in file_lines]
+
+
+def read_file(
+    path: str,
+    split_lines: bool = False,
+    strip_white_spaces: bool = False,
+    line_separator: str = "\n",
+) -> Union[str, List[str]]:
+    with open(path, "r") as f:
+        file_content = f.read()
+    if strip_white_spaces:
+        file_content = file_content.strip()
+    if not split_lines:
+        return file_content
+    lines = file_content.split(line_separator)
+    if not strip_white_spaces:
+        return lines
+    return [line.strip() for line in lines]
+
+
+def save_json(path: str, content: dict) -> None:
+    ensure_parent_dir_exists(path=path)
+    with open(path, "w") as f:
+        json.dump(content, f, indent=4)
+
+
+def ensure_parent_dir_exists(path: str) -> None:
+    parent_dir = os.path.dirname(os.path.abspath(path))
+    os.makedirs(parent_dir, exist_ok=True)
+
+
+def create_new_run_directory(base_output_dir: str) -> str:
+    """
+    Creates a new numbered directory for the current training run.
+
+    Args:
+        base_output_dir (str): The base directory where all run directories are stored.
+
+    Returns:
+        str: The path to the newly created run directory.
+    """
+    base_output_dir = os.path.abspath(base_output_dir)
+    existing_run_dirs = [d for d in glob(os.path.join(base_output_dir, "*")) if os.path.isdir(d)]
+    new_run_number = len(existing_run_dirs) + 1
+    new_run_dir = os.path.join(base_output_dir, str(new_run_number))
+    os.makedirs(new_run_dir, exist_ok=True)
+    return new_run_dir
diff --git a/maestro/trainer/common/utils/leaderboard.py b/maestro/trainer/common/utils/leaderboard.py
new file mode 100644
index 0000000..6f381fe
--- /dev/null
+++ b/maestro/trainer/common/utils/leaderboard.py
@@ -0,0 +1,42 @@
+from typing import Dict, Tuple, Optional
+
+
+class CheckpointsLeaderboard:
+
+    def __init__(
+        self,
+        max_checkpoints: int,
+    ):
+        self._max_checkpoints = max(max_checkpoints, 1)
+        self._leaderboard: Dict[int, Tuple[str, float]] = {}
+
+    def register_checkpoint(self, epoch: int, path: str, loss: float) -> Tuple[bool, Optional[str]]:
+        if len(self._leaderboard) < self._max_checkpoints:
+            self._leaderboard[epoch] = (path, loss)
+            return True, None
+        max_loss_key, max_loss_in_leaderboard = None, None
+        for key, (_, loss) in self._leaderboard.items():
+            if max_loss_in_leaderboard is None:
+                max_loss_key = key
+                max_loss_in_leaderboard = loss
+            if loss > max_loss_in_leaderboard:  # type: ignore
+                max_loss_key = key
+                max_loss_in_leaderboard = loss
+        if loss >= max_loss_in_leaderboard:  # type: ignore
+            return False, None
+        to_be_removed, _ = self._leaderboard.pop(max_loss_key)  # type: ignore
+        self._leaderboard[epoch] = (path, loss)
+        return True, to_be_removed
+
+    def get_best_model(self) -> str:
+        min_loss_key, min_loss_in_leaderboard = None, None
+        for key, (_, loss) in self._leaderboard.items():
+            if min_loss_in_leaderboard is None:
+                min_loss_key = key
+                min_loss_in_leaderboard = loss
+            if loss < min_loss_in_leaderboard:  # type: ignore
+                min_loss_key = key
+                min_loss_in_leaderboard = loss
+        if min_loss_key is None:
+            raise RuntimeError("Could not retrieve best model")
+        return self._leaderboard[min_loss_key][0]
diff --git a/maestro/trainer/common/utils/metrics.py b/maestro/trainer/common/utils/metrics.py
new file mode 100644
index 0000000..0d1fb9b
--- /dev/null
+++ b/maestro/trainer/common/utils/metrics.py
@@ -0,0 +1,319 @@
+from __future__ import annotations
+
+import base64
+import html
+import io
+import json
+import os
+from abc import ABC, abstractmethod
+from collections import defaultdict
+from typing import Any, Dict, List, Tuple
+
+import matplotlib.pyplot as plt
+import supervision as sv
+from PIL import Image
+from supervision.metrics.mean_average_precision import MeanAveragePrecision
+
+
+class BaseMetric(ABC):
+    """
+    Abstract base class for custom metrics. Subclasses must implement
+    the 'describe' and 'compute' methods.
+    """
+
+    @abstractmethod
+    def describe(self) -> List[str]:
+        """
+        Describe the names of the metrics that this class will compute.
+
+        Returns:
+            List[str]: A list of metric names that will be computed.
+        """
+        pass
+
+    @abstractmethod
+    def compute(self, targets: List[Any], predictions: List[Any]) -> Dict[str, float]:
+        """
+        Compute the metric based on the targets and predictions.
+
+        Args:
+            targets (List[Any]): The ground truth.
+            predictions (List[Any]): The prediction result.
+
+        Returns:
+            Dict[str, float]: A dictionary of computed metrics with metric names as
+                keys and their values.
+        """
+        pass
+
+
+class MeanAveragePrecisionMetric(BaseMetric):
+    """
+    A class used to compute the Mean Average Precision (mAP) metric.
+    """
+
+    def describe(self) -> List[str]:
+        """
+        Returns a list of metric names that this class will compute.
+
+        Returns:
+            List[str]: A list of metric names.
+        """
+        return ["map50:95", "map50", "map75"]
+
+    def compute(
+        self,
+        targets: List[sv.Detections],
+        predictions: List[sv.Detections]
+    ) -> Dict[str, float]:
+        """
+        Computes the mAP metrics based on the targets and predictions.
+
+        Args:
+            targets (List[sv.Detections]): The ground truth detections.
+            predictions (List[sv.Detections]): The predicted detections.
+
+        Returns:
+            Dict[str, float]: A dictionary of computed mAP metrics with metric names as
+                keys and their values.
+        """
+        result = MeanAveragePrecision().update(
+            targets=targets, predictions=predictions).compute()
+        return {
+            "map50:95": result.map50_95,
+            "map50": result.map50,
+            "map75": result.map75
+        }
+
+
+class MetricsTracker:
+
+    @classmethod
+    def init(cls, metrics: List[str]) -> MetricsTracker:
+        return cls(metrics={metric: [] for metric in metrics})
+
+    def __init__(self, metrics: Dict[str, List[Tuple[int, int, float]]]):
+        self._metrics = metrics
+
+    def register(self, metric: str, epoch: int, step: int, value: float) -> None:
+        self._metrics[metric].append((epoch, step, value))
+
+    def describe_metrics(self) -> List[str]:
+        return list(self._metrics.keys())
+
+    def get_metric_values(
+        self,
+        metric: str,
+        with_index: bool = True,
+    ) -> list:
+        if with_index:
+            return self._metrics[metric]
+        return [value[2] for value in self._metrics[metric]]
+
+    def as_json(
+        self,
+        output_dir: str = None,
+        filename: str = None
+    ) -> Dict[str, List[Dict[str, float]]]:
+        metrics_data = {}
+        for metric, values in self._metrics.items():
+            metrics_data[metric] = [
+                {'epoch': epoch, 'step': step, 'value': value}
+                for epoch, step, value
+                in values
+            ]
+
+        if output_dir and filename:
+            if not os.path.exists(output_dir):
+                os.makedirs(output_dir)
+            filepath = os.path.join(output_dir, filename)
+            with open(filepath, 'w') as file:
+                json.dump(metrics_data, file, indent=4)
+
+        return metrics_data
+
+
+def aggregate_by_epoch(metric_values: List[Tuple[int, int, float]]) -> Dict[int, float]:
+    """
+    Aggregates metric values by epoch, calculating the average for each epoch.
+
+    Args:
+        metric_values (List[Tuple[int, int, float]]): A list of tuples containing
+            (epoch, step, value) for each metric measurement.
+
+    Returns:
+        Dict[int, float]: A dictionary with epochs as keys and average metric values as values.
+    """
+    epoch_data = defaultdict(list)
+    for epoch, step, value in metric_values:
+        epoch_data[epoch].append(value)
+    avg_per_epoch = {
+        epoch: sum(values) / len(values)
+        for epoch, values
+        in epoch_data.items()
+    }
+    return avg_per_epoch
+
+
+def save_metric_plots(
+    training_tracker: MetricsTracker,
+    validation_tracker: MetricsTracker,
+    output_dir: str
+):
+    """
+    Saves plots of training and validation metrics over epochs.
+
+    Args:
+        training_tracker (MetricsTracker): Tracker containing training metrics.
