diff --git a/.github/workflows/lint_and_format.yml b/.github/workflows/lint_and_format.yml
new file mode 100644
index 0000000..fcaf4f4
--- /dev/null
+++ b/.github/workflows/lint_and_format.yml
@@ -0,0 +1,18 @@
+name: Ruff
+on: pull_request
+jobs:
+  lint:
+    name: Lint, Format, and Commit
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: chartboost/ruff-action@v1
+        name: Lint
+        with:
+          version: 0.3.5
+          args: "check --output-format=full --statistics"
+      - uses: chartboost/ruff-action@v1
+        name: Format
+        with:
+          version: 0.3.5
+          args: "format --check"
diff --git a/README.md b/README.md
index 9a1c569..bc04d18 100644
--- a/README.md
+++ b/README.md
@@ -255,3 +255,10 @@ If you would like to contribute to this project, we recommend following the "for
 5.  Submit a **Pull request** so that we can review your changes
 
 NOTE: Be sure to merge the latest from "upstream" before making a pull request!
+
+### Checklist Before Pull Request (Optional)
+
+1. Use `ruff check --fix` to check and fix lint errors
+2. Use `ruff format` to apply formatting
+
+NOTE: Ruff linting and formatting checks are done when PR is raised via Git Action. Before raising a PR, it is a good practice to check and fix lint errors, as well as apply formatting.
diff --git a/llmtune/data/dataset_generator.py b/llmtune/data/dataset_generator.py
index 6986289..e8eec92 100644
--- a/llmtune/data/dataset_generator.py
+++ b/llmtune/data/dataset_generator.py
@@ -1,10 +1,10 @@
 import os
-from os.path import join, exists
+import pickle
+import re
 from functools import partial
+from os.path import exists, join
 from typing import Tuple, Union
-import pickle
 
-import re
 from datasets import Dataset
 
 from llmtune.data.ingestor import Ingestor, get_ingestor
@@ -61,12 +61,8 @@ def _format_one_prompt(self, example, is_test: bool = False):
         return example
 
     def _format_prompts(self):
-        self.dataset["train"] = self.dataset["train"].map(
-            partial(self._format_one_prompt, is_test=False)
-        )
-        self.dataset["test"] = self.dataset["test"].map(
-            partial(self._format_one_prompt, is_test=True)
-        )
+        self.dataset["train"] = self.dataset["train"].map(partial(self._format_one_prompt, is_test=False))
+        self.dataset["test"] = self.dataset["test"].map(partial(self._format_one_prompt, is_test=True))
 
     def get_dataset(self) -> Tuple[Dataset, Dataset]:
         self._train_test_split()
diff --git a/llmtune/data/ingestor.py b/llmtune/data/ingestor.py
index 227e4d7..3f06c33 100644
--- a/llmtune/data/ingestor.py
+++ b/llmtune/data/ingestor.py
@@ -1,9 +1,8 @@
+import csv
 from abc import ABC, abstractmethod
-from functools import partial
 
 import ijson
-import csv
-from datasets import Dataset, load_dataset, concatenate_datasets
+from datasets import Dataset, concatenate_datasets, load_dataset
 
 
 def get_ingestor(data_type: str):
@@ -14,9 +13,7 @@ def get_ingestor(data_type: str):
     elif data_type == "huggingface":
         return HuggingfaceIngestor
     else:
-        raise ValueError(
-            f"'type' must be one of 'json', 'csv', or 'huggingface', you have {data_type}"
-        )
+        raise ValueError(f"'type' must be one of 'json', 'csv', or 'huggingface', you have {data_type}")
 
 
 class Ingestor(ABC):
diff --git a/llmtune/finetune/lora.py b/llmtune/finetune/lora.py
index da751fe..e74667c 100644
--- a/llmtune/finetune/lora.py
+++ b/llmtune/finetune/lora.py
@@ -1,31 +1,26 @@
-from os.path import join, exists
-from typing import Tuple
-
-import torch
+from os.path import join
 
 import bitsandbytes as bnb
+import torch
 from datasets import Dataset
+from peft import (
+    LoraConfig,
+    get_peft_model,
+    prepare_model_for_kbit_training,
+)
 from transformers import (
-    AutoTokenizer,
     AutoModelForCausalLM,
-    BitsAndBytesConfig,
-    TrainingArguments,
     AutoTokenizer,
+    BitsAndBytesConfig,
     ProgressCallback,
-)
-from peft import (
-    prepare_model_for_kbit_training,
-    get_peft_model,
-    LoraConfig,
+    TrainingArguments,
 )
 from trl import SFTTrainer
-from rich.console import Console
 
-
-from llmtune.pydantic_models.config_model import Config
-from llmtune.utils.save_utils import DirectoryHelper
 from llmtune.finetune.generics import Finetune
+from llmtune.pydantic_models.config_model import Config
 from llmtune.ui.rich_ui import RichUI
+from llmtune.utils.save_utils import DirectoryHelper
 
 
 class LoRAFinetune(Finetune):
@@ -99,9 +94,7 @@ def _inject_lora(self):
         self.model = get_peft_model(self.model, self._lora_config)
 
         if not self.config.accelerate:
-            self.optimizer = bnb.optim.Adam8bit(
-                self.model.parameters(), lr=self._training_args.learning_rate
-            )
+            self.optimizer = bnb.optim.Adam8bit(self.model.parameters(), lr=self._training_args.learning_rate)
             self.lr_scheduler = torch.optim.lr_scheduler.ConstantLR(self.optimizer)
         if self.config.accelerate:
             self.model, self.optimizer, self.lr_scheduler = self.accelerator.prepare(
@@ -132,7 +125,7 @@ def finetune(self, train_dataset: Dataset):
             **self._sft_args.model_dump(),
         )
 
