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LoRA with  code 🚀 using PEFT (parameter efficient fine-tuning)
LoRA with  code 🚀 using PEFT (parameter efficient fine-tuning)
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LoRA (Low-Rank Adaptation)
LoRA (Low-Rank Adaptation)
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LoRA adds low-rank matrices to specific layers and reduce the number of trainable parameters for efficient fine-tuning.
LoRA adds low-rank matrices to specific layers and reduce the number of trainable parameters for efficient fine-tuning.
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Code:
Code:
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Please install these libraries first:
Please install these libraries first:
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pip install peft 
pip install peft 
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pip install datasets
pip install datasets
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pip install transformers
pip install transformers
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```
from transformers import AutoModelForSequenceClassification, Trainer, TrainingArguments
from peft import LoraConfig, get_peft_model
from datasets import load_dataset

# Loading the pre-trained BERT model
model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)

# Configuring the LoRA parameters
lora_config = LoraConfig(
    r=8,
    lora_alpha=16, 
    lora_dropout=0.1, 
    bias="none" 
)

# Applying LoRA to the model
model = get_peft_model(model, lora_config)

# Loading dataset for classification
dataset = load_dataset("glue", "sst2")
train_dataset = dataset["train"]

# Setting the training arguments
training_args = TrainingArguments(
    output_dir="./results",
    per_device_train_batch_size=16,
    num_train_epochs=3,
    logging_dir="./logs",
)

# Creating a Trainer instance for fine-tuning
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
)

# Finally we can fine-tune the model
trainer.train()
```
from transformers import AutoModelForSequenceClassification, Trainer, TrainingArguments
from peft import LoraConfig, get_peft_model
from datasets import load_dataset

# Loading the pre-trained BERT model
model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)

# Configuring the LoRA parameters
lora_config = LoraConfig(
    r=8,
    lora_alpha=16, 
    lora_dropout=0.1, 
    bias="none" 
)

# Applying LoRA to the model
model = get_peft_model(model, lora_config)

# Loading dataset for classification
dataset = load_dataset("glue", "sst2")
train_dataset = dataset["train"]

# Setting the training arguments
training_args = TrainingArguments(
    output_dir="./results",
    per_device_train_batch_size=16,
    num_train_epochs=3,
    logging_dir="./logs",
)

# Creating a Trainer instance for fine-tuning
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
)

# Finally we can fine-tune the model
trainer.train()
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LoRA adds low-rank matrices to fine-tune only a small portion of the model and reduces training overhead by training fewer parameters.
LoRA adds low-rank matrices to fine-tune only a small portion of the model and reduces training overhead by training fewer parameters.
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We can perform efficient fine-tuning with minimal impact on accuracy and its suitable for large models where full-precision training is still feasible.
We can perform efficient fine-tuning with minimal impact on accuracy and its suitable for large models where full-precision training is still feasible.
LoRA with  code 🚀 using PEFT (parameter efficient fine-tuning)

LoRA (Low-Rank Adaptation)
LoRA adds low-rank matrices to specific layers and reduce the number of trainable parameters for efficient fine-tuning.

Code:
Please install these libraries first:
pip install peft 
pip install datasets
pip install transformers

```
from transformers import AutoModelForSequenceClassification, Trainer, TrainingArguments
from peft import LoraConfig, get_peft_model
from datasets import load_dataset

# Loading the pre-trained BERT model
model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)

# Configuring the LoRA parameters
lora_config = LoraConfig(
    r=8,
    lora_alpha=16, 
    lora_dropout=0.1, 
    bias="none" 
)

# Applying LoRA to the model
model = get_peft_model(model, lora_config)

# Loading dataset for classification
dataset = load_dataset("glue", "sst2")
train_dataset = dataset["train"]

# Setting the training arguments
training_args = TrainingArguments(
    output_dir="./results",
    per_device_train_batch_size=16,
    num_train_epochs=3,
    logging_dir="./logs",
)

# Creating a Trainer instance for fine-tuning
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
)

# Finally we can fine-tune the model
trainer.train()
```

LoRA adds low-rank matrices to fine-tune only a small portion of the model and reduces training overhead by training fewer parameters.
We can perform efficient fine-tuning with minimal impact on accuracy and its suitable for large models where full-precision training is still feasible.
🔥
Sengil Mnemonic111
👀
John6666