+        validation_tracker (MetricsTracker): Tracker containing validation metrics.
+        output_dir (str): Directory to save the generated plots.
+
+    Returns:
+        None
+    """
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+
+    training_metrics = training_tracker.describe_metrics()
+    validation_metrics = validation_tracker.describe_metrics()
+    all_metrics = set(training_metrics + validation_metrics)
+
+    for metric in all_metrics:
+        plt.figure(figsize=(8, 6))
+
+        if metric in training_metrics:
+            training_values = training_tracker.get_metric_values(
+                metric=metric, with_index=True)
+            training_avg_values = aggregate_by_epoch(training_values)
+            training_epochs = sorted(training_avg_values.keys())
+            training_vals = [training_avg_values[epoch] for epoch in training_epochs]
+            plt.plot(
+                training_epochs,
+                training_vals,
+                label=f'Training {metric}',
+                marker='o',
+                linestyle='-',
+                color='blue'
+            )
+
+        if metric in validation_metrics:
+            validation_values = validation_tracker.get_metric_values(
+                metric=metric, with_index=True)
+            validation_avg_values = aggregate_by_epoch(validation_values)
+            validation_epochs = sorted(validation_avg_values.keys())
+            validation_vals = [
+                validation_avg_values[epoch]
+                for epoch
+                in validation_epochs
+            ]
+            plt.plot(
+                validation_epochs,
+                validation_vals,
+                label=f'Validation {metric}',
+                marker='o',
+                linestyle='--',
+                color='orange'
+            )
+
+        plt.title(f'{metric.capitalize()} over Epochs')
+        plt.xlabel('Epoch')
+        plt.ylabel(f'{metric.capitalize()} Value')
+        plt.legend()
+        plt.grid(True)
+        plt.savefig(f'{output_dir}/{metric}_plot.png')
+        plt.close()
+
+
+
+def display_results(
+    prompts: List[str],
+    expected_responses: List[str],
+    generated_texts: List[str],
+    images: List[Image.Image]
+) -> None:
+    """
+    Display the results of model inference in IPython environments.
+
+    This function attempts to display the results (prompts, expected responses,
+    generated texts, and images) in an HTML format if running in an IPython
+    environment. If not in IPython or if there's an ImportError, it silently passes.
+
+    Args:
+        prompts (List[str]): List of input prompts.
+        expected_responses (List[str]): List of expected responses.
+        generated_texts (List[str]): List of texts generated by the model.
+        images (List[Image.Image]): List of input images.
+
+    Returns:
+        None
+    """
+    try:
+        import IPython
+        if IPython.get_ipython() is not None:
+            from IPython.display import display, HTML
+            html_out = create_html_output(prompts, expected_responses, generated_texts, images)
+            display(HTML(html_out))
+    except ImportError:
+        pass  # Skip visualization if required libraries are not available
+
+
+def create_html_output(
+    prompts: List[str],
+    expected_responses: List[str],
+    generated_texts: List[str],
+    images: List[Image.Image]
+) -> str:
+    """
+    Create an HTML string to display the results of model inference.
+
+    This function generates an HTML string that includes styled divs for each
+    result, containing the input image, prompt, expected response, and generated text.
+
+    Args:
+        prompts (List[str]): List of input prompts.
+        expected_responses (List[str]): List of expected responses.
+        generated_texts (List[str]): List of texts generated by the model.
+        images (List[Image.Image]): List of input images.
+
+    Returns:
+        str: An HTML string containing the formatted results.
+    """
+    html_out = "<style>.result-container{display:flex;margin-bottom:20px;border:1px solid #ddd;padding:10px;}.image-container{flex:0 0 256px;}.text-container{flex:1;margin-left:20px;}.prompt,.expected,.generated{margin-bottom:10px;}</style>"
+    count = min(8, len(images))  # Display up to 8 examples
+    for i in range(count):
+        html_out += f"""
+        <div class="result-container">
+            <div class="image-container">
+                <img style="width:256px; height:256px;" src="{render_inline(images[i])}" />
+            </div>
+            <div class="text-container">
+                <div class="prompt"><strong>Prompt:</strong> {html.escape(prompts[i])}</div>
+                <div class="expected"><strong>Expected:</strong> {html.escape(expected_responses[i])}</div>
+                <div class="generated"><strong>Generated:</strong> {html.escape(generated_texts[i])}</div>
+            </div>
+        </div>
+        """
+    return html_out
+
+
+def render_inline(image: Image.Image, resize: Tuple[int, int] = (256, 256)) -> str:
+    """
+    Convert an image into an inline HTML string.
+
+    This function takes an image, resizes it, and converts it to a base64-encoded
+    string that can be used as the source for an HTML img tag.
+
+    Args:
+        image (Image.Image): The input image to be converted.
+        resize (Tuple[int, int], optional): The dimensions to resize the image to.
+            Defaults to (256, 256).
+
+    Returns:
+        str: A string containing the data URI for the image, ready to be used
+        in an HTML img tag's src attribute.
+    """
+    image = image.resize(resize)
+    with io.BytesIO() as buffer:
+        image.save(buffer, format='jpeg')
+        image_b64 = base64.b64encode(buffer.getvalue()).decode()
+    return f"data:image/jpeg;base64,{image_b64}"
\ No newline at end of file
diff --git a/maestro/trainer/common/utils/reproducibility.py b/maestro/trainer/common/utils/reproducibility.py
new file mode 100644
index 0000000..69e6f1f
--- /dev/null
+++ b/maestro/trainer/common/utils/reproducibility.py
@@ -0,0 +1,24 @@
+import os
+import random
+from typing import Optional
+
+import torch
+import numpy as np
+
+from maestro.trainer.common.configuration.env import SEED_ENV, DEFAULT_SEED
+
+
+def make_it_reproducible(
+    seed: Optional[int] = None,
+    disable_cudnn_benchmark: bool = True,
+    avoid_non_deterministic_algorithms: bool = True,
+) -> None:
+    if seed is None:
+        seed = int(os.getenv(SEED_ENV, DEFAULT_SEED))
+    random.seed(seed)
+    torch.manual_seed(seed)
+    np.random.seed(seed)
+    if avoid_non_deterministic_algorithms:
+        torch.use_deterministic_algorithms(True)
+    if disable_cudnn_benchmark:
+        torch.backends.cudnn.benchmark = False
diff --git a/maestro/trainer/models/__init__.py b/maestro/trainer/models/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/maestro/trainer/models/florence_2/__init__.py b/maestro/trainer/models/florence_2/__init__.py
new file mode 100644
index 0000000..77d45b3
--- /dev/null
+++ b/maestro/trainer/models/florence_2/__init__.py
@@ -0,0 +1,1 @@
+from maestro.trainer.models.florence_2.core import TrainingConfiguration, train
diff --git a/maestro/trainer/models/florence_2/checkpoints.py b/maestro/trainer/models/florence_2/checkpoints.py
new file mode 100644
index 0000000..a489f2b
--- /dev/null
+++ b/maestro/trainer/models/florence_2/checkpoints.py
@@ -0,0 +1,119 @@
+import os
+from typing import Optional, Tuple
+
+import torch
+from transformers import AutoModelForCausalLM, AutoProcessor
+
+from maestro.trainer.common.configuration.env import CUDA_DEVICE_ENV, \
+    DEFAULT_CUDA_DEVICE
+
+DEFAULT_FLORENCE2_MODEL_ID = "microsoft/Florence-2-base-ft"
+DEFAULT_FLORENCE2_MODEL_REVISION = "refs/pr/20"
+DEVICE = torch.device("cpu") \
+    if not torch.cuda.is_available() \
+    else os.getenv(CUDA_DEVICE_ENV, DEFAULT_CUDA_DEVICE)
+
+
+class CheckpointManager:
+    """Manages checkpoints for model training.
+
+    This class handles saving and retrieving model checkpoints during training.
+
+    Attributes:
+        training_dir (str): Directory where checkpoints will be saved.
+        best_val_loss (float): Best validation loss achieved so far.
+        latest_checkpoint_dir (str): Directory for the latest checkpoint.
+        best_checkpoint_dir (str): Directory for the best checkpoint.
+    """
+
+    def __init__(self, training_dir: str):
+        """Initializes the CheckpointManager.
+
+        Args:
+            training_dir (str): Directory where checkpoints will be saved.
+        """
+        self.training_dir = training_dir
+        self.best_val_loss = float('inf')
+        self.latest_checkpoint_dir = os.path.join(training_dir, "checkpoints", "latest")
+        self.best_checkpoint_dir = os.path.join(training_dir, "checkpoints", "best")
+
+    def save_latest(self, processor: AutoProcessor, model: AutoModelForCausalLM):
+        """Saves the latest model checkpoint.