-        trainer_stats = self._trainer.train()
+        self._trainer.train()
 
     def save_model(self) -> None:
         self._trainer.model.save_pretrained(self._weights_path)
diff --git a/llmtune/inference/lora.py b/llmtune/inference/lora.py
index c585f35..720822c 100644
--- a/llmtune/inference/lora.py
+++ b/llmtune/inference/lora.py
@@ -1,20 +1,18 @@
+import csv
 import os
 from os.path import join
 from threading import Thread
-import csv
 
-from transformers import TextIteratorStreamer
-from rich.text import Text
+import torch
 from datasets import Dataset
-from transformers import AutoTokenizer, BitsAndBytesConfig
 from peft import AutoPeftModelForCausalLM
-import torch
-
+from rich.text import Text
+from transformers import AutoTokenizer, BitsAndBytesConfig, TextIteratorStreamer
 
-from llmtune.pydantic_models.config_model import Config
-from llmtune.utils.save_utils import DirectoryHelper
 from llmtune.inference.generics import Inference
+from llmtune.pydantic_models.config_model import Config
 from llmtune.ui.rich_ui import RichUI
+from llmtune.utils.save_utils import DirectoryHelper
 
 
 # TODO: Add type hints please!
@@ -35,9 +33,7 @@ def __init__(
         self.device_map = self.config.model.device_map
         self._weights_path = dir_helper.save_paths.weights
 
-        self.model, self.tokenizer = self._get_merged_model(
-            dir_helper.save_paths.weights
-        )
+        self.model, self.tokenizer = self._get_merged_model(dir_helper.save_paths.weights)
 
     def _get_merged_model(self, weights_path: str):
         # purge VRAM
@@ -47,20 +43,14 @@ def _get_merged_model(self, weights_path: str):
         dtype = (
             torch.float16
             if self.config.training.training_args.fp16
-            else (
-                torch.bfloat16
-                if self.config.training.training_args.bf16
-                else torch.float32
-            )
+            else (torch.bfloat16 if self.config.training.training_args.bf16 else torch.float32)
         )
 
         self.model = AutoPeftModelForCausalLM.from_pretrained(
             weights_path,
             torch_dtype=dtype,
             device_map=self.device_map,
-            quantization_config=(
-                BitsAndBytesConfig(**self.config.model.bitsandbytes.model_dump())
-            ),
+            quantization_config=(BitsAndBytesConfig(**self.config.model.bitsandbytes.model_dump())),
         )
 
         """TODO: figure out multi-gpu
@@ -70,9 +60,7 @@ def _get_merged_model(self, weights_path: str):
 
         model = self.model.merge_and_unload()
 
-        tokenizer = AutoTokenizer.from_pretrained(
-            self._weights_path, device_map=self.device_map
-        )
+        tokenizer = AutoTokenizer.from_pretrained(self._weights_path, device_map=self.device_map)
 
         return model, tokenizer
 
@@ -83,13 +71,11 @@ def infer_all(self):
 
         # inference loop
         for idx, (prompt, label) in enumerate(zip(prompts, labels)):
-            RichUI.inference_ground_truth_display(
-                f"Generating on test set: {idx+1}/{len(prompts)}", prompt, label
-            )
+            RichUI.inference_ground_truth_display(f"Generating on test set: {idx+1}/{len(prompts)}", prompt, label)
 
             try:
                 result = self.infer_one(prompt)
-            except:
+            except Exception:
                 continue
             results.append((prompt, label, result))
 
@@ -103,9 +89,7 @@ def infer_all(self):
                 writer.writerow(row)
 
     def infer_one(self, prompt: str) -> str:
-        input_ids = self.tokenizer(
-            prompt, return_tensors="pt", truncation=True
-        ).input_ids.cuda()
+        input_ids = self.tokenizer(prompt, return_tensors="pt", truncation=True).input_ids.cuda()
 
         # stream processor
         streamer = TextIteratorStreamer(
@@ -115,9 +99,7 @@ def infer_one(self, prompt: str) -> str:
             timeout=60,  # 60 sec timeout for generation; to handle OOM errors
         )
 
-        generation_kwargs = dict(
-            input_ids=input_ids, streamer=streamer, **self.config.inference.model_dump()
-        )
+        generation_kwargs = dict(input_ids=input_ids, streamer=streamer, **self.config.inference.model_dump())
 
         thread = Thread(target=self.model.generate, kwargs=generation_kwargs)
         thread.start()
diff --git a/llmtune/pydantic_models/config_model.py b/llmtune/pydantic_models/config_model.py
index e2f5617..755c60c 100644
--- a/llmtune/pydantic_models/config_model.py
+++ b/llmtune/pydantic_models/config_model.py
@@ -1,27 +1,21 @@
-from typing import Literal, Union, List, Dict, Optional
-from pydantic import BaseModel, FilePath, validator, Field
-
-from huggingface_hub.utils import validate_repo_id
+from typing import List, Literal, Optional, Union
 
 import torch
+from pydantic import BaseModel, Field, FilePath, validator
+
 
 # TODO: Refactor this into multiple files...
 HfModelPath = str
 
+
 class QaConfig(BaseModel):
-    llm_tests: Optional[List[str]] = Field([], description = "list of tests that needs to be connected")
-    
+    llm_tests: Optional[List[str]] = Field([], description="list of tests that needs to be connected")
+
 
 class DataConfig(BaseModel):
-    file_type: Literal["json", "csv", "huggingface"] = Field(
-        None, description="File type"
-    )
-    path: Union[FilePath, HfModelPath] = Field(
-        None, description="Path to the file or HuggingFace model"
-    )
-    prompt: str = Field(
-        None, description="Prompt for the model. Use {} brackets for column name"
-    )
+    file_type: Literal["json", "csv", "huggingface"] = Field(None, description="File type")
+    path: Union[FilePath, HfModelPath] = Field(None, description="Path to the file or HuggingFace model")
+    prompt: str = Field(None, description="Prompt for the model. Use {} brackets for column name")
     prompt_stub: str = Field(
         None,
         description="Stub for the prompt; this is injected during training. Use {} brackets for column name",
@@ -48,9 +42,7 @@ class DataConfig(BaseModel):
 
