black formatting

src/axolotl/utils/models.py

@@ -8,15 +8,19 @@ import transformers
 from transformers import (
     AutoModelForCausalLM,
     AutoTokenizer,
-    PreTrainedModel, AutoConfig,
+    PreTrainedModel,
+    AutoConfig,
 )
+
 try:
     from transformers import (
         LlamaForCausalLM,
         LlamaTokenizer,
     )
 except:
-    logging.warning("This version of transformers does not support Llama. Consider upgrading.")
+    logging.warning(
+        "This version of transformers does not support Llama. Consider upgrading."
+    )
 
 from axolotl.prompt_tokenizers import LLAMA_DEFAULT_PAD_TOKEN
 
@@ -40,7 +44,9 @@ def load_model(
     # TODO refactor as a kwarg
     load_in_8bit = cfg.load_in_8bit
     tokenizer = None
-    is_llama_derived_model = "llama" in base_model or (cfg.model_type and "llama" in cfg.model_type.lower())
+    is_llama_derived_model = "llama" in base_model or (
+        cfg.model_type and "llama" in cfg.model_type.lower()
+    )
 
     if is_llama_derived_model and cfg.flash_attention:
         if cfg.device not in ["mps", "cpu"] and inference is False:
@@ -49,11 +55,16 @@ def load_model(
             logging.info("patching with flash attention")
             replace_llama_attn_with_flash_attn()
     elif is_llama_derived_model and cfg.xformers_attention:
-        from alpaca_lora_4bit.monkeypatch.llama_attn_hijack_xformers import hijack_llama_attention
+        from alpaca_lora_4bit.monkeypatch.llama_attn_hijack_xformers import (
+            hijack_llama_attention,
+        )
+
         logging.info("patching with xformers attention")
         hijack_llama_attention()
 
-    torch_dtype = torch.float16 if cfg.load_in_8bit or cfg.fp16 or cfg.bf16 else torch.float32
+    torch_dtype = (
+        torch.float16 if cfg.load_in_8bit or cfg.fp16 or cfg.bf16 else torch.float32
+    )
     try:
         if cfg.load_4bit:
             from alpaca_lora_4bit.monkeypatch.peft_tuners_lora_monkey_patch import (
@@ -74,8 +85,12 @@ def load_model(
             try:
                 snapshot_download_kwargs = {}
                 if cfg.base_model_ignore_patterns:
-                    snapshot_download_kwargs["ignore_patterns"] = cfg.base_model_ignore_patterns
-                cache_model_path = Path(snapshot_download(base_model, **snapshot_download_kwargs))
+                    snapshot_download_kwargs[
+                        "ignore_patterns"
+                    ] = cfg.base_model_ignore_patterns
+                cache_model_path = Path(
+                    snapshot_download(base_model, **snapshot_download_kwargs)
+                )
                 files = (
                     list(cache_model_path.glob("*.pt"))
                     + list(cache_model_path.glob("*.safetensors"))