本指南將逐步說明如何使用 Hugging Face Transformers 和 TRL,針對視覺任務 (產生產品說明) 在自訂圖片和文字資料集上微調 Gemma。您將學會:
- 量化低秩調整 (QLoRA) 是什麼
- 設定開發環境
- 建立及準備視覺任務的精修資料集
- 使用 TRL 和 SFTTrainer 對 Gemma 進行微調
- 測試模型推論,並根據圖片和文字產生產品說明。
量化低秩調整 (QLoRA) 是什麼
本指南將說明如何使用量化低秩序調整 (QLoRA),這是一種有效精細調整 LLM 的熱門方法,因為它可減少運算資源需求,同時維持高效能。在 QloRA 中,預先訓練的模型會量化為 4 位元,權重則會凍結。接著,系統會附加可訓練的轉接層 (LoRA),並只訓練轉接層。之後,轉接器權重可與基礎模型合併,或保留為獨立的轉接器。
設定開發環境
第一步是安裝 Hugging Face 程式庫 (包括 TRL) 和資料集,以便微調開放式模型。
# Install Pytorch & other libraries
%pip install "torch>=2.4.0" tensorboard torchvision
# Install Gemma release branch from Hugging Face
%pip install "transformers>=4.51.3"
# Install Hugging Face libraries
%pip install --upgrade \
"datasets==3.3.2" \
"accelerate==1.4.0" \
"evaluate==0.4.3" \
"bitsandbytes==0.45.3" \
"trl==0.15.2" \
"peft==0.14.0" \
"pillow==11.1.0" \
protobuf \
sentencepiece
請務必先接受 Gemma 的使用條款,才能開始訓練。您可以接受 Hugging Face 的授權,方法是點選模型頁面 (http://huggingface.co/google/gemma-3-4b-pt) 上的「同意並存取存放區」按鈕 (或您使用的具備視覺功能 Gemma 模型的適當模型頁面)。
接受授權後,您必須使用有效的 Hugging Face 權杖才能存取模型。如果您在 Google Colab 中執行,可以使用 Colab 機密資料安全地使用 Hugging Face 權杖;否則,您可以直接在 login
方法中設定權杖。請確認您的權杖也有寫入權限,因為您會在訓練期間將模型推送至 Hub。
from google.colab import userdata
from huggingface_hub import login
# Login into Hugging Face Hub
hf_token = userdata.get('HF_TOKEN') # If you are running inside a Google Colab
login(hf_token)
建立及準備精修資料集
在微調 LLM 時,請務必瞭解您的用途和要解決的任務。這有助於您建立資料集,以便微調模型。如果您尚未定義用途,建議您重新規劃。
本指南會以以下用途為例:
- 微調 Gemma 模型,為電子商務平台產生精簡的 SEO 最佳化產品說明,特別針對行動搜尋進行調整。
本指南使用 philschmid/amazon-product-descriptions-vlm 資料集,這是 Amazon 產品說明資料集,包括產品圖片和類別。
Hugging Face TRL 支援多模態對話。其中最重要的部分是「image」角色,可告知處理類別應載入圖片。結構應符合以下規定:
{"messages": [{"role": "system", "content": [{"type": "text", "text":"You are..."}]}, {"role": "user", "content": [{"type": "text", "text": "..."}, {"type": "image"}]}, {"role": "assistant", "content": [{"type": "text", "text": "..."}]}]}
{"messages": [{"role": "system", "content": [{"type": "text", "text":"You are..."}]}, {"role": "user", "content": [{"type": "text", "text": "..."}, {"type": "image"}]}, {"role": "assistant", "content": [{"type": "text", "text": "..."}]}]}
{"messages": [{"role": "system", "content": [{"type": "text", "text":"You are..."}]}, {"role": "user", "content": [{"type": "text", "text": "..."}, {"type": "image"}]}, {"role": "assistant", "content": [{"type": "text", "text": "..."}]}]}
您現在可以使用 Hugging Face 資料集程式庫載入資料集,並建立提示範本,將圖片、產品名稱和類別結合,並新增系統訊息。資料集會將圖片納入 Pil.Image
物件。
from datasets import load_dataset
from PIL import Image
# System message for the assistant
system_message = "You are an expert product description writer for Amazon."
# User prompt that combines the user query and the schema
user_prompt = """Create a Short Product description based on the provided <PRODUCT> and <CATEGORY> and image.
Only return description. The description should be SEO optimized and for a better mobile search experience.