 
 class BitsAndBytesConfig(BaseModel):
-    load_in_8bit: Optional[bool] = Field(
-        False, description="Enable 8-bit quantization with LLM.int8()"
-    )
+    load_in_8bit: Optional[bool] = Field(False, description="Enable 8-bit quantization with LLM.int8()")
     llm_int8_threshold: Optional[float] = Field(
         6.0, description="Outlier threshold for outlier detection in 8-bit quantization"
     )
@@ -61,9 +53,7 @@ class BitsAndBytesConfig(BaseModel):
         False,
         description="Enable splitting model parts between int8 on GPU and fp32 on CPU",
     )
-    llm_int8_has_fp16_weight: Optional[bool] = Field(
-        False, description="Run LLM.int8() with 16-bit main weights"
-    )
+    llm_int8_has_fp16_weight: Optional[bool] = Field(False, description="Run LLM.int8() with 16-bit main weights")
 
     load_in_4bit: Optional[bool] = Field(
         True,
@@ -86,14 +76,10 @@ class ModelConfig(BaseModel):
         "NousResearch/Llama-2-7b-hf",
         description="Path to the model (huggingface repo or local path)",
     )
-    device_map: Optional[str] = Field(
-        "auto", description="device onto which to load the model"
-    )
+    device_map: Optional[str] = Field("auto", description="device onto which to load the model")
 
     quantize: Optional[bool] = Field(False, description="Flag to enable quantization")
-    bitsandbytes: BitsAndBytesConfig = Field(
-        None, description="Bits and Bytes configuration"
-    )
+    bitsandbytes: BitsAndBytesConfig = Field(None, description="Bits and Bytes configuration")
 
     # @validator("hf_model_ckpt")
     # def validate_model(cls, v, **kwargs):
@@ -116,22 +102,12 @@ def set_device_map_to_none(cls, v, values, **kwargs):
 
 class LoraConfig(BaseModel):
     r: Optional[int] = Field(8, description="Lora rank")
-    task_type: Optional[str] = Field(
-        "CAUSAL_LM", description="Base Model task type during training"
-    )
+    task_type: Optional[str] = Field("CAUSAL_LM", description="Base Model task type during training")
 
-    lora_alpha: Optional[int] = Field(
-        16, description="The alpha parameter for Lora scaling"
-    )
-    bias: Optional[str] = Field(
-        "none", description="Bias type for Lora. Can be 'none', 'all' or 'lora_only'"
-    )
-    lora_dropout: Optional[float] = Field(
-        0.1, description="The dropout probability for Lora layers"
-    )
-    target_modules: Optional[List[str]] = Field(
-        None, description="The names of the modules to apply Lora to"
-    )
+    lora_alpha: Optional[int] = Field(16, description="The alpha parameter for Lora scaling")
+    bias: Optional[str] = Field("none", description="Bias type for Lora. Can be 'none', 'all' or 'lora_only'")
+    lora_dropout: Optional[float] = Field(0.1, description="The dropout probability for Lora layers")
+    target_modules: Optional[List[str]] = Field(None, description="The names of the modules to apply Lora to")
     fan_in_fan_out: Optional[bool] = Field(
         False,
         description="Flag to indicate if the layer to replace stores weight like (fan_in, fan_out)",
@@ -140,9 +116,7 @@ class LoraConfig(BaseModel):
         None,
         description="List of modules apart from LoRA layers to be set as trainable and saved in the final checkpoint",
     )
-    layers_to_transform: Optional[Union[List[int], int]] = Field(
-        None, description="The layer indexes to transform"
-    )
+    layers_to_transform: Optional[Union[List[int], int]] = Field(None, description="The layer indexes to transform")
     layers_pattern: Optional[str] = Field(None, description="The layer pattern name")
     # rank_pattern: Optional[Dict[str, int]] = Field(
     #     {}, description="The mapping from layer names or regexp expression to ranks"
@@ -155,15 +129,9 @@ class LoraConfig(BaseModel):
 # TODO: Get comprehensive Args!
 class TrainingArgs(BaseModel):
     num_train_epochs: Optional[int] = Field(1, description="Number of training epochs")
-    per_device_train_batch_size: Optional[int] = Field(
-        1, description="Batch size per training device"
-    )
-    gradient_accumulation_steps: Optional[int] = Field(
-        1, description="Number of steps for gradient accumulation"
-    )
-    gradient_checkpointing: Optional[bool] = Field(
-        True, description="Flag to enable gradient checkpointing"
-    )
+    per_device_train_batch_size: Optional[int] = Field(1, description="Batch size per training device")
+    gradient_accumulation_steps: Optional[int] = Field(1, description="Number of steps for gradient accumulation")
+    gradient_checkpointing: Optional[bool] = Field(True, description="Flag to enable gradient checkpointing")
     optim: Optional[str] = Field("paged_adamw_32bit", description="Optimizer")
     logging_steps: Optional[int] = Field(100, description="Number of logging steps")
     learning_rate: Optional[float] = Field(2.0e-4, description="Learning rate")
@@ -172,9 +140,7 @@ class TrainingArgs(BaseModel):
     fp16: Optional[bool] = Field(False, description="Flag to enable fp16")
     max_grad_norm: Optional[float] = Field(0.3, description="Maximum gradient norm")
     warmup_ratio: Optional[float] = Field(0.03, description="Warmup ratio")
-    lr_scheduler_type: Optional[str] = Field(
-        "constant", description="Learning rate scheduler type"
-    )
+    lr_scheduler_type: Optional[str] = Field("constant", description="Learning rate scheduler type")
 
