From 718f673439d99189eb85bcf0c31d6e0372f7d5ea Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Thu, 12 Mar 2026 08:19:58 +0000
Subject: [PATCH] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 unsloth/models/llama.py  | 18 ++++++++++++++++--
 unsloth/models/loader.py |  5 ++++-
 2 files changed, 20 insertions(+), 3 deletions(-)

diff --git a/unsloth/models/llama.py b/unsloth/models/llama.py
index 8a7e754425..8853715ac4 100644
--- a/unsloth/models/llama.py
+++ b/unsloth/models/llama.py
@@ -2808,7 +2808,14 @@ class FastLlamaModel:
         if r <= 0:
             raise TypeError(f"Unsloth: Rank of {str(r)} must be larger than 0.")
 
-        if isinstance(model, (PeftModelForCausalLM, PeftModelForSequenceClassification, PeftModelForSeq2SeqLM)):
+        if isinstance(
+            model,
+            (
+                PeftModelForCausalLM,
+                PeftModelForSequenceClassification,
+                PeftModelForSeq2SeqLM,
+            ),
+        ):
             # Check if exactly the same and then pass through!
             assert hasattr(model, "peft_config")
 
@@ -3246,7 +3253,14 @@ class FastLlamaModel:
                 model = model,
                 use_gradient_checkpointing = use_gradient_checkpointing,
             )
-        if not isinstance(model, (PeftModelForCausalLM, PeftModelForSequenceClassification, PeftModelForSeq2SeqLM)):
+        if not isinstance(
+            model,
+            (
+                PeftModelForCausalLM,
+                PeftModelForSequenceClassification,
+                PeftModelForSeq2SeqLM,
+            ),
+        ):
             raise TypeError(
                 "Unsloth: Your model needs to call `.get_peft_model` first!"
             )
diff --git a/unsloth/models/loader.py b/unsloth/models/loader.py
index b585a4492f..33dd8928e2 100644
--- a/unsloth/models/loader.py
+++ b/unsloth/models/loader.py
@@ -1395,7 +1395,10 @@ class FastModel(FastBaseModel):
         is_vlm = any(x.endswith("ForConditionalGeneration") for x in architectures)
         is_vlm = is_vlm or hasattr(model_config, "vision_config")
         if auto_model is None:
-            if AutoModelForSeq2SeqLM._model_mapping.get(type(model_config), None) is not None:
+            if (
+                AutoModelForSeq2SeqLM._model_mapping.get(type(model_config), None)
+                is not None
+            ):
                 auto_model = AutoModelForSeq2SeqLM
             elif is_vlm:
                 # Check if the model's auto_map supports the VLM auto class.