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Convert generator expression to list to prevent potential bugs if the files variable is used multiple times in future modifications. (#3167)

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Roland Tannous 2025-08-18 14:10:08 +03:00 • committed by GitHub
commit 8d62d91c04
1 changed files with 11 additions and 11 deletions
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22
unsloth/models/loader.py
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@ -158,7 +158,7 @@ class FastLanguageModel(FastLlamaModel):
)
pass
pass
old_model_name = model_name
if not use_exact_model_name:
model_name = get_model_name(model_name, load_in_4bit)
@ -214,7 +214,7 @@ class FastLanguageModel(FastLlamaModel):
else:
# Because HfFileSystem assumes linux paths, we need to set the path with forward slashes, even on Windows.
files = HfFileSystem(token = token).glob(f"{model_name}/*.json")
files = (os.path.split(x)[-1] for x in files)
files = list(os.path.split(x)[-1] for x in files)
if sum(x == "adapter_config.json" or x == "config.json" for x in files) >= 2:
both_exist = True
pass
@ -239,7 +239,7 @@ class FastLanguageModel(FastLlamaModel):
f"This includes Llama 3.1. The minimum required version is 4.43.2\n"\
f'Try `pip install --upgrade "transformers>=4.43.2"`\n'\
f"to obtain the latest transformers build, then restart this session."\
)
)
# Create a combined error message showing both failures
combined_error = (
"Unsloth: Failed to load model. Both AutoConfig and PeftConfig loading failed.\n\n"
@ -316,7 +316,7 @@ class FastLanguageModel(FastLlamaModel):
"To update flash-attn, do the below:\n"\
'\npip install --no-deps --upgrade "flash-attn>=2.6.3"'
)
dispatch_model = FastGemma2Model
elif model_type == "qwen2":
dispatch_model = FastQwen2Model
@ -383,7 +383,7 @@ class FastLanguageModel(FastLlamaModel):
fast_inference = False
pass
from unsloth_zoo.vllm_utils import (
patch_vllm,
patch_vllm,
vllm_dynamic_quant_supported,
)
patch_vllm()
@ -421,7 +421,7 @@ class FastLanguageModel(FastLlamaModel):
disable_log_stats = disable_log_stats,
*args, **kwargs,
)
if resize_model_vocab is not None:
model.resize_token_embeddings(resize_model_vocab)
pass
@ -598,7 +598,7 @@ class FastModel(FastBaseModel):
"float16;torch.float16;torch.float16;"\
"if name.endswith(('.conv')): module;"\
"from unsloth_zoo.temporary_patches.gemma3n import patch_Gemma3nConvNormAct_forward; patch_Gemma3nConvNormAct_forward()"
if transformers_version < Version("4.53.0"):
raise RuntimeError("Unsloth: Gemma 3N only works on transformers >= 4.53.0" + LATEST)
elif "falcon-h1" in lowered_model_name:
@ -697,7 +697,7 @@ class FastModel(FastBaseModel):
both_exist = exist_adapter_config and exist_config
else:
files = HfFileSystem(token = token).glob(f"{model_name}/*.json")
files = (os.path.split(x)[-1] for x in files)
files = list(os.path.split(x)[-1] for x in files)
if sum(x == "adapter_config.json" or x == "config.json" for x in files) >= 2:
both_exist = True
pass
@ -722,7 +722,7 @@ class FastModel(FastBaseModel):
f"This includes Llama 3.1. The minimum required version is 4.43.2\n"\
f'Try `pip install --upgrade "transformers>=4.43.2"`\n'\
f"to obtain the latest transformers build, then restart this session."\
)
)
# Create a combined error message showing both failures
combined_error = (
"Unsloth: Failed to load model. Both AutoConfig and PeftConfig loading failed.\n\n"
@ -738,7 +738,7 @@ class FastModel(FastBaseModel):
model_name = peft_config.base_model_name_or_path
if not use_exact_model_name:
model_name = get_model_name(model_name, load_in_4bit)
model_config = AutoConfig.from_pretrained(
model_name,
token = token,
@ -854,7 +854,7 @@ class FastModel(FastBaseModel):
use_gradient_checkpointing = use_gradient_checkpointing,
supports_sdpa = supports_sdpa,
whisper_language = whisper_language,
whisper_task = whisper_task,
whisper_task = whisper_task,
*args, **kwargs,
)