 
 # TODO: Get comprehensive Args!
diff --git a/llmtune/qa/generics.py b/llmtune/qa/generics.py
index e44639c..4bd9b0e 100644
--- a/llmtune/qa/generics.py
+++ b/llmtune/qa/generics.py
@@ -1,9 +1,10 @@
+import statistics
 from abc import ABC, abstractmethod
-from typing import Union, List, Tuple, Dict
+from typing import Dict, List, Union
+
 import pandas as pd
+
 from llmtune.ui.rich_ui import RichUI
-import statistics
-from llmtune.qa.qa_tests import *
 
 
 class LLMQaTest(ABC):
@@ -13,9 +14,7 @@ def test_name(self) -> str:
         pass
 
     @abstractmethod
-    def get_metric(
-        self, prompt: str, grount_truth: str, model_pred: str
-    ) -> Union[float, int, bool]:
+    def get_metric(self, prompt: str, grount_truth: str, model_pred: str) -> Union[float, int, bool]:
         pass
 
 
@@ -32,7 +31,7 @@ def inner_wrapper(wrapped_class):
         return inner_wrapper
 
     @classmethod
-    def create_tests_from_list(cls, test_name: str) -> List[LLMQaTest]:
+    def create_tests_from_list(cls, test_names: List[str]) -> List[LLMQaTest]:
         return [cls.create_test(test) for test in test_names]
 
 
@@ -44,7 +43,6 @@ def __init__(
         ground_truths: List[str],
         model_preds: List[str],
     ) -> None:
-
         self.tests = tests
         self.prompts = prompts
         self.ground_truths = ground_truths
@@ -56,9 +54,7 @@ def run_tests(self) -> Dict[str, List[Union[float, int, bool]]]:
         test_results = {}
         for test in zip(self.tests):
             metrics = []
-            for prompt, ground_truth, model_pred in zip(
-                self.prompts, self.ground_truths, self.model_preds
-            ):
+            for prompt, ground_truth, model_pred in zip(self.prompts, self.ground_truths, self.model_preds):
                 metrics.append(test.get_metric(prompt, ground_truth, model_pred))
             test_results[test.test_name] = metrics
 
@@ -71,19 +67,12 @@ def test_results(self):
 
     def print_test_results(self):
         result_dictionary = self.test_results()
-        column_data = {
-            key: [value for value in result_dictionary[key]]
-            for key in result_dictionary
-        }
+        column_data = {key: list(result_dictionary[key]) for key in result_dictionary}
         mean_values = {key: statistics.mean(column_data[key]) for key in column_data}
-        median_values = {
-            key: statistics.median(column_data[key]) for key in column_data
-        }
+        median_values = {key: statistics.median(column_data[key]) for key in column_data}
         stdev_values = {key: statistics.stdev(column_data[key]) for key in column_data}
         # Use the RichUI class to display the table
-        RichUI.display_table(
-            result_dictionary, mean_values, median_values, stdev_values
-        )
+        RichUI.display_table(result_dictionary, mean_values, median_values, stdev_values)
 
     def save_test_results(self, path: str):
         # TODO: save these!
diff --git a/llmtune/qa/qa_tests.py b/llmtune/qa/qa_tests.py
index bee45f8..c9f3f6f 100644
--- a/llmtune/qa/qa_tests.py
+++ b/llmtune/qa/qa_tests.py
@@ -1,14 +1,16 @@
-from llmtune.qa.generics import LLMQaTest
-from typing import Union, List, Tuple, Dict
-import torch
-from transformers import DistilBertModel, DistilBertTokenizer
+from typing import List, Union
+
 import nltk
 import numpy as np
-from rouge_score import rouge_scorer
+import torch
+from nltk import pos_tag
 from nltk.corpus import stopwords
 from nltk.tokenize import word_tokenize
-from nltk import pos_tag
-from llmtune.qa.generics import TestRegistry
+from rouge_score import rouge_scorer
+from transformers import DistilBertModel, DistilBertTokenizer
+
+from llmtune.qa.generics import LLMQaTest, TestRegistry
+
 
 model_name = "distilbert-base-uncased"
 tokenizer = DistilBertTokenizer.from_pretrained(model_name)
@@ -25,9 +27,7 @@ class LengthTest(LLMQaTest):
     def test_name(self) -> str:
         return "summary_length"
 
-    def get_metric(
-        self, prompt: str, ground_truth: str, model_prediction: str
-    ) -> Union[float, int, bool]:
+    def get_metric(self, prompt: str, ground_truth: str, model_prediction: str) -> Union[float, int, bool]:
         return abs(len(ground_truth) - len(model_prediction))
 
 
@@ -37,9 +37,7 @@ class JaccardSimilarityTest(LLMQaTest):
     def test_name(self) -> str:
         return "jaccard_similarity"
 
-    def get_metric(
-        self, prompt: str, ground_truth: str, model_prediction: str
-    ) -> Union[float, int, bool]:
+    def get_metric(self, prompt: str, ground_truth: str, model_prediction: str) -> Union[float, int, bool]:
         set_ground_truth = set(ground_truth.lower())
         set_model_prediction = set(model_prediction.lower())
 
@@ -62,14 +60,10 @@ def _encode_sentence(self, sentence):
             outputs = model(**tokens)
         return outputs.last_hidden_state.mean(dim=1).squeeze().numpy()
 
-    def get_metric(
-        self, prompt: str, ground_truth: str, model_prediction: str
-    ) -> Union[float, int, bool]:
+    def get_metric(self, prompt: str, ground_truth: str, model_prediction: str) -> Union[float, int, bool]:
         embedding_ground_truth = self._encode_sentence(ground_truth)
         embedding_model_prediction = self._encode_sentence(model_prediction)
-        dot_product_similarity = np.dot(
-            embedding_ground_truth, embedding_model_prediction
-        )
+        dot_product_similarity = np.dot(embedding_ground_truth, embedding_model_prediction)
         return dot_product_similarity
 