+
+        Args:
+            processor (AutoProcessor): The processor to save.
+            model (AutoModelForCausalLM): The model to save.
+        """
+        save_model(self.latest_checkpoint_dir, processor, model)
+
+    def save_best(self, processor: AutoProcessor, model: AutoModelForCausalLM, val_loss: float):
+        """Saves the best model checkpoint if the validation loss improves.
+
+        Args:
+            processor (AutoProcessor): The processor to save.
+            model (AutoModelForCausalLM): The model to save.
+            val_loss (float): The current validation loss.
+        """
+        if val_loss < self.best_val_loss:
+            self.best_val_loss = val_loss
+            save_model(self.best_checkpoint_dir, processor, model)
+            print(f"New best model saved with validation loss: {self.best_val_loss}")
+
+    def get_best_model_path(self):
+        """Returns the path to the best model checkpoint.
+
+        Returns:
+            str: Path to the best model checkpoint.
+        """
+        return self.best_checkpoint_dir
+
+
+def save_model(
+    target_dir: str,
+    processor: AutoProcessor,
+    model: AutoModelForCausalLM,
+) -> None:
+    """Saves the model and processor to the specified directory.
+
+    Args:
+        target_dir (str): Directory where the model and processor will be saved.
+        processor (AutoProcessor): The processor to save.
+        model (AutoModelForCausalLM): The model to save.
+    """
+    os.makedirs(target_dir, exist_ok=True)
+    processor.save_pretrained(target_dir)
+    model.save_pretrained(target_dir)
+
+
+def load_model(
+    model_id_or_path: str = DEFAULT_FLORENCE2_MODEL_ID,
+    revision: str = DEFAULT_FLORENCE2_MODEL_REVISION,
+    device: torch.device = DEVICE,
+    cache_dir: Optional[str] = None,
+) -> Tuple[AutoProcessor, AutoModelForCausalLM]:
+    """Loads a Florence-2 model and its associated processor.
+
+    Args:
+        model_id_or_path: The identifier or path of the model to load.
+        revision: The specific model revision to use.
+        device: The device to load the model onto.
+        cache_dir: Directory to cache the downloaded model files.
+
+    Returns:
+        A tuple containing the loaded processor and model.
+
+    Raises:
+        ValueError: If the model or processor cannot be loaded.
+    """
+    processor = AutoProcessor.from_pretrained(
+        model_id_or_path,
+        trust_remote_code=True,
+        revision=revision,
+    )
+    model = AutoModelForCausalLM.from_pretrained(
+        model_id_or_path,
+        trust_remote_code=True,
+        revision=revision,
+        cache_dir=cache_dir,
+    ).to(device)
+    return processor, model
diff --git a/maestro/trainer/models/florence_2/core.py b/maestro/trainer/models/florence_2/core.py
new file mode 100644
index 0000000..8ef20ce
--- /dev/null
+++ b/maestro/trainer/models/florence_2/core.py
@@ -0,0 +1,423 @@
+import os
+from dataclasses import dataclass, field, replace
+from typing import List, Literal, Optional, Tuple, Union
+
+import torch
+from peft import LoraConfig, PeftModel, get_peft_model
+from torch.optim import Adam, AdamW, Optimizer, SGD
+from torch.optim.lr_scheduler import LRScheduler
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+from transformers import AutoModelForCausalLM, AutoProcessor, get_scheduler
+
+from maestro.trainer.common.utils.file_system import create_new_run_directory
+from maestro.trainer.common.utils.metrics import (
+    BaseMetric,
+    MetricsTracker,
+    display_results,
+    save_metric_plots,
+    MeanAveragePrecisionMetric
+)
+from maestro.trainer.common.utils.reproducibility import make_it_reproducible
+from maestro.trainer.models.florence_2.checkpoints import (
+    CheckpointManager,
+    load_model,
+    DEFAULT_FLORENCE2_MODEL_ID,
+    DEFAULT_FLORENCE2_MODEL_REVISION,
+    DEVICE
+)
+from maestro.trainer.models.florence_2.data_loading import prepare_data_loaders
+from maestro.trainer.models.florence_2.metrics import (
+    extract_unique_detection_dataset_classes,
+    postprocess_florence2_output_for_mean_average_precision,
+    run_predictions,
+)
+from maestro.trainer.models.paligemma.training import LoraInitLiteral
+
+
+@dataclass(frozen=True)
+class TrainingConfiguration:
+    """Configuration for training a Florence-2 model.
+
+    This class encapsulates all the parameters needed for training a Florence-2 model,
+    including dataset paths, model specifications, training hyperparameters, and output
+    settings.
+
+    Attributes:
+        dataset (str): Path to the dataset used for training.
+        model_id (str): Identifier for the Florence-2 model.
+        revision (str): Revision of the model to use.
+        device (torch.device): Device to use for training.
+        cache_dir (Optional[str]): Directory to cache the model.
+        epochs (int): Number of training epochs.
+        optimizer (Literal["sgd", "adamw", "adam"]): Optimizer to use for training.
+        lr (float): Learning rate for the optimizer.
+        lr_scheduler (Literal["linear", "cosine", "polynomial"]): Learning rate
+            scheduler.
+        batch_size (int): Batch size for training.
+        val_batch_size (Optional[int]): Batch size for validation.
+        num_workers (int): Number of workers for data loading.
+        val_num_workers (Optional[int]): Number of workers for validation data loading.
+        lora_r (int): Rank of the LoRA update matrices.
+        lora_alpha (int): Scaling factor for the LoRA update.
+        lora_dropout (float): Dropout probability for LoRA layers.
+        bias (Literal["none", "all", "lora_only"]): Which bias to train.
+        use_rslora (bool): Whether to use RSLoRA.
+        init_lora_weights (Union[bool, LoraInitLiteral]): How to initialize LoRA
+            weights.
+        output_dir (str): Directory to save output files.
+        metrics (List[BaseMetric]): List of metrics to track during training.
+    """
+    dataset: str
+    model_id: str = DEFAULT_FLORENCE2_MODEL_ID
+    revision: str = DEFAULT_FLORENCE2_MODEL_REVISION
+    device: torch.device = DEVICE
+    cache_dir: Optional[str] = None
+    epochs: int = 10
+    optimizer: Literal["sgd", "adamw", "adam"] = "adamw"
+    lr: float = 1e-5
+    lr_scheduler: Literal["linear", "cosine", "polynomial"] = "linear"
+    batch_size: int = 4
+    val_batch_size: Optional[int] = None
+    num_workers: int = 0
+    val_num_workers: Optional[int] = None
+    lora_r: int = 8
+    lora_alpha: int = 8
+    lora_dropout: float = 0.05
+    bias: Literal["none", "all", "lora_only"] = "none"
+    use_rslora: bool = True
+    init_lora_weights: Union[bool, LoraInitLiteral] = "gaussian"
+    output_dir: str = "./training/florence-2"
+    metrics: List[BaseMetric] = field(default_factory=list)
+
+
+def train(config: TrainingConfiguration) -> None:
+    make_it_reproducible(avoid_non_deterministic_algorithms=False)
+    run_dir = create_new_run_directory(
+        base_output_dir=config.output_dir,
+    )
+    config = replace(
+        config,
+        output_dir=run_dir,
+    )
+    checkpoint_manager = CheckpointManager(run_dir)
+    
+    processor, model = load_model(
+        model_id_or_path=config.model_id,
+        revision=config.revision,
+        device=config.device,
+        cache_dir=config.cache_dir,
+    )
+    train_loader, val_loader, test_loader = prepare_data_loaders(
+        dataset_location=config.dataset,
+        train_batch_size=config.batch_size,
+        processor=processor,
+        device=config.device,
+        num_workers=config.num_workers,
+        test_loaders_workers=config.val_num_workers,
+    )
+    peft_model = prepare_peft_model(
+        model=model,
+        r=config.lora_r,
+        lora_alpha=config.lora_alpha,
+        lora_dropout=config.lora_dropout,
+        bias=config.bias,
+        use_rslora=config.use_rslora,
+        init_lora_weights=config.init_lora_weights,
+        revision=config.revision,
+    )
+    training_metrics_tracker = MetricsTracker.init(metrics=["loss"])
+    metrics = ["loss"]
+    for metric in config.metrics:
+        metrics += metric.describe()
+    validation_metrics_tracker = MetricsTracker.init(metrics=metrics)
+
+    run_training_loop(
+        processor=processor,
+        model=peft_model,
+        data_loaders=(train_loader, val_loader),
+        config=config,
+        training_metrics_tracker=training_metrics_tracker,
+        validation_metrics_tracker=validation_metrics_tracker,
