---
library_name: transformers
tags: []
---



---

# Fine-Tuning LLaMA-2-7b with QLoRA on Custom Dataset

This repository provides a setup and script for fine-tuning the LLaMA-2-7b model using QLoRA (Quantized Low-Rank Adaptation) with custom datasets. The script is designed for efficiency and flexibility in training large language models (LLMs) by leveraging advanced techniques such as 4-bit quantization and LoRA.

## Overview

The script fine-tunes a pre-trained LLaMA-2-7b model using a custom dataset, applying QLoRA techniques to optimize performance. It utilizes the `transformers`, `datasets`, `peft`, and `trl` libraries for model management, data processing, and training. The setup includes support for mixed precision training, gradient checkpointing, and advanced quantization techniques to enhance the efficiency of the fine-tuning process.

## Components

### 1. Dependencies

Ensure the following libraries are installed:
- `torch`
- `datasets`
- `transformers`
- `peft`
- `trl`

Install them using pip if they are not already available:
```bash
pip install torch datasets transformers peft trl
```

### 2. Model and Dataset

- **Model**: The base model used is `LLaMA-2-7b`. The script loads this model from a specified local directory.
- **Dataset**: The training data is loaded from a specified directory. The dataset should be formatted in a way that the `"text"` field contains the training examples.

### 3. QLoRA Configuration

QLoRA parameters are used to configure the quantization and adaptation process:
- **LoRA Attention Dimension (`lora_r`)**: 64
- **LoRA Alpha Parameter (`lora_alpha`)**: 16
- **LoRA Dropout Probability (`lora_dropout`)**: 0.1

### 4. BitsAndBytes Configuration

Quantization settings for the model:
- **Use 4-bit Precision**: True
- **Compute Data Type**: `float16`
- **Quantization Type**: `nf4`
- **Nested Quantization**: False

### 5. Training Configuration

Training parameters are defined as follows:
- **Output Directory**: `./results`
- **Number of Epochs**: 300
- **Batch Size**: 4
- **Gradient Accumulation Steps**: 1
- **Learning Rate**: 2e-4
- **Weight Decay**: 0.001
- **Optimizer**: `paged_adamw_32bit`
- **Learning Rate Scheduler**: `cosine`
- **Gradient Clipping**: 0.3
- **Warmup Ratio**: 0.03
- **Logging Steps**: 25
- **Save Steps**: 0

### 6. Training and Evaluation

The script includes preprocessing of the dataset, model initialization with QLoRA, and training using `SFTTrainer` from the `trl` library. It supports mixed precision training and gradient checkpointing to enhance training efficiency.

### 7. Usage Instructions

1. **Update File Paths**: Adjust `model_name`, `dataset_name`, and `new_model` paths according to your environment.
2. **Run the Script**: Execute the script in your Python environment to start the fine-tuning process.

```bash
python fine_tune_llama.py
```

3. **Monitor Training**: Use TensorBoard or similar tools to monitor the training progress.

### 8. Model Saving

After training, the model is saved to the specified directory (`new_model`). This trained model can be loaded for further evaluation or deployment.

## Example Configuration

Here’s an example configuration used for fine-tuning:

_hint_: the base model is: NousResearch/Llama-2-7b-chat-hf
_hint_: the dataset is: mlabonne/guanaco-llama2-1k

_hint_: I saved them on my local machine then laod them! you can directly download them from huggingface

```python
model_name = "/data/bio-eng-llm/llm_repo/NousResearch/Llama-2-7b-chat-hf" # the base model is: NousResearch/Llama-2-7b-chat-hf
dataset_name = "/data/bio-eng-llm/llm_repo/mlabonne/guanaco-llama2-1k" # the dataset is: mlabonne/guanaco-llama2-1k
new_model = "/data/bio-eng-llm/llm_repo/mlabonne/llama-2-7b-miniguanaco"

lora_r = 64
lora_alpha = 16
lora_dropout = 0.1

use_4bit = True
bnb_4bit_compute_dtype = "float16"
bnb_4bit_quant_type = "nf4"
use_nested_quant = False

output_dir = "./results"
num_train_epochs = 300
fp16 = False
bf16 = False
per_device_train_batch_size = 4
gradient_accumulation_steps = 1
gradient_checkpointing = True
max_grad_norm = 0.3
learning_rate = 2e-4
weight_decay = 0.001
optim = "paged_adamw_32bit"
lr_scheduler_type = "cosine"
max_steps = -1
warmup_ratio = 0.03
group_by_length = True
save_steps = 0
logging_steps = 25
```