 
@@ -79,9 +73,7 @@ class RougeScoreTest(LLMQaTest):
     def test_name(self) -> str:
         return "rouge_score"
 
-    def get_metric(
-        self, prompt: str, ground_truth: str, model_prediction: str
-    ) -> Union[float, int, bool]:
+    def get_metric(self, prompt: str, ground_truth: str, model_prediction: str) -> Union[float, int, bool]:
         scorer = rouge_scorer.RougeScorer(["rouge1"], use_stemmer=True)
         scores = scorer.score(model_prediction, ground_truth)
         return float(scores["rouge1"].precision)
@@ -99,9 +91,7 @@ def _remove_stopwords(self, text: str) -> str:
         filtered_text = [word for word in word_tokens if word.lower() not in stop_words]
         return " ".join(filtered_text)
 
-    def get_metric(
-        self, prompt: str, ground_truth: str, model_prediction: str
-    ) -> Union[float, int, bool]:
+    def get_metric(self, prompt: str, ground_truth: str, model_prediction: str) -> Union[float, int, bool]:
         cleaned_model_prediction = self._remove_stopwords(model_prediction)
         cleaned_ground_truth = self._remove_stopwords(ground_truth)
 
@@ -128,12 +118,8 @@ class VerbPercent(PosCompositionTest):
     def test_name(self) -> str:
         return "verb_percent"
 
-    def get_metric(
-        self, prompt: str, ground_truth: str, model_prediction: str
-    ) -> float:
-        return self._get_pos_percent(
-            model_prediction, ["VB", "VBD", "VBG", "VBN", "VBP", "VBZ"]
-        )
+    def get_metric(self, prompt: str, ground_truth: str, model_prediction: str) -> float:
+        return self._get_pos_percent(model_prediction, ["VB", "VBD", "VBG", "VBN", "VBP", "VBZ"])
 
 
 @TestRegistry.register("adjective_percent")
@@ -142,9 +128,7 @@ class AdjectivePercent(PosCompositionTest):
     def test_name(self) -> str:
         return "adjective_percent"
 
-    def get_metric(
-        self, prompt: str, ground_truth: str, model_prediction: str
-    ) -> float:
+    def get_metric(self, prompt: str, ground_truth: str, model_prediction: str) -> float:
         return self._get_pos_percent(model_prediction, ["JJ", "JJR", "JJS"])
 
 
@@ -154,9 +138,7 @@ class NounPercent(PosCompositionTest):
     def test_name(self) -> str:
         return "noun_percent"
 
-    def get_metric(
-        self, prompt: str, ground_truth: str, model_prediction: str
-    ) -> float:
+    def get_metric(self, prompt: str, ground_truth: str, model_prediction: str) -> float:
         return self._get_pos_percent(model_prediction, ["NN", "NNS", "NNP", "NNPS"])
 
 
diff --git a/llmtune/ui/generics.py b/llmtune/ui/generics.py
index 59d5997..87d8fee 100644
--- a/llmtune/ui/generics.py
+++ b/llmtune/ui/generics.py
@@ -61,7 +61,7 @@ def finetune_found(weights_path: str):
         pass
 
     """
-    INFERENCE 
+    INFERENCE
     """
 
     # Lifecycle functions
@@ -91,7 +91,7 @@ def inference_stream_display(text: Text):
         pass
 
     """
-    QA 
+    QA
     """
 
     # Lifecycle functions
diff --git a/llmtune/ui/rich_ui.py b/llmtune/ui/rich_ui.py
index f138f4c..b8d71bc 100644
--- a/llmtune/ui/rich_ui.py
+++ b/llmtune/ui/rich_ui.py
@@ -1,15 +1,15 @@
 from datasets import Dataset
-
 from rich.console import Console
 from rich.layout import Layout
+from rich.live import Live
 from rich.panel import Panel
 from rich.table import Table
-from rich.live import Live
 from rich.text import Text
 
 from llmtune.ui.generics import UI
 from llmtune.utils.rich_print_utils import inject_example_to_rich_layout
 
+
 console = Console()
 
 
@@ -25,9 +25,7 @@ def __enter__(self):
         return self  # This allows you to use variables from this context if needed
 
     def __exit__(self, exc_type, exc_val, exc_tb):
-        self.task.__exit__(
-            exc_type, exc_val, exc_tb
-        )  # Cleanly exit the console status context
+        self.task.__exit__(exc_type, exc_val, exc_tb)  # Cleanly exit the console status context
 
 
 class LiveContext:
@@ -47,9 +45,7 @@ def __enter__(self):
         return self  # This allows you to use variables from this context if needed
 
     def __exit__(self, exc_type, exc_val, exc_tb):
-        self.task.__exit__(
-            exc_type, exc_val, exc_tb
-        )  # Cleanly exit the console status context
+        self.task.__exit__(exc_type, exc_val, exc_tb)  # Cleanly exit the console status context
 
     def update(self, new_text: Text):
         self.task.update(new_text)
@@ -72,7 +68,7 @@ def during_dataset_creation(message: str, spinner: str):
     @staticmethod
     def after_dataset_creation(save_dir: str, train: Dataset, test: Dataset):
         console.print(f"Dataset Saved at {save_dir}")
-        console.print(f"Post-Split data size:")
+        console.print("Post-Split data size:")
         console.print(f"Train: {len(train)}")
         console.print(f"Test: {len(test)}")
 
@@ -93,9 +89,7 @@ def dataset_display_one_example(train_row: dict, test_row: dict):
         )
 
         inject_example_to_rich_layout(layout["train"], "Train Example", train_row)
-        inject_example_to_rich_layout(
-            layout["inference"], "Inference Example", test_row
-        )
+        inject_example_to_rich_layout(layout["inference"], "Inference Example", test_row)
 
         console.print(layout)
 
@@ -122,14 +116,14 @@ def during_finetune():
 
     @staticmethod
     def after_finetune():
-        console.print(f"Finetuning complete!")
+        console.print("Finetuning complete!")
 