+        checkpoint_manager=checkpoint_manager
+    )
+
+    save_metric_plots(
+        training_tracker=training_metrics_tracker,
+        validation_tracker=validation_metrics_tracker,
+        output_dir=os.path.join(config.output_dir, "metrics"),
+    )
+    training_metrics_tracker.as_json(
+        output_dir=os.path.join(config.output_dir, "metrics"),
+        filename="training.json")
+    validation_metrics_tracker.as_json(
+        output_dir=os.path.join(config.output_dir, "metrics"),
+        filename="validation.json")
+    
+    # Log out paths for latest and best checkpoints
+    print(f"Latest checkpoint saved at: {checkpoint_manager.latest_checkpoint_dir}")
+    print(f"Best checkpoint saved at: {checkpoint_manager.best_checkpoint_dir}")
+
+
+def prepare_peft_model(
+    model: AutoModelForCausalLM,
+    r: int = 8,
+    lora_alpha: int = 8,
+    lora_dropout: float = 0.05,
+    bias: Literal["none", "all", "lora_only"] = "none",
+    inference_mode: bool = False,
+    use_rslora: bool = True,
+    init_lora_weights: Union[bool, LoraInitLiteral] = "gaussian",
+    revision: str = DEFAULT_FLORENCE2_MODEL_REVISION,
+) -> PeftModel:
+    config = LoraConfig(
+        r=r,
+        lora_alpha=lora_alpha,
+        target_modules=["q_proj", "o_proj", "k_proj", "v_proj", "linear", "Conv2d", "lm_head", "fc2"],
+        task_type="CAUSAL_LM",
+        lora_dropout=lora_dropout,
+        bias=bias,
+        inference_mode=inference_mode,
+        use_rslora=use_rslora,
+        init_lora_weights=init_lora_weights,
+        revision=revision,
+    )
+    peft_model = get_peft_model(model, config)
+    peft_model.print_trainable_parameters()
+    return peft_model.to(model.device)
+
+
+def run_training_loop(
+    processor: AutoProcessor,
+    model: PeftModel,
+    data_loaders: Tuple[DataLoader, Optional[DataLoader]],
+    config: TrainingConfiguration,
+    training_metrics_tracker: MetricsTracker,
+    validation_metrics_tracker: MetricsTracker,
+    checkpoint_manager: CheckpointManager,
+) -> None:
+    train_loader, val_loader = data_loaders
+    optimizer = get_optimizer(model=model, config=config)
+    total_steps = config.epochs * len(train_loader)
+    lr_scheduler = get_scheduler(
+        name=config.lr_scheduler,
+        optimizer=optimizer,
+        num_warmup_steps=0,
+        num_training_steps=total_steps,
+    )
+    for epoch in range(config.epochs):
+        run_training_epoch(
+            processor=processor,
+            model=model,
+            train_loader=train_loader,
+            val_loader=val_loader,
+            epoch=epoch + 1,
+            config=config,
+            optimizer=optimizer,
+            lr_scheduler=lr_scheduler,
+            training_metrics_tracker=training_metrics_tracker,
+            validation_metrics_tracker=validation_metrics_tracker,
+            checkpoint_manager=checkpoint_manager
+        )
+
+
+def run_training_epoch(
+    processor: AutoProcessor,
+    model: PeftModel,
+    train_loader: DataLoader,
+    val_loader: Optional[DataLoader],
+    epoch: int,
+    config: TrainingConfiguration,
+    optimizer: Optimizer,
+    lr_scheduler: LRScheduler,
+    training_metrics_tracker: MetricsTracker,
+    validation_metrics_tracker: MetricsTracker,
+    checkpoint_manager: CheckpointManager,
+) -> None:
+    model.train()
+    training_losses: List[float] = []
+    
+    with tqdm(total=len(train_loader), desc=f"Epoch {epoch}/{config.epochs}", unit="batch") as pbar:
+        for step_id, (inputs, answers) in enumerate(train_loader):
+            input_ids = inputs["input_ids"]
+            pixel_values = inputs["pixel_values"]
+            labels = processor.tokenizer(
+                text=answers, 
+                return_tensors="pt", 
+                padding=True, 
+                return_token_type_ids=False
+            ).input_ids.to(config.device)
+            outputs = model(input_ids=input_ids, pixel_values=pixel_values, labels=labels)
+            loss = outputs.loss
+            loss.backward()
+            optimizer.step()
+            lr_scheduler.step()
+            optimizer.zero_grad()
+            loss = loss.item()
+            training_metrics_tracker.register(
+                metric="loss",
+                epoch=epoch,
+                step=step_id + 1,
+                value=loss,
+            )
+            training_losses.append(loss)
+            
+            # Update progress bar
+            last_100_losses = training_losses[-100:]
+            loss_moving_average = sum(last_100_losses) / len(last_100_losses) if last_100_losses else 0.0
+            pbar.set_postfix({"Loss": f"{loss_moving_average:.4f}"})
+            pbar.update(1)
+        
+    # Save checkpoints based on training loss if no validation loader
+    if val_loader is None or len(val_loader) == 0:
+        train_loss = sum(training_losses) / len(training_losses)
+        checkpoint_manager.save_latest(processor, model)
+        checkpoint_manager.save_best(processor, model, train_loss)
+        return
+
+    run_validation_epoch(
+        processor=processor,
+        model=model,
+        loader=val_loader,
+        epoch_number=epoch,
+        config=config,
+        metrics_tracker=validation_metrics_tracker,
+    )
+    
+    val_loss = validation_metrics_tracker.get_metric_values("loss")[-1][2]
+    checkpoint_manager.save_latest(processor, model)
+    checkpoint_manager.save_best(processor, model, val_loss)
+
+
+def run_validation_epoch(
+    processor: AutoProcessor,
+    model: Union[PeftModel, AutoModelForCausalLM],
+    loader: DataLoader,
+    config: TrainingConfiguration,
+    metrics_tracker: MetricsTracker,
+    epoch_number: int
+) -> None:
+    val_loss = 0.0
+    with torch.no_grad():
+        for inputs, targets in loader:
+            input_ids = inputs["input_ids"]
+            pixel_values = inputs["pixel_values"]
+            labels = processor.tokenizer(
+                text=targets,
+                return_tensors="pt",
+                padding=True,
+                return_token_type_ids=False
+            ).input_ids.to(config.device)
+            outputs = model(
+                input_ids=input_ids,
+                pixel_values=pixel_values,
+                labels=labels
+            )
+            loss = outputs.loss
+            val_loss += loss.item()
+        avg_val_loss = val_loss / len(loader)
+        metrics_tracker.register(
+            metric="loss",
+            epoch=epoch_number,
+            step=1,
+            value=avg_val_loss,
+        )
+        # Run inference once for all metrics
+        prompts, expected_responses, generated_texts, images = run_predictions(
+            dataset=loader.dataset,
+            processor=processor,
+            model=model,
+            device=config.device,
+        )
+        
+        metrics_results = {"loss": avg_val_loss}
+        
+        for metric in config.metrics:
+            if isinstance(metric, MeanAveragePrecisionMetric):
+                classes = extract_unique_detection_dataset_classes(loader.dataset)
+                targets, predictions = postprocess_florence2_output_for_mean_average_precision(
+                    expected_responses=expected_responses,
+                    generated_texts=generated_texts,
+                    images=images,
+                    classes=classes,
+                    processor=processor
+                )
+                result = metric.compute(targets=targets, predictions=predictions)
+                for key, value in result.items():
+                    metrics_tracker.register(
+                        metric=key,
+                        epoch=epoch_number,
+                        step=1,
+                        value=value,
+                    )
+                    metrics_results[key] = value
+        
+        print("Validation Metrics:", ", ".join([f"{k}: {v:.4f}" for k, v in metrics_results.items()]))
+
+        # Display inference results in IPython environments
+        display_results(prompts, expected_responses, generated_texts, images)
+
+
+def get_optimizer(model: PeftModel, config: TrainingConfiguration) -> Optimizer:
+    optimizer_type = config.optimizer.lower()
+    if optimizer_type == "adamw":
+        return AdamW(model.parameters(), lr=config.lr)
+    if optimizer_type == "adam":
+        return Adam(model.parameters(), lr=config.lr)
+    if optimizer_type == "sgd":
+        return SGD(model.parameters(), lr=config.lr)