<PRODUCT>
{product}
</PRODUCT>
<CATEGORY>
{category}
</CATEGORY>
"""
# Convert dataset to OAI messages
def format_data(sample):
return {
"messages": [
{
"role": "system",
"content": [{"type": "text", "text": system_message}],
},
{
"role": "user",
"content": [
{
"type": "text",
"text": user_prompt.format(
product=sample["Product Name"],
category=sample["Category"],
),
},
{
"type": "image",
"image": sample["image"],
},
],
},
{
"role": "assistant",
"content": [{"type": "text", "text": sample["description"]}],
},
],
}
def process_vision_info(messages: list[dict]) -> list[Image.Image]:
image_inputs = []
# Iterate through each conversation
for msg in messages:
# Get content (ensure it's a list)
content = msg.get("content", [])
if not isinstance(content, list):
content = [content]
# Check each content element for images
for element in content:
if isinstance(element, dict) and (
"image" in element or element.get("type") == "image"
):
# Get the image and convert to RGB
if "image" in element:
image = element["image"]
else:
image = element
image_inputs.append(image.convert("RGB"))
return image_inputs
# Load dataset from the hub
dataset = load_dataset("philschmid/amazon-product-descriptions-vlm", split="train")
# Convert dataset to OAI messages
# need to use list comprehension to keep Pil.Image type, .mape convert image to bytes
dataset = [format_data(sample) for sample in dataset]
print(dataset[345]["messages"])
使用 TRL 和 SFTTrainer 對 Gemma 進行微調
您現在可以微調模型了。透過 Hugging Face TRL 的 SFTTrainer,您可以輕鬆監督開放式 LLM 的微調作業。SFTTrainer
是 transformers
程式庫中 Trainer
的子類別,支援所有相同的功能,包括記錄、評估和檢查點,但會新增其他便利功能,包括:
- 資料集格式,包括對話和指示格式
- 只訓練完成動作,忽略提示
- 壓縮資料集以提高訓練效率
- 高效參數微調 (PEFT) 支援功能,包括 QloRA
- 準備對話微調的模型和代碼化工具 (例如新增特殊符記)
下列程式碼會從 Hugging Face 載入 Gemma 模型和分析器,並初始化量化設定。
import torch
from transformers import AutoProcessor, AutoModelForImageTextToText, BitsAndBytesConfig
# Hugging Face model id
model_id = "google/gemma-3-4b-pt" # or `google/gemma-3-12b-pt`, `google/gemma-3-27-pt`
# Check if GPU benefits from bfloat16
if torch.cuda.get_device_capability()[0] < 8:
raise ValueError("GPU does not support bfloat16, please use a GPU that supports bfloat16.")
# Define model init arguments
model_kwargs = dict(
attn_implementation="eager", # Use "flash_attention_2" when running on Ampere or newer GPU
torch_dtype=torch.bfloat16, # What torch dtype to use, defaults to auto
device_map="auto", # Let torch decide how to load the model
)
# BitsAndBytesConfig int-4 config
model_kwargs["quantization_config"] = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=model_kwargs["torch_dtype"],
bnb_4bit_quant_storage=model_kwargs["torch_dtype"],
)
# Load model and tokenizer
model = AutoModelForImageTextToText.from_pretrained(model_id, **model_kwargs)
processor = AutoProcessor.from_pretrained("google/gemma-3-4b-it")
SFTTrainer
支援內建整合 peft
,可讓您輕鬆使用 QLoRA 有效調整 LLM。您只需建立 LoraConfig
並提供給訓練工具即可。
from peft import LoraConfig
peft_config = LoraConfig(
lora_alpha=16,
lora_dropout=0.05,
r=16,
bias="none",
target_modules="all-linear",
task_type="CAUSAL_LM",
modules_to_save=[
"lm_head",
"embed_tokens",
],
)
開始訓練前,您必須定義要在 SFTConfig
中使用的超參數,以及用於處理視覺處理作業的自訂 collate_fn
。collate_fn
會將含有文字和圖片的訊息轉換為模型可解讀的格式。
from trl import SFTConfig
args = SFTConfig(
output_dir="gemma-product-description", # directory to save and repository id
num_train_epochs=1, # number of training epochs
per_device_train_batch_size=1, # batch size per device during training
gradient_accumulation_steps=4, # number of steps before performing a backward/update pass
gradient_checkpointing=True, # use gradient checkpointing to save memory
optim="adamw_torch_fused", # use fused adamw optimizer
logging_steps=5, # log every 5 steps
save_strategy="epoch", # save checkpoint every epoch
learning_rate=2e-4, # learning rate, based on QLoRA paper
bf16=True, # use bfloat16 precision
max_grad_norm=0.3, # max gradient norm based on QLoRA paper
warmup_ratio=0.03, # warmup ratio based on QLoRA paper
lr_scheduler_type="constant", # use constant learning rate scheduler
push_to_hub=True, # push model to hub
report_to="tensorboard", # report metrics to tensorboard
gradient_checkpointing_kwargs={
"use_reentrant": False
}, # use reentrant checkpointing
dataset_text_field="", # need a dummy field for collator
dataset_kwargs={"skip_prepare_dataset": True}, # important for collator
)