     @staticmethod
     def finetune_found(weights_path: str):
         console.print(f"Fine-Tuned Model Found at {weights_path}... skipping training")
 
     """
-    INFERENCE 
+    INFERENCE
     """
 
     # Lifecycle functions
@@ -167,7 +161,7 @@ def inference_stream_display(text: Text):
         return LiveContext(text)
 
     """
-    QA 
+    QA
     """
 
     # Lifecycle functions
@@ -188,10 +182,7 @@ def qa_found():
         pass
 
     @staticmethod
-    def qa_display_table(
-        self, result_dictionary, mean_values, median_values, stdev_values
-    ):
-
+    def qa_display_table(self, result_dictionary, mean_values, median_values, stdev_values):
         # Create a table
         table = Table(show_header=True, header_style="bold", title="Test Results")
 
diff --git a/llmtune/utils/ablation_utils.py b/llmtune/utils/ablation_utils.py
index 37d9d80..062892e 100644
--- a/llmtune/utils/ablation_utils.py
+++ b/llmtune/utils/ablation_utils.py
@@ -1,9 +1,6 @@
 import copy
 import itertools
-from typing import List, Type, Any, Dict, Optional, Union, Tuple
-from typing import get_args, get_origin, get_type_hints
-
-import yaml
+from typing import Dict, Tuple, Union, get_args, get_origin
 
 
 # TODO: organize this a little bit. It's a bit of a mess rn.
@@ -14,17 +11,11 @@
 """
 
 
-def get_types_from_dict(
-    source_dict: dict, root="", type_dict={}
-) -> Dict[str, Tuple[type, type]]:
+def get_types_from_dict(source_dict: dict, root="", type_dict={}) -> Dict[str, Tuple[type, type]]:
     for key, val in source_dict.items():
-        if type(val) is not dict:
+        if not isinstance(val, dict):
             attr = f"{root}.{key}" if root else key
-            tp = (
-                (type(val), None)
-                if type(val) is not list
-                else (type(val), type(val[0]))
-            )
+            tp = (type(val), None) if not isinstance(val, list) else (type(val), type(val[0]))
             type_dict[attr] = tp
         else:
             join_array = [root, key] if root else [key]
diff --git a/llmtune/utils/rich_print_utils.py b/llmtune/utils/rich_print_utils.py
index 371f742..d39cc4c 100644
--- a/llmtune/utils/rich_print_utils.py
+++ b/llmtune/utils/rich_print_utils.py
@@ -1,7 +1,7 @@
-from rich.panel import Panel
 from rich.layout import Layout
-from rich.text import Text
+from rich.panel import Panel
 from rich.table import Table
+from rich.text import Text
 
 
 def inject_example_to_rich_layout(layout: Layout, layout_name: str, example: dict):
diff --git a/llmtune/utils/save_utils.py b/llmtune/utils/save_utils.py
index dd8879b..ef5d0e6 100644
--- a/llmtune/utils/save_utils.py
+++ b/llmtune/utils/save_utils.py
@@ -4,20 +4,19 @@
     2. Check if files are present at various experiment stages
 """
 
-import shutil
+import hashlib
 import os
-from os.path import exists
-import yaml
-
 import re
-import hashlib
-from functools import cached_property
 from dataclasses import dataclass
+from functools import cached_property
+from os.path import exists
 
+import yaml
 from sqids import Sqids
 
 from llmtune.pydantic_models.config_model import Config
 
+
 NUM_MD5_DIGITS_FOR_SQIDS = 5  # TODO: maybe move consts to a dedicated folder
 
 
diff --git a/poetry.lock b/poetry.lock
index c65c484..db361f2 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -291,52 +291,6 @@ files = [
 [package.dependencies]
 scipy = "*"
 