+    raise ValueError(f"Unsupported optimizer: {config.optimizer}")
+
+
+def evaluate(config: TrainingConfiguration) -> None:
+    processor, model = load_model(
+        model_id_or_path=config.model_id,
+        revision=config.revision,
+        device=config.device,
+        cache_dir=config.cache_dir,
+    )
+    train_loader, val_loader, test_loader = prepare_data_loaders(
+        dataset_location=config.dataset,
+        train_batch_size=config.batch_size,
+        processor=processor,
+        device=config.device,
+        num_workers=config.num_workers,
+        test_loaders_workers=config.val_num_workers,
+    )
+    evaluation_loader = test_loader if test_loader is not None else val_loader
+    
+    metrics = []
+    for metric in config.metrics:
+        metrics += metric.describe()
+    evaluation_metrics_tracker = MetricsTracker.init(metrics=metrics)
+
+    # Run inference once for all metrics
+    _, expected_responses, generated_texts, images = run_predictions(
+        dataset=evaluation_loader.dataset,
+        processor=processor,
+        model=model,
+        device=config.device,
+    )
+
+    for metric in config.metrics:
+        if isinstance(metric, MeanAveragePrecisionMetric):
+            classes = extract_unique_detection_dataset_classes(train_loader.dataset)
+            targets, predictions = postprocess_florence2_output_for_mean_average_precision(
+                expected_responses=expected_responses,
+                generated_texts=generated_texts,
+                images=images,
+                classes=classes,
+                processor=processor
+            )
+            result = metric.compute(targets=targets, predictions=predictions)
+            for key, value in result.items():
+                evaluation_metrics_tracker.register(
+                    metric=key,
+                    epoch=1,
+                    step=1,
+                    value=value,
+                )
+
+    evaluation_metrics_tracker.as_json(
+        output_dir=os.path.join(config.output_dir, "metrics"),
+        filename="evaluation.json")
diff --git a/maestro/trainer/models/florence_2/data_loading.py b/maestro/trainer/models/florence_2/data_loading.py
new file mode 100644
index 0000000..4873003
--- /dev/null
+++ b/maestro/trainer/models/florence_2/data_loading.py
@@ -0,0 +1,110 @@
+import logging
+import os
+from functools import partial
+from typing import Optional, Tuple, List
+
+import torch
+from PIL import Image
+from torch.utils.data import DataLoader
+from transformers import AutoProcessor
+
+from maestro.trainer.common.data_loaders.datasets import DetectionDataset
+
+
+def prepare_data_loaders(
+    dataset_location: str,
+    train_batch_size: int,
+    processor: AutoProcessor,
+    device: torch.device,
+    num_workers: int = 0,
+    test_batch_size: Optional[int] = None,
+    test_loaders_workers: Optional[int] = None,
+) -> Tuple[
+    DataLoader,
+    Optional[DataLoader],
+    Optional[DataLoader],
+]:
+    test_batch_size = test_batch_size or train_batch_size
+    test_loaders_workers = test_loaders_workers or num_workers
+    train_data_loader = prepare_detection_data_loader(
+        dataset_location=dataset_location,
+        split_name="train",
+        batch_size=train_batch_size,
+        processor=processor,
+        device=device,
+        num_workers=num_workers,
+        shuffle=True,
+    )
+    if train_data_loader is None:
+        raise RuntimeError("Could not initialise train data loader")
+    valid_data_loader = prepare_detection_data_loader(
+        dataset_location=dataset_location,
+        split_name="valid",
+        batch_size=test_batch_size,
+        processor=processor,
+        device=device,
+        num_workers=test_loaders_workers,
+        shuffle=False,
+    )
+    test_data_loader = prepare_detection_data_loader(
+        dataset_location=dataset_location,
+        split_name="test",
+        batch_size=test_batch_size,
+        processor=processor,
+        device=device,
+        num_workers=test_loaders_workers,
+        shuffle=False,
+    )
+    return train_data_loader, valid_data_loader, test_data_loader
+
+
+def prepare_detection_data_loader(
+    dataset_location: str,
+    split_name: str,
+    batch_size: int,
+    processor: AutoProcessor,
+    device: torch.device,
+    num_workers: int = 0,
+    shuffle: bool = True,
+) -> Optional[DataLoader]:
+    dataset = prepare_detection_dataset(
+        dataset_location=dataset_location,
+        split_name=split_name,
+    )
+    if dataset is None:
+        return None
+    return DataLoader(
+        dataset,
+        batch_size=batch_size,
+        collate_fn=partial(collate_fn, processor=processor, device=device),
+        num_workers=num_workers,
+        shuffle=shuffle,
+    )
+
+
+def prepare_detection_dataset(
+    dataset_location: str,
+    split_name: str,
+) -> Optional[DetectionDataset]:
+    image_directory_path = os.path.join(dataset_location, split_name)
+    jsonl_file_path = os.path.join(dataset_location, split_name, "annotations.jsonl")
+    if not os.path.exists(image_directory_path):
+        logging.warning(f"Could not data directory: {image_directory_path}")
+        return None
+    if not os.path.exists(jsonl_file_path):
+        logging.warning(f"Could not find JSONL file: {jsonl_file_path}")
+        return None
+    return DetectionDataset(
+        jsonl_file_path=jsonl_file_path,
+        image_directory_path=image_directory_path,
+    )
+
+
+def collate_fn(
+    batch: Tuple[List[str], List[str], List[Image.Image]],
+    processor: AutoProcessor,
+    device: torch.device,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    questions, answers, images = zip(*batch)
+    inputs = processor(text=list(questions), images=list(images), return_tensors="pt", padding=True).to(device)
+    return inputs, answers
diff --git a/maestro/trainer/models/florence_2/entrypoint.py b/maestro/trainer/models/florence_2/entrypoint.py
new file mode 100644
index 0000000..f18d81e
--- /dev/null
+++ b/maestro/trainer/models/florence_2/entrypoint.py
@@ -0,0 +1,219 @@
+import dataclasses
+from typing import Optional, Annotated, List, Dict, Type
+
+import rich
+import torch
+import typer
+
+from maestro.trainer.models.florence_2.checkpoints import DEFAULT_FLORENCE2_MODEL_ID, \
+    DEFAULT_FLORENCE2_MODEL_REVISION, DEVICE
+from maestro.trainer.models.florence_2.core import TrainingConfiguration
+from maestro.trainer.models.florence_2.core import train as florence2_train
+from maestro.trainer.models.florence_2.core import evaluate as florence2_evaluate
+from maestro.trainer.common.utils.metrics import BaseMetric, MeanAveragePrecisionMetric
+
+florence_2_app = typer.Typer(help="Fine-tune and evaluate Florence 2 model")
+
+
+METRIC_CLASSES: Dict[str, Type[BaseMetric]] = {
+    "mean_average_precision": MeanAveragePrecisionMetric,
+}
+
+
+def parse_metrics(metrics: List[str]) -> List[BaseMetric]:
+    metric_objects = []
+    for metric_name in metrics:
+        metric_class = METRIC_CLASSES.get(metric_name.lower())
+        if metric_class:
+            metric_objects.append(metric_class())
+        else:
+            raise ValueError(f"Unsupported metric: {metric_name}")
+    return metric_objects
+
+
+@florence_2_app.command(
+    help="Train Florence 2 model",
+    context_settings={"allow_extra_args": True, "ignore_unknown_options": True}
+)
+def train(
+    dataset: Annotated[
+        str,
+        typer.Option("--dataset", help="Path to the dataset used for training"),
+    ],
+    model_id: Annotated[
+        str,
+        typer.Option("--model_id", help="Identifier for the Florence-2 model"),
+    ] = DEFAULT_FLORENCE2_MODEL_ID,
+    revision: Annotated[
+        str,
+        typer.Option("--revision", help="Revision of the model to use"),
+    ] = DEFAULT_FLORENCE2_MODEL_REVISION,
+    device: Annotated[
+        str,
+        typer.Option("--device", help="Device to use for training"),
+    ] = DEVICE,
+    cache_dir: Annotated[
+        Optional[str],
+        typer.Option("--cache_dir", help="Directory to cache the model"),
+    ] = None,
+    epochs: Annotated[
+        int,
+        typer.Option("--epochs", help="Number of training epochs"),
+    ] = 10,
+    optimizer: Annotated[
+        str,
+        typer.Option("--optimizer", help="Optimizer to use for training"),
+    ] = "adamw",
+    lr: Annotated[
+        float,
+        typer.Option("--lr", help="Learning rate for the optimizer"),
+    ] = 1e-5,
+    lr_scheduler: Annotated[
