diff --git a/unsloth/__init__.py b/unsloth/__init__.py
index db54c9a169..dd526dc3cc 100644
--- a/unsloth/__init__.py
+++ b/unsloth/__init__.py
@@ -11,10 +11,8 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-import os
-import warnings
-import importlib
-import sys
+
+import warnings, importlib, sys
 from packaging.version import Version
 
 # # Define a list of modules to check
@@ -60,9 +58,8 @@ except:
                       "We have some installation instructions on our Github page.")
 pass
 
-import os, re
+import os, re, subprocess, inspect
 import numpy as np
-import subprocess
 
 # Hugging Face Hub faster downloads (only enable during Colab and Kaggle sessions)
 keynames = "\n" + "\n".join(os.environ.keys())
@@ -83,12 +80,12 @@ elif (major_torch == 2) and (minor_torch < 2):
     del os.environ["PYTORCH_CUDA_ALLOC_CONF"]
 pass
 
-# Torch 2.5 has including_emulation
+# Torch 2.4 has including_emulation
 major_version, minor_version = torch.cuda.get_device_capability()
 SUPPORTS_BFLOAT16 = (major_version >= 8)
 
-if (major_torch == 2) and (minor_torch >= 5): 
-    old_is_bf16_supported = torch.cuda.is_bf16_supported
+old_is_bf16_supported = torch.cuda.is_bf16_supported
+if "including_emulation" in str(inspect.signature(old_is_bf16_supported)):
     def is_bf16_supported(including_emulation = False):
         return old_is_bf16_supported(including_emulation)
     torch.cuda.is_bf16_supported = is_bf16_supported
diff --git a/unsloth/models/loader.py b/unsloth/models/loader.py
index e260017fb9..02ed00f5cd 100644
--- a/unsloth/models/loader.py
+++ b/unsloth/models/loader.py
@@ -169,13 +169,23 @@ class FastLanguageModel(FastLlamaModel):
         autoconfig_error = None
         peft_error = None
         try:
-            model_config = AutoConfig.from_pretrained(model_name, token = token, revision = revision)
+            model_config = AutoConfig.from_pretrained(
+                model_name,
+                token = token,
+                revision = revision,
+                trust_remote_code = trust_remote_code,
+            )
             is_model = True
         except Exception as error:
             autoconfig_error = str(error)
             is_model = False
         try:
-            peft_config = PeftConfig .from_pretrained(model_name, token = token, revision = revision)
+            peft_config = PeftConfig.from_pretrained(
+                model_name,
+                token = token,
+                revision = revision,
+                trust_remote_code = trust_remote_code,
+            )
             is_peft = True
         except Exception as error:
             peft_error = str(error)
@@ -207,7 +217,12 @@ class FastLanguageModel(FastLlamaModel):
         if is_peft:
             # Check base model again for PEFT
             model_name = get_model_name(peft_config.base_model_name_or_path, load_in_4bit)
-            model_config = AutoConfig.from_pretrained(model_name, token = token, revision = revision)
+            model_config = AutoConfig.from_pretrained(
+                model_name,
+                token = token,
+                revision = revision,
+                trust_remote_code = trust_remote_code,
+            )
         pass
 
         if not was_disabled: enable_progress_bars()
@@ -340,10 +355,11 @@ class FastLanguageModel(FastLlamaModel):
                 token = token,
                 revision = revision,
                 is_trainable = True,
+                trust_remote_code = trust_remote_code,
             )
             # Patch it as well!
             model = dispatch_model.patch_peft_model(model, use_gradient_checkpointing)
         pass
         return model, tokenizer
     pass
-pass
+pass
\ No newline at end of file