-[[package]]
-name = "black"
-version = "24.3.0"
-description = "The uncompromising code formatter."
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "black-24.3.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:7d5e026f8da0322b5662fa7a8e752b3fa2dac1c1cbc213c3d7ff9bdd0ab12395"},
-    {file = "black-24.3.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:9f50ea1132e2189d8dff0115ab75b65590a3e97de1e143795adb4ce317934995"},
-    {file = "black-24.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e2af80566f43c85f5797365077fb64a393861a3730bd110971ab7a0c94e873e7"},
-    {file = "black-24.3.0-cp310-cp310-win_amd64.whl", hash = "sha256:4be5bb28e090456adfc1255e03967fb67ca846a03be7aadf6249096100ee32d0"},
-    {file = "black-24.3.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:4f1373a7808a8f135b774039f61d59e4be7eb56b2513d3d2f02a8b9365b8a8a9"},
-    {file = "black-24.3.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:aadf7a02d947936ee418777e0247ea114f78aff0d0959461057cae8a04f20597"},
-    {file = "black-24.3.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:65c02e4ea2ae09d16314d30912a58ada9a5c4fdfedf9512d23326128ac08ac3d"},
-    {file = "black-24.3.0-cp311-cp311-win_amd64.whl", hash = "sha256:bf21b7b230718a5f08bd32d5e4f1db7fc8788345c8aea1d155fc17852b3410f5"},
-    {file = "black-24.3.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:2818cf72dfd5d289e48f37ccfa08b460bf469e67fb7c4abb07edc2e9f16fb63f"},
-    {file = "black-24.3.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:4acf672def7eb1725f41f38bf6bf425c8237248bb0804faa3965c036f7672d11"},
-    {file = "black-24.3.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c7ed6668cbbfcd231fa0dc1b137d3e40c04c7f786e626b405c62bcd5db5857e4"},
-    {file = "black-24.3.0-cp312-cp312-win_amd64.whl", hash = "sha256:56f52cfbd3dabe2798d76dbdd299faa046a901041faf2cf33288bc4e6dae57b5"},
-    {file = "black-24.3.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:79dcf34b33e38ed1b17434693763301d7ccbd1c5860674a8f871bd15139e7837"},
-    {file = "black-24.3.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:e19cb1c6365fd6dc38a6eae2dcb691d7d83935c10215aef8e6c38edee3f77abd"},
-    {file = "black-24.3.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:65b76c275e4c1c5ce6e9870911384bff5ca31ab63d19c76811cb1fb162678213"},
-    {file = "black-24.3.0-cp38-cp38-win_amd64.whl", hash = "sha256:b5991d523eee14756f3c8d5df5231550ae8993e2286b8014e2fdea7156ed0959"},
-    {file = "black-24.3.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:c45f8dff244b3c431b36e3224b6be4a127c6aca780853574c00faf99258041eb"},
-    {file = "black-24.3.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:6905238a754ceb7788a73f02b45637d820b2f5478b20fec82ea865e4f5d4d9f7"},
-    {file = "black-24.3.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d7de8d330763c66663661a1ffd432274a2f92f07feeddd89ffd085b5744f85e7"},
-    {file = "black-24.3.0-cp39-cp39-win_amd64.whl", hash = "sha256:7bb041dca0d784697af4646d3b62ba4a6b028276ae878e53f6b4f74ddd6db99f"},
-    {file = "black-24.3.0-py3-none-any.whl", hash = "sha256:41622020d7120e01d377f74249e677039d20e6344ff5851de8a10f11f513bf93"},
-    {file = "black-24.3.0.tar.gz", hash = "sha256:a0c9c4a0771afc6919578cec71ce82a3e31e054904e7197deacbc9382671c41f"},
-]
-
-[package.dependencies]
-click = ">=8.0.0"
-mypy-extensions = ">=0.4.3"
-packaging = ">=22.0"
-pathspec = ">=0.9.0"
-platformdirs = ">=2"
-tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
-typing-extensions = {version = ">=4.0.1", markers = "python_version < \"3.11\""}
-
-[package.extras]
-colorama = ["colorama (>=0.4.3)"]
-d = ["aiohttp (>=3.7.4)", "aiohttp (>=3.7.4,!=3.9.0)"]
-jupyter = ["ipython (>=7.8.0)", "tokenize-rt (>=3.2.0)"]
-uvloop = ["uvloop (>=0.15.2)"]
-
 [[package]]
 name = "brotli"
 version = "1.1.0"
@@ -2082,17 +2036,6 @@ files = [
 qa = ["flake8 (==3.8.3)", "mypy (==0.782)"]
 testing = ["docopt", "pytest (<6.0.0)"]
 
-[[package]]
-name = "pathspec"
-version = "0.12.1"
-description = "Utility library for gitignore style pattern matching of file paths."
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "pathspec-0.12.1-py3-none-any.whl", hash = "sha256:a0d503e138a4c123b27490a4f7beda6a01c6f288df0e4a8b79c7eb0dc7b4cc08"},
-    {file = "pathspec-0.12.1.tar.gz", hash = "sha256:a482d51503a1ab33b1c67a6c3813a26953dbdc71c31dacaef9a838c4e29f5712"},
-]
-
 [[package]]
 name = "peft"
 version = "0.8.2"
@@ -2136,21 +2079,6 @@ files = [
 [package.dependencies]
 ptyprocess = ">=0.5"
 
-[[package]]
-name = "platformdirs"
-version = "4.2.0"
-description = "A small Python package for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "platformdirs-4.2.0-py3-none-any.whl", hash = "sha256:0614df2a2f37e1a662acbd8e2b25b92ccf8632929bc6d43467e17fe89c75e068"},
-    {file = "platformdirs-4.2.0.tar.gz", hash = "sha256:ef0cc731df711022c174543cb70a9b5bd22e5a9337c8624ef2c2ceb8ddad8768"},
-]
-
-[package.extras]
-docs = ["furo (>=2023.9.10)", "proselint (>=0.13)", "sphinx (>=7.2.6)", "sphinx-autodoc-typehints (>=1.25.2)"]
-test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)"]
-
 [[package]]
 name = "prompt-toolkit"
 version = "3.0.43"
@@ -3026,6 +2954,32 @@ nltk = "*"
 numpy = "*"
 six = ">=1.14.0"
 