+        str,
+        typer.Option("--lr_scheduler", help="Learning rate scheduler"),
+    ] = "linear",
+    batch_size: Annotated[
+        int,
+        typer.Option("--batch_size", help="Batch size for training"),
+    ] = 4,
+    val_batch_size: Annotated[
+        Optional[int],
+        typer.Option("--val_batch_size", help="Batch size for validation"),
+    ] = None,
+    num_workers: Annotated[
+        int,
+        typer.Option("--num_workers", help="Number of workers for data loading"),
+    ] = 0,
+    val_num_workers: Annotated[
+        Optional[int],
+        typer.Option("--val_num_workers", help="Number of workers for validation data loading"),
+    ] = None,
+    lora_r: Annotated[
+        int,
+        typer.Option("--lora_r", help="Rank of the LoRA update matrices"),
+    ] = 8,
+    lora_alpha: Annotated[
+        int,
+        typer.Option("--lora_alpha", help="Scaling factor for the LoRA update"),
+    ] = 8,
+    lora_dropout: Annotated[
+        float,
+        typer.Option("--lora_dropout", help="Dropout probability for LoRA layers"),
+    ] = 0.05,
+    bias: Annotated[
+        str,
+        typer.Option("--bias", help="Which bias to train"),
+    ] = "none",
+    use_rslora: Annotated[
+        bool,
+        typer.Option("--use_rslora/--no_use_rslora", help="Whether to use RSLoRA"),
+    ] = True,
+    init_lora_weights: Annotated[
+        str,
+        typer.Option("--init_lora_weights", help="How to initialize LoRA weights"),
+    ] = "gaussian",
+    output_dir: Annotated[
+        str,
+        typer.Option("--output_dir", help="Directory to save output files"),
+    ] = "./training/florence-2",
+    metrics: Annotated[
+        List[str],
+        typer.Option("--metrics", help="List of metrics to track during training"),
+    ] = [],
+) -> None:
+    metric_objects = parse_metrics(metrics)
+    config = TrainingConfiguration(
+        dataset=dataset,
+        model_id=model_id,
+        revision=revision,
+        device=torch.device(device),
+        cache_dir=cache_dir,
+        epochs=epochs,
+        optimizer=optimizer,
+        lr=lr,
+        lr_scheduler=lr_scheduler,
+        batch_size=batch_size,
+        val_batch_size=val_batch_size,
+        num_workers=num_workers,
+        val_num_workers=val_num_workers,
+        lora_r=lora_r,
+        lora_alpha=lora_alpha,
+        lora_dropout=lora_dropout,
+        bias=bias,
+        use_rslora=use_rslora,
+        init_lora_weights=init_lora_weights,
+        output_dir=output_dir,
+        metrics=metric_objects
+    )
+    typer.echo(typer.style(
+        text="Training configuration",
+        fg=typer.colors.BRIGHT_GREEN,
+        bold=True
+    ))
+    rich.print(dataclasses.asdict(config))
+    florence2_train(config=config)
+
+
+@florence_2_app.command(help="Evaluate Florence 2 model")
+def evaluate(
+    dataset: Annotated[
+        str,
+        typer.Option("--dataset", help="Path to the dataset used for evaluation"),
+    ],
+    model_id: Annotated[
+        str,
+        typer.Option("--model_id", help="Identifier for the Florence-2 model"),
+    ] = DEFAULT_FLORENCE2_MODEL_ID,
+    revision: Annotated[
+        str,
+        typer.Option("--revision", help="Revision of the model to use"),
+    ] = DEFAULT_FLORENCE2_MODEL_REVISION,
+    device: Annotated[
+        str,
+        typer.Option("--device", help="Device to use for evaluation"),
+    ] = DEVICE,
+    cache_dir: Annotated[
+        Optional[str],
+        typer.Option("--cache_dir", help="Directory to cache the model"),
+    ] = None,
+    batch_size: Annotated[
+        int,
+        typer.Option("--batch_size", help="Batch size for evaluation"),
+    ] = 4,
+    num_workers: Annotated[
+        int,
+        typer.Option("--num_workers", help="Number of workers for data loading"),
+    ] = 0,
+    val_num_workers: Annotated[
+        Optional[int],
+        typer.Option("--val_num_workers", help="Number of workers for validation data loading"),
+    ] = None,
+    output_dir: Annotated[
+        str,
+        typer.Option("--output_dir", help="Directory to save output files"),
+    ] = "./evaluation/florence-2",
+    metrics: Annotated[
+        List[str],
+        typer.Option("--metrics", help="List of metrics to track during evaluation"),
+    ] = [],
+) -> None:
+    metric_objects = parse_metrics(metrics)
+    config = TrainingConfiguration(
+        dataset=dataset,
+        model_id=model_id,
+        revision=revision,
+        device=torch.device(device),
+        cache_dir=cache_dir,
+        batch_size=batch_size,
+        num_workers=num_workers,
+        val_num_workers=val_num_workers,
+        output_dir=output_dir,
+        metrics=metric_objects
+    )
+    typer.echo(typer.style(
+        text="Evaluation configuration",
+        fg=typer.colors.BRIGHT_GREEN,
+        bold=True
+    ))
+    rich.print(dataclasses.asdict(config))
+    florence2_evaluate(config=config)
diff --git a/maestro/trainer/models/florence_2/metrics.py b/maestro/trainer/models/florence_2/metrics.py
new file mode 100644
index 0000000..09e02bd
--- /dev/null
+++ b/maestro/trainer/models/florence_2/metrics.py
@@ -0,0 +1,83 @@
+import re
+from typing import List
+from typing import Tuple
+
+import numpy as np
+import supervision as sv
+import torch
+from PIL import Image
+from tqdm import tqdm
+from transformers import AutoProcessor, AutoModelForCausalLM
+
+from maestro.trainer.common.data_loaders.datasets import DetectionDataset
+
+DETECTION_CLASS_PATTERN = r"([a-zA-Z0-9 -]+)<loc_\d+>"
+
+
+def postprocess_florence2_output_for_mean_average_precision(
+    expected_responses: List[str],
+    generated_texts: List[str],
+    images: List[Image.Image],
+    classes: List[str],
+    processor: AutoProcessor
+) -> Tuple[List[sv.Detections], List[sv.Detections]]:
+    targets = []
+    predictions = []
+    
+    for image, suffix, generated_text in zip(images, expected_responses, generated_texts):
+        # Postprocess prediction for mean average precision calculation
+        prediction = processor.post_process_generation(generated_text, task="<OD>", image_size=image.size)
+        prediction = sv.Detections.from_lmm(sv.LMM.FLORENCE_2, prediction, resolution_wh=image.size)
+        prediction = prediction[np.isin(prediction["class_name"], classes)]
+        prediction.class_id = np.array([classes.index(class_name) for class_name in prediction["class_name"]])
+        # Set confidence for mean average precision calculation
+        prediction.confidence = np.ones(len(prediction))
+        
+        # Postprocess target for mean average precision calculation
+        target = processor.post_process_generation(suffix, task="<OD>", image_size=image.size)
+        target = sv.Detections.from_lmm(sv.LMM.FLORENCE_2, target, resolution_wh=image.size)
+        target.class_id = np.array([classes.index(class_name) for class_name in target["class_name"]])
+        
+        targets.append(target)
+        predictions.append(prediction)
+    
+    return targets, predictions
+
+
+def run_predictions(
+    dataset: DetectionDataset,
+    processor: AutoProcessor,
+    model: AutoModelForCausalLM,
+    device: torch.device,
+) -> Tuple[List[str], List[str], List[str], List[Image.Image]]:
+    prompts = []
+    expected_responses = []
+    generated_texts = []
+    images = []
+    
+    for idx in tqdm(list(range(len(dataset))), desc="Generating predictions..."):
+        image, data = dataset.dataset[idx]
+        prefix = data["prefix"]
+        suffix = data["suffix"]
+
+        inputs = processor(text=prefix, images=image, return_tensors="pt").to(device)
+        generated_ids = model.generate(
+            input_ids=inputs["input_ids"], pixel_values=inputs["pixel_values"], max_new_tokens=1024, num_beams=3
+        )
+        generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
+        
+        prompts.append(prefix)
+        expected_responses.append(suffix)
+        generated_texts.append(generated_text)
+        images.append(image)
+    
+    return prompts, expected_responses, generated_texts, images
+
+
+def extract_unique_detection_dataset_classes(dataset: DetectionDataset) -> List[str]:
+    class_set = set()
+    for i in range(len(dataset)):
+        _, suffix, _ = dataset[i]
+        classes = re.findall(DETECTION_CLASS_PATTERN, suffix)
+        class_set.update(classes)
+    return sorted(class_set)