# The entire Python training module:

```python


import os
import torch
from datasets import load_dataset
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    BitsAndBytesConfig,
    HfArgumentParser,
    TrainingArguments,
    pipeline,
    logging,
)
from peft import LoraConfig, PeftModel
from trl import SFTTrainer



import sys
import os

cwd = os.getcwd()
# sys.path.append(cwd + '/my_directory')
sys.path.append(cwd)


def setting_directory(depth):
    current_dir = os.path.abspath(os.getcwd())
    root_dir = current_dir
    for i in range(depth):
        root_dir = os.path.abspath(os.path.join(root_dir, os.pardir))
        sys.path.append(os.path.dirname(root_dir))
    return root_dir

#################################
#S:\Llavar_repo\LLaVA\NousResearch\Llama-2-7b-chat-hf

# The model that you want to train from the Hugging Face hub



model_name = "/data/bio-eng-llm/llm_repo/NousResearch/Llama-2-7b-chat-hf"


#model_name = setting_directory(2) + "\\Llavar_repo\\LLaVA\NousResearch\\Llama-2-7b-chat-hf"



# The instruction dataset to use
dataset_name = "/data/bio-eng-llm/llm_repo/mlabonne/guanaco-llama2-1k"

# Fine-tuned model name
new_model = "/data/bio-eng-llm/llm_repo/mlabonne/llama-2-7b-miniguanaco"

################################################################################
# QLoRA parameters
################################################################################

# LoRA attention dimension
lora_r = 64

# Alpha parameter for LoRA scaling
lora_alpha = 16

# Dropout probability for LoRA layers
lora_dropout = 0.1

################################################################################
# bitsandbytes parameters
################################################################################

# Activate 4-bit precision base model loading
use_4bit = True

# Compute dtype for 4-bit base models
bnb_4bit_compute_dtype = "float16"

# Quantization type (fp4 or nf4)
bnb_4bit_quant_type = "nf4"

# Activate nested quantization for 4-bit base models (double quantization)
use_nested_quant = False

################################################################################
# TrainingArguments parameters
################################################################################

# Output directory where the model predictions and checkpoints will be stored
output_dir = "./results"

# Number of training epochs
num_train_epochs = 300

# Enable fp16/bf16 training (set bf16 to True with an A100)
fp16 = False
bf16 = False

# Batch size per GPU for training
per_device_train_batch_size = 4

# Batch size per GPU for evaluation
per_device_eval_batch_size = 4

# Number of update steps to accumulate the gradients for
gradient_accumulation_steps = 1

# Enable gradient checkpointing
gradient_checkpointing = True

# Maximum gradient normal (gradient clipping)
max_grad_norm = 0.3

# Initial learning rate (AdamW optimizer)
learning_rate = 2e-4

# Weight decay to apply to all layers except bias/LayerNorm weights
weight_decay = 0.001

# Optimizer to use
optim = "paged_adamw_32bit"

# Learning rate schedule
lr_scheduler_type = "cosine"

# Number of training steps (overrides num_train_epochs)
max_steps = -1

# Ratio of steps for a linear warmup (from 0 to learning rate)
warmup_ratio = 0.03

# Group sequences into batches with same length
# Saves memory and speeds up training considerably
group_by_length = True

# Save checkpoint every X updates steps
save_steps = 0

# Log every X updates steps
logging_steps = 25

################################################################################
# SFT parameters
################################################################################

# Maximum sequence length to use
max_seq_length = None

# Pack multiple short examples in the same input sequence to increase efficiency
packing = False

# Load the entire model on the GPU 0
device_map = {"": 0}



################################################################################


# Load dataset (you can process it here)
dataset = load_dataset(dataset_name, split="train")

print(dataset[0].keys())  # This will print all the field names in your dataset

# Load tokenizer and model with QLoRA configuration
compute_dtype = getattr(torch, bnb_4bit_compute_dtype)

bnb_config = BitsAndBytesConfig(
    load_in_4bit=use_4bit,
    bnb_4bit_quant_type=bnb_4bit_quant_type,
    bnb_4bit_compute_dtype=compute_dtype,
    bnb_4bit_use_double_quant=use_nested_quant,
)

# Check GPU compatibility with bfloat16
if compute_dtype == torch.float16 and use_4bit:
    major, _ = torch.cuda.get_device_capability()
    if major >= 8:
        print("=" * 80)
        print("Your GPU supports bfloat16: accelerate training with bf16=True")
        print("=" * 80)

# Load base model
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    quantization_config=bnb_config,
    device_map=device_map
)
model.config.use_cache = False
model.config.pretraining_tp = 1

# Load LLaMA tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "right" # Fix weird overflow issue with fp16 training

# Load LoRA configuration
peft_config = LoraConfig(
    lora_alpha=lora_alpha,
    lora_dropout=lora_dropout,
    r=lora_r,
    bias="none",
    task_type="CAUSAL_LM",
)

# Set training parameters
training_arguments = TrainingArguments(
    output_dir=output_dir,
    num_train_epochs=num_train_epochs,
    per_device_train_batch_size=per_device_train_batch_size,
    gradient_accumulation_steps=gradient_accumulation_steps,
    optim=optim,
    save_steps=save_steps,
    logging_steps=logging_steps,
    learning_rate=learning_rate,
    weight_decay=weight_decay,
    fp16=fp16,
    bf16=bf16,
    max_grad_norm=max_grad_norm,
    max_steps=max_steps,
    warmup_ratio=warmup_ratio,
    group_by_length=group_by_length,
    lr_scheduler_type=lr_scheduler_type,
    report_to="tensorboard"
)

# Set supervised fine-tuning parameters

def preprocess_function(examples):
    return tokenizer(examples["text"], truncation=True, max_length=512)

tokenized_dataset = dataset.map(preprocess_function, batched=True)

trainer = SFTTrainer(
    model=model,
    train_dataset=tokenized_dataset,
    peft_config=peft_config,
    tokenizer=tokenizer,
    args=training_arguments,
    packing=packing,
)

# Train model
trainer.train()

# Save trained model
trainer.model.save_pretrained(new_model)
```





## License

This repository is licensed under the [MIT License](LICENSE).

## Contact

For questions or issues, please contact [author](mailto:forootan@example.com).

---

This README provides a comprehensive guide to understanding and utilizing the script for fine-tuning the LLaMA-2-7b model using advanced techniques. Adjust file paths and parameters as needed based on your specific requirements.












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