+[[package]]
+name = "ruff"
+version = "0.3.5"
+description = "An extremely fast Python linter and code formatter, written in Rust."
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "ruff-0.3.5-py3-none-macosx_10_12_x86_64.macosx_11_0_arm64.macosx_10_12_universal2.whl", hash = "sha256:aef5bd3b89e657007e1be6b16553c8813b221ff6d92c7526b7e0227450981eac"},
+    {file = "ruff-0.3.5-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:89b1e92b3bd9fca249153a97d23f29bed3992cff414b222fcd361d763fc53f12"},
+    {file = "ruff-0.3.5-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5e55771559c89272c3ebab23326dc23e7f813e492052391fe7950c1a5a139d89"},
+    {file = "ruff-0.3.5-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:dabc62195bf54b8a7876add6e789caae0268f34582333cda340497c886111c39"},
+    {file = "ruff-0.3.5-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:3a05f3793ba25f194f395578579c546ca5d83e0195f992edc32e5907d142bfa3"},
+    {file = "ruff-0.3.5-py3-none-manylinux_2_17_ppc64.manylinux2014_ppc64.whl", hash = "sha256:dfd3504e881082959b4160ab02f7a205f0fadc0a9619cc481982b6837b2fd4c0"},
+    {file = "ruff-0.3.5-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:87258e0d4b04046cf1d6cc1c56fadbf7a880cc3de1f7294938e923234cf9e498"},
+    {file = "ruff-0.3.5-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:712e71283fc7d9f95047ed5f793bc019b0b0a29849b14664a60fd66c23b96da1"},
+    {file = "ruff-0.3.5-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a532a90b4a18d3f722c124c513ffb5e5eaff0cc4f6d3aa4bda38e691b8600c9f"},
+    {file = "ruff-0.3.5-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:122de171a147c76ada00f76df533b54676f6e321e61bd8656ae54be326c10296"},
+    {file = "ruff-0.3.5-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:d80a6b18a6c3b6ed25b71b05eba183f37d9bc8b16ace9e3d700997f00b74660b"},
+    {file = "ruff-0.3.5-py3-none-musllinux_1_2_i686.whl", hash = "sha256:a7b6e63194c68bca8e71f81de30cfa6f58ff70393cf45aab4c20f158227d5936"},
+    {file = "ruff-0.3.5-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:a759d33a20c72f2dfa54dae6e85e1225b8e302e8ac655773aff22e542a300985"},
+    {file = "ruff-0.3.5-py3-none-win32.whl", hash = "sha256:9d8605aa990045517c911726d21293ef4baa64f87265896e491a05461cae078d"},
+    {file = "ruff-0.3.5-py3-none-win_amd64.whl", hash = "sha256:dc56bb16a63c1303bd47563c60482a1512721053d93231cf7e9e1c6954395a0e"},
+    {file = "ruff-0.3.5-py3-none-win_arm64.whl", hash = "sha256:faeeae9905446b975dcf6d4499dc93439b131f1443ee264055c5716dd947af55"},
+    {file = "ruff-0.3.5.tar.gz", hash = "sha256:a067daaeb1dc2baf9b82a32dae67d154d95212080c80435eb052d95da647763d"},
+]
+
 [[package]]
 name = "safetensors"
 version = "0.4.2"
@@ -4378,4 +4332,4 @@ multidict = ">=4.0"
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9, <=3.12"
-content-hash = "32b85b0200f6dab57cb9f4c57fb47cd28bbf41e6077f88428331b18239e0f7e1"
+content-hash = "5b9a7244db14a2307b67a46d6278a720635eb08ed1a23a5fa643703b189f6053"
diff --git a/pyproject.toml b/pyproject.toml
index 76e72a6..caf9203 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -43,8 +43,30 @@ shellingham = "^1.5.4"
 
 
 [tool.poetry.group.dev.dependencies]
-black = "^24.3.0"
+ruff = "~0.3.5"
 
 [build-system]
 requires = ["poetry-core"]
 build-backend = "poetry.core.masonry.api"
+
+[tool.ruff]
+lint.ignore = ["C901", "E501", "E741", "F402", "F823" ]
+lint.select = ["C", "E", "F", "I", "W"]
+line-length = 119
+exclude = [
+    "llama2",
+    "mistral",
+]
+
+
+[tool.ruff.lint.isort]
+lines-after-imports = 2
+known-first-party = ["llmtune"]
+
+[tool.ruff.format]
+quote-style = "double"
+indent-style = "space"
+skip-magic-trailing-comma = false
+line-ending = "auto"
+
+
diff --git a/toolkit.py b/toolkit.py
index 0ccb91b..2f807a5 100644
--- a/toolkit.py
+++ b/toolkit.py
@@ -1,20 +1,21 @@
-from os import listdir
-from os.path import join, exists
-import yaml
 import logging
+from os import listdir
+from os.path import exists, join
 
-from transformers import utils as hf_utils
-from pydantic import ValidationError
 import torch
 import typer
+import yaml
+from pydantic import ValidationError
+from transformers import utils as hf_utils
 
-from llmtune.pydantic_models.config_model import Config
 from llmtune.data.dataset_generator import DatasetGenerator
-from llmtune.utils.save_utils import DirectoryHelper
-from llmtune.utils.ablation_utils import generate_permutations
 from llmtune.finetune.lora import LoRAFinetune
 from llmtune.inference.lora import LoRAInference
+from llmtune.pydantic_models.config_model import Config
 from llmtune.ui.rich_ui import RichUI
+from llmtune.utils.ablation_utils import generate_permutations
+from llmtune.utils.save_utils import DirectoryHelper
+
 
 hf_utils.logging.set_verbosity_error()
 torch._logging.set_logs(all=logging.CRITICAL)
@@ -32,7 +33,7 @@ def run_one_experiment(config: Config, config_path: str) -> None:
     with RichUI.during_dataset_creation("Injecting Values into Prompt", "monkey"):
         dataset_generator = DatasetGenerator(**config.data.model_dump())
 
-    train_columns = dataset_generator.train_columns
+    _ = dataset_generator.train_columns
     test_column = dataset_generator.test_column
 
     dataset_path = dir_helper.save_paths.dataset
@@ -66,9 +67,8 @@ def run_one_experiment(config: Config, config_path: str) -> None:
     results_path = dir_helper.save_paths.results
     results_file_path = join(dir_helper.save_paths.results, "results.csv")
     if not exists(results_path) or exists(results_file_path):
-        inference_runner = LoRAInference(
-            test, test_column, config, dir_helper
-        ).infer_all()
+        inference_runner = LoRAInference(test, test_column, config, dir_helper)
+        inference_runner.infer_all()
         RichUI.after_inference(results_path)
     else:
         RichUI.inference_found(results_path)
@@ -90,9 +90,7 @@ def run(config_path: str = "./config.yml") -> None:
     with open(config_path, "r") as file:
         config = yaml.safe_load(file)
         configs = (
-            generate_permutations(config, Config)
-            if config.get("ablation", {}).get("use_ablate", False)
-            else [config]
+            generate_permutations(config, Config) if config.get("ablation", {}).get("use_ablate", False) else [config]
         )
     for config in configs:
         # validate data with pydantic