diff --git a/maestro/trainer/models/paligemma/__init__.py b/maestro/trainer/models/paligemma/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/maestro/trainer/models/paligemma/entrypoint.py b/maestro/trainer/models/paligemma/entrypoint.py
new file mode 100644
index 0000000..6ef6dc6
--- /dev/null
+++ b/maestro/trainer/models/paligemma/entrypoint.py
@@ -0,0 +1,13 @@
+import typer
+
+paligemma_app = typer.Typer(help="Fine-tune and evaluate PaliGemma model")
+
+
+@paligemma_app.command(help="Train PaliGemma model")
+def train() -> None:
+    typer.echo("🚧 Just a placeholder - to be implemented 🚧")
+
+
+@paligemma_app.command(help="Evaluate PaliGemma model")
+def evaluate() -> None:
+    typer.echo("🚧 Just a placeholder - to be implemented 🚧")
diff --git a/maestro/trainer/models/paligemma/training.py b/maestro/trainer/models/paligemma/training.py
new file mode 100644
index 0000000..9de0f0c
--- /dev/null
+++ b/maestro/trainer/models/paligemma/training.py
@@ -0,0 +1,222 @@
+import os
+from typing import List, Tuple, Optional, Literal, Union, Iterator
+
+from PIL import Image
+from torch import optim
+from torch.optim import Optimizer
+from torch.optim.lr_scheduler import LRScheduler
+from tqdm import tqdm
+from transformers import AutoProcessor, PaliGemmaForConditionalGeneration
+import torch
+from peft import LoraConfig, get_peft_model, PeftModel
+
+from maestro.trainer.common.configuration.env import (
+    CUDA_DEVICE_ENV,
+    DEFAULT_CUDA_DEVICE,
+    HF_TOKEN_ENV,
+)
+from maestro.trainer.common.data_loaders.jsonl import JSONLDataset
+from maestro.trainer.common.utils.metrics import MetricsTracker
+from maestro.trainer.common.utils.reproducibility import make_it_reproducible
+
+DEFAULT_PALIGEMMA_MODEL_ID = "google/paligemma-3b-pt-224"
+DEVICE = torch.device("cpu") if not torch.cuda.is_available() else os.getenv(CUDA_DEVICE_ENV, DEFAULT_CUDA_DEVICE)
+
+LoraInitLiteral = Literal["gaussian", "olora", "pissa", "pissa_niter_[number of iters]", "loftq"]
+
+
+def train(
+    model: PaliGemmaForConditionalGeneration,
+    processor: AutoProcessor,
+    train_dataset: JSONLDataset,
+    dataset_root: str,
+    batch_size: int,
+    epochs: int,
+    learning_rate: float,
+    device: torch.device = DEVICE,
+) -> MetricsTracker:
+    make_it_reproducible()
+    if device.type == "cup":
+        raise RuntimeError("PaliGemma training process requires GPU")
+    metrics_tracker = MetricsTracker.init(metrics=["loss"])
+    peft_model = prepare_peft_model(model=model).train()
+    train_steps = len(train_dataset) // batch_size
+    with torch.amp.autocast(device.type, torch.float16):
+        lora_layers = filter(lambda p: p.requires_grad, peft_model.parameters())
+        optimizer = optim.SGD(lora_layers, lr=learning_rate)
+        scheduler = optim.lr_scheduler.CosineAnnealingLR(
+            optimizer,
+            epochs * train_steps + 1,
+            eta_min=learning_rate / 10,
+        )
+        run_training_epochs(
+            peft_model=peft_model,
+            processor=processor,
+            epochs=epochs,
+            train_steps=train_steps,
+            train_dataset=train_dataset,
+            batch_size=batch_size,
+            dataset_root=dataset_root,
+            optimizer=optimizer,
+            scheduler=scheduler,
+            metrics_tracker=metrics_tracker,
+        )
+    return metrics_tracker
+
+
+def run_training_epochs(
+    peft_model: PeftModel,
+    processor: AutoProcessor,
+    epochs: int,
+    train_steps: int,
+    train_dataset: JSONLDataset,
+    batch_size: int,
+    dataset_root: str,
+    optimizer: Optimizer,
+    scheduler: LRScheduler,
+    metrics_tracker: MetricsTracker,
+) -> None:
+    for epoch in tqdm(range(epochs), desc="EPOCHS"):
+        train_dataset.shuffle()
+        dataset_iterator = iter(train_dataset)
+        progress_bar = tqdm(range(train_steps), desc="STEPS")
+        run_training_steps(
+            peft_model=peft_model,
+            processor=processor,
+            epoch=epoch,
+            train_steps=train_steps,
+            batch_size=batch_size,
+            dataset_iterator=dataset_iterator,
+            dataset_root=dataset_root,
+            optimizer=optimizer,
+            scheduler=scheduler,
+            metrics_tracker=metrics_tracker,
+            progress_bar=progress_bar,
+        )
+
+
+def run_training_steps(
+    peft_model: PeftModel,
+    processor: AutoProcessor,
+    epoch: int,
+    train_steps: int,
+    batch_size: int,
+    dataset_iterator: Iterator[dict],
+    dataset_root: str,
+    optimizer: Optimizer,
+    scheduler: LRScheduler,
+    metrics_tracker: MetricsTracker,
+    progress_bar,
+) -> None:
+    for step in range(1, train_steps + 1):
+        batch = collect_batch(
+            batch_size=batch_size,
+            dataset_iterator=dataset_iterator,
+            dataset_root=dataset_root,
+            processor=processor,
+        )
+        loss_tensor = peft_model(**batch)["loss"]
+        loss_tensor.backward()
+        loss = loss_tensor.cpu().detach().numpy()
+        optimizer.step()
+        optimizer.zero_grad()
+        scheduler.step()
+        progress_bar.update(1)
+        progress_bar.set_description(f"Loss: {loss}")
+        metrics_tracker.register(metric="loss", epoch=epoch, step=step, value=loss)
+
+
+def collect_batch(
+    batch_size: int,
+    dataset_iterator: Iterator[dict],
+    dataset_root: str,
+    processor: AutoProcessor,
+) -> torch.Tensor:
+    with torch.no_grad():
+        examples = []
+        for _ in range(batch_size):
+            examples.append(next(dataset_iterator))
+        return _collate_fn(
+            examples=examples,
+            dataset_root=dataset_root,
+            processor=processor,
+        )
+
+
+def load_model(
+    model_id: str = DEFAULT_PALIGEMMA_MODEL_ID,
+    revision: str = "float16",
+    device: torch.device = DEVICE,
+    hf_token: Optional[str] = None,
+    cache_dir: Optional[str] = None,
+) -> Tuple[AutoProcessor, PaliGemmaForConditionalGeneration]:
+    if hf_token is None:
+        hf_token = os.getenv(HF_TOKEN_ENV)
+    processor = AutoProcessor.from_pretrained(model_id, token=hf_token, cache_dir=cache_dir)
+    model = PaliGemmaForConditionalGeneration.from_pretrained(
+        model_id,
+        revision=revision,
+        device_map=device,
+        cache_dir=cache_dir,
+        token=hf_token,
+        torch_dtype=torch.float16,
+    ).eval()
+    return processor, model
+
+
+def prepare_peft_model(
+    model: PaliGemmaForConditionalGeneration,
+    r: int = 12,
+    lora_alpha: int = 12,
+    lora_dropout: float = 0.05,
+    bias: Literal["none", "all", "lora_only"] = "none",
+    inference_mode: bool = False,
+    use_rslora: bool = True,
+    init_lora_weights: Union[bool, LoraInitLiteral] = "gaussian",
+    revision: str = "float16",
+    device: torch.device = DEVICE,
+) -> PeftModel:
+    config = LoraConfig(
+        r=r,
+        lora_alpha=lora_alpha,
+        target_modules=["q_proj", "o_proj", "k_proj", "v_proj", "linear"],
+        task_type="CAUSAL_LM",
+        lora_dropout=lora_dropout,
+        bias=bias,
+        inference_mode=inference_mode,
+        use_rslora=use_rslora,
+        init_lora_weights=init_lora_weights,
+        revision=revision,
+    )
+    return get_peft_model(model, config).to(device)
+
+
+def _collate_fn(
+    examples: List[dict],
+    dataset_root: str,
+    processor: AutoProcessor,
+    device: torch.device = DEVICE,
+    image_file_key: str = "image",
+    prefix_key: str = "prefix",
+    suffix_key: str = "suffix",
+) -> torch.Tensor:
+    images = [
+        _load_image_from_dataset(
+            image_name=example[image_file_key],
+            dataset_root=dataset_root,
+        )
+        for example in examples
+    ]
+    tokens = processor(
+        text=[example[prefix_key] for example in examples],
+        suffix=[example[suffix_key] for example in examples],
+        images=images,
+        return_tensors="pt",
+        padding="longest",
+    )
+    return tokens.to(device)
+
+
+def _load_image_from_dataset(image_name: str, dataset_root: str) -> Image.Image:
+    image_path = os.path.join(dataset_root, "dataset", image_name)
+    return Image.open(image_path).convert("RGB")
diff --git a/mypy.ini b/mypy.ini
new file mode 100644
index 0000000..61a34b0
--- /dev/null
+++ b/mypy.ini
@@ -0,0 +1,2 @@
+[mypy-requests]
+ignore_missing_imports = True
diff --git a/poetry.lock b/poetry.lock
deleted file mode 100644
index 732c059..0000000
--- a/poetry.lock
+++ /dev/null
@@ -1,1867 +0,0 @@
-# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand.
-
-[[package]]
-name = "babel"
-version = "2.13.1"
-description = "Internationalization utilities"
-optional = false
-python-versions = ">=3.7"
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-    {file = "Babel-2.13.1.tar.gz", hash = "sha256:33e0952d7dd6374af8dbf6768cc4ddf3ccfefc244f9986d4074704f2fbd18900"},
-]
-
-[package.dependencies]
-pytz = {version = ">=2015.7", markers = "python_version < \"3.9\""}
-
-[package.extras]
-dev = ["freezegun (>=1.0,<2.0)", "pytest (>=6.0)", "pytest-cov"]
-
-[[package]]
-name = "certifi"
-version = "2023.11.17"
-description = "Python package for providing Mozilla's CA Bundle."
-optional = false
-python-versions = ">=3.6"
-files = [
-    {file = "certifi-2023.11.17-py3-none-any.whl", hash = "sha256:e036ab49d5b79556f99cfc2d9320b34cfbe5be05c5871b51de9329f0603b0474"},
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-]
-
-[[package]]
-name = "charset-normalizer"
-version = "3.3.2"
-description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
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-    {file = "watchdog-3.0.0.tar.gz", hash = "sha256:4d98a320595da7a7c5a18fc48cb633c2e73cda78f93cac2ef42d42bf609a33f9"},
-]
-
-[package.extras]
-watchmedo = ["PyYAML (>=3.10)"]
-
-[[package]]
-name = "zipp"
-version = "3.17.0"
-description = "Backport of pathlib-compatible object wrapper for zip files"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "zipp-3.17.0-py3-none-any.whl", hash = "sha256:0e923e726174922dce09c53c59ad483ff7bbb8e572e00c7f7c46b88556409f31"},
-    {file = "zipp-3.17.0.tar.gz", hash = "sha256:84e64a1c28cf7e91ed2078bb8cc8c259cb19b76942096c8d7b84947690cabaf0"},
-]
-
-[package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
-testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"]
-
-[metadata]
-lock-version = "2.0"
-python-versions = ">=3.8,<3.12.0"
-content-hash = "0dc130ed9149c05685afdd1b072e746263e0497e691cab4903ac0af506dcbcb0"
diff --git a/pyproject.toml b/pyproject.toml
deleted file mode 100644
index 26a2983..0000000
--- a/pyproject.toml
+++ /dev/null
@@ -1,52 +0,0 @@
-[tool.poetry]
-name = "maestro"
-version = "0.1.1rc1"
-description = "Visual Prompting for Large Multimodal Models (LMMs)"
-authors = ["Piotr Skalski <piotr.skalski92@gmail.com>"]
-maintainers = ["Piotr Skalski <piotr.skalski92@gmail.com>"]
-readme = "README.md"
-license = "MIT"
-packages = [{include = "maestro"}]
-homepage = "https://github.com/roboflow/multimodal-maestro"
-repository = "https://github.com/roboflow/multimodal-maestro"
-documentation = "https://roboflow.github.io/multimodal-maestro"
-
-classifiers=[
-    'Development Status :: 3 - Alpha',
-    'Intended Audience :: Developers',
-    'Intended Audience :: Education',
-    'Intended Audience :: Science/Research',
-    'License :: OSI Approved :: MIT License',
-    'Programming Language :: Python :: 3',
-    'Programming Language :: Python :: 3.8',
-    'Programming Language :: Python :: 3.9',
-    'Programming Language :: Python :: 3.10',
-    'Programming Language :: Python :: 3.11',
-    'Programming Language :: Python :: 3 :: Only',
-    'Topic :: Software Development',
-    'Topic :: Scientific/Engineering',
-    "Topic :: Scientific/Engineering :: Artificial Intelligence",
-    'Typing :: Typed',
-    'Operating System :: Microsoft :: Windows',
-    'Operating System :: POSIX :: Linux',
-    'Operating System :: MacOS'
-]
-
-[tool.poetry.dependencies]
-python = ">=3.8,<3.12.0"
-supervision = "^0.17.0rc4"
-requests = "^2.31.0"
-transformers = "^4.35.2"
-
-[tool.poetry.group.dev.dependencies]
-pytest = "^7.2.2"
-
-[tool.poetry.group.docs.dependencies]
-mkdocs-material = "^9.1.4"
-mkdocstrings = {extras = ["python"], version = ">=0.20,<0.25"}
-
-[tool.setuptools]
-include-package-data = false
-
-[tool.setuptools.packages.find]
-exclude = ["docs*", "test*","cookbooks*"]
\ No newline at end of file
diff --git a/requirements/requirements.docs.txt b/requirements/requirements.docs.txt
new file mode 100644
index 0000000..877109e
--- /dev/null
+++ b/requirements/requirements.docs.txt
@@ -0,0 +1,2 @@
+mkdocs-material~=9.5.33
+mkdocstrings[python]>=0.20.0,<0.25.2
diff --git a/requirements/requirements.txt b/requirements/requirements.txt
new file mode 100644
index 0000000..e06e303
--- /dev/null
+++ b/requirements/requirements.txt
@@ -0,0 +1,11 @@
+supervision~=0.24.0rc1
+requests>=2.31.0,<=2.32.3
+transformers~=4.44.2
+torch~=2.4.0
+accelerate~=0.33.0
+sentencepiece~=0.2.0
+peft~=0.12.0
+flash-attn~=2.6.3 # does not work on mac
+einops~=0.8.0
+timm~=1.0.9
+typer~=0.12.5
diff --git a/setup.py b/setup.py
new file mode 100644
index 0000000..293f4e9
--- /dev/null
+++ b/setup.py
@@ -0,0 +1,71 @@
+from typing import List, Union
+
+import setuptools
+from setuptools import find_packages
+
+
+with open("README.md", "r") as fh:
+    long_description = fh.read()
+
+
+def read_requirements(path: Union[str, List[str]]) -> List[str]:
+    if not isinstance(path, list):
+        path = [path]
+    requirements = []
+    for p in path:
+        with open(p) as fh:
+            requirements.extend([line.strip() for line in fh])
+    return requirements
+
+
+setuptools.setup(
+    name="maestro",
+    version="0.2.0rc1",
+    author="Roboflow",
+    author_email="help@roboflow.com",
+    description="Visual Prompting for Large Multimodal Models (LMMs)",
+    long_description=long_description,
+    long_description_content_type="text/markdown",
+    url="https://github.com/roboflow/multimodal-maestro",
+    packages=find_packages(
+        where=".",
+        exclude=(
+            "cookbooks",
+            "docs",
+            "tests",
+            "tests.*",
+            "requirements",
+        ),
+    ),
+    install_requires=read_requirements("requirements/requirements.txt"),
+    extras_require={
+        "dev": read_requirements("requirements/requirements.test.txt"),
+        "docs": read_requirements("requirements/requirements.docs.txt"),
+    },
+    entry_points={
+        "console_scripts": [
+            "maestro=maestro.cli.main:app",
+        ],
+    },
+    classifiers=[
+        "Development Status :: 3 - Alpha",
+        "Intended Audience :: Developers",
+        "Intended Audience :: Education",
+        "Intended Audience :: Science/Research",
+        "License :: OSI Approved :: MIT License",
+        "Programming Language :: Python :: 3",
+        "Programming Language :: Python :: 3.9",
+        "Programming Language :: Python :: 3.10",
+        "Programming Language :: Python :: 3.11",
+        "Programming Language :: Python :: 3.12",
+        "Programming Language :: Python :: 3 :: Only",
+        "Topic :: Software Development",
+        "Topic :: Scientific/Engineering",
+        "Topic :: Scientific/Engineering :: Artificial Intelligence",
+        "Typing :: Typed",
+        "Operating System :: Microsoft :: Windows",
+        "Operating System :: POSIX :: Linux",
+        "Operating System :: MacOS",
+    ],
+    python_requires=">=3.9,<3.13",
+)


Test patch:
diff --git a/requirements/requirements.test.txt b/requirements/requirements.test.txt
new file mode 100644
index 0000000..615272d
--- /dev/null
+++ b/requirements/requirements.test.txt
@@ -0,0 +1,5 @@
+pytest~=8.3.2
+black~=24.8.0
+pre-commit~=3.8.0
+mypy~=1.11.2
+flake8~=7.1.1