args.remove_unused_columns = False # important for collator
# Create a data collator to encode text and image pairs
def collate_fn(examples):
texts = []
images = []
for example in examples:
image_inputs = process_vision_info(example["messages"])
text = processor.apply_chat_template(
example["messages"], add_generation_prompt=False, tokenize=False
)
texts.append(text.strip())
images.append(image_inputs)
# Tokenize the texts and process the images
batch = processor(text=texts, images=images, return_tensors="pt", padding=True)
# The labels are the input_ids, and we mask the padding tokens and image tokens in the loss computation
labels = batch["input_ids"].clone()
# Mask image tokens
image_token_id = [
processor.tokenizer.convert_tokens_to_ids(
processor.tokenizer.special_tokens_map["boi_token"]
)
]
# Mask tokens for not being used in the loss computation
labels[labels == processor.tokenizer.pad_token_id] = -100
labels[labels == image_token_id] = -100
labels[labels == 262144] = -100
batch["labels"] = labels
return batch
您現在已擁有建立 SFTTrainer
所需的所有建構區塊,可以開始訓練模型了。
from trl import SFTTrainer
trainer = SFTTrainer(
model=model,
args=args,
train_dataset=dataset,
peft_config=peft_config,
processing_class=processor,
data_collator=collate_fn,
)
呼叫 train()
方法開始訓練。
# Start training, the model will be automatically saved to the Hub and the output directory
trainer.train()
# Save the final model again to the Hugging Face Hub
trainer.save_model()
請務必釋放記憶體,才能測試模型。
# free the memory again
del model
del trainer
torch.cuda.empty_cache()
使用 QLoRA 時,您只需訓練轉接器,而非完整模型。也就是說,在訓練期間儲存模型時,您只會儲存適應器權重,而非完整模型。如果您想儲存完整模型,以便搭配 vLLM 或 TGI 等服務堆疊使用,可以使用 merge_and_unload
方法將轉接器權重合併至模型權重,然後使用 save_pretrained
方法儲存模型。這會儲存可用於推論的預設模型。
from peft import PeftModel
# Load Model base model
model = AutoModelForImageTextToText.from_pretrained(model_id, low_cpu_mem_usage=True)
# Merge LoRA and base model and save
peft_model = PeftModel.from_pretrained(model, args.output_dir)
merged_model = peft_model.merge_and_unload()
merged_model.save_pretrained("merged_model", safe_serialization=True, max_shard_size="2GB")
processor = AutoProcessor.from_pretrained(args.output_dir)
processor.save_pretrained("merged_model")
測試模型推論並產生產品說明
訓練完成後,您需要評估及測試模型。您可以從測試資料集中載入不同的樣本,並針對這些樣本評估模型。
import torch
# Load Model with PEFT adapter
model = AutoModelForImageTextToText.from_pretrained(
args.output_dir,
device_map="auto",
torch_dtype=torch.bfloat16,
attn_implementation="eager",
)
processor = AutoProcessor.from_pretrained(args.output_dir)
您可以提供產品名稱、類別和圖片,藉此測試推論功能。sample
包含漫威的動作公仔。
import requests
from PIL import Image
# Test sample with Product Name, Category and Image
sample = {
"product_name": "Hasbro Marvel Avengers-Serie Marvel Assemble Titan-Held, Iron Man, 30,5 cm Actionfigur",
"category": "Toys & Games | Toy Figures & Playsets | Action Figures",
"image": Image.open(requests.get("https://m.media-amazon.com/images/I/81+7Up7IWyL._AC_SY300_SX300_.jpg", stream=True).raw).convert("RGB")
}
def generate_description(sample, model, processor):
# Convert sample into messages and then apply the chat template
messages = [
{"role": "system", "content": [{"type": "text", "text": system_message}]},
{"role": "user", "content": [
{"type": "image","image": sample["image"]},
{"type": "text", "text": user_prompt.format(product=sample["product_name"], category=sample["category"])},
]},
]
text = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
# Process the image and text
image_inputs = process_vision_info(messages)
# Tokenize the text and process the images
inputs = processor(
text=[text],
images=image_inputs,
padding=True,
return_tensors="pt",
)
# Move the inputs to the device
inputs = inputs.to(model.device)
# Generate the output
stop_token_ids = [processor.tokenizer.eos_token_id, processor.tokenizer.convert_tokens_to_ids("<end_of_turn>")]
generated_ids = model.generate(**inputs, max_new_tokens=256, top_p=1.0, do_sample=True, temperature=0.8, eos_token_id=stop_token_ids, disable_compile=True)
# Trim the generation and decode the output to text
generated_ids_trimmed = [out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]
output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
return output_text[0]
# generate the description
description = generate_description(sample, model, processor)
print(description)
總結與後續步驟
本教學課程說明如何使用 TRL 和 QLoRA 微調 Gemma 模型,以便執行視覺任務,特別是產生產品說明。接著請查看下列文件: