A Really Quick Introduction to Fine Tune Jamba

Fine-tuning large language models like Jamba has become increasingly popular in recent times. By adapting these models to specific domains or tasks, we can unlock their full potential and achieve remarkable results. In this article, we will dive into the process of fine-tuning Jamba, a powerful language model, using a step-by-step approach. We'll explore the necessary tools, code snippets, and best practices to help you successfully fine-tune Jamba for your own projects. So, let's get started!

Getting Started with Jamba Fine-Tuning

First things first, let's make sure you have everything you need:

• Python 3.x installed (because who doesn't love Python? 🐍)
• Some basic Python skills (you got this! 💪)
• A general understanding of deep learning (don't worry, we'll keep it simple!)
• A GPU with enough memory (for those lightning-fast training sessions ⚡️)

Step 1: Install the Cool Libraries

To kick things off, we need to install a few awesome libraries. Open up your terminal and run these commands:

pip install datasets trl peft torch transformers bitsandbytes mamba_ssm

These libraries are like your trusty sidekicks, helping you load datasets, train models, and work with Jamba effortlessly.

Step 2: Import the Necessary Modules

In your Python script, let's import the modules we'll be using:

from datasets import load_dataset
from trl import SFTTrainer
from peft import LoraConfig
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, BitsAndBytesConfig
import mamba_ssm

Think of these modules as your tools for fine-tuning success!

Step 3: Set Up Quantization

To make sure we're using memory efficiently and speeding up training, let's configure quantization:

quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    llm_int4_skip_modules=["mamba"]
)

Don't forget to double-check the modeling_jamba.py file and make any necessary adjustments for the Fast Mamba Kernel.

Step 4: Load the Tokenizer and Dataset

Now, let's load the Jamba tokenizer and the dataset we'll be using for fine-tuning:

tokenizer = AutoTokenizer.from_pretrained("jamba")
dataset = load_dataset("Abirate/awesome_quotes", split="train")

In this example, we're using the "awesome_quotes" dataset from the Abirate repository. Feel free to choose any dataset that tickles your fancy!

Step 5: Set Up Training Arguments

Time to configure the training arguments for fine-tuning Jamba:

training_args = TrainingArguments(
    output_dir="./results",
    num_train_epochs=2,
    per_device_train_batch_size=2,
    gradient_accumulation_steps=8,
    optim="adamw_8bit",
    max_grad_norm=0.5,
    weight_decay=0.01,
    warmup_ratio=0.05,
    gradient_checkpointing=True,
    logging_dir='./logs',
    logging_steps=2,
    max_steps=100,
    group_by_length=True,
    lr_scheduler_type="cosine",
    learning_rate=5e-4
)

Feel free to tweak these arguments based on your specific needs and available resources.

Step 6: Configure LoRA

LoRA (Low-Rank Adaptation) is a nifty technique for efficiently fine-tuning large language models. Let's set up LoRA:

lora_config = LoraConfig(
    lora_alpha=32,
    lora_dropout=0.1,
    init_lora_weights=True,
    r=16,
    target_modules=["embed_tokens", "x_proj", "in_proj", "out_proj"],
    task_type="CAUSAL_LM",
    bias="all"
)

These settings will determine how LoRA is applied during the fine-tuning process.

Step 7: Load the Jamba Model

Finally, let's load the Jamba model with our custom configurations:

model = AutoModelForCausalLM.from_pretrained(
    "jamba",
    trust_remote_code=True,
    device_map='auto',
    attn_implementation="flash_attention_2",
    quantization_config=quantization_config,
    use_mamba_kernels=True
)

We're using the flash_attention_2 implementation and Mamba kernels for lightning-fast training! ⚡️

Step 8: Create the Trainer

Now, let's create our trainer object:

trainer = SFTTrainer(
    model=model,
    tokenizer=tokenizer,
    args=training_args,
    peft_config=lora_config,
    train_dataset=dataset,
    max_seq_length=512,
    dataset_text_field="quote",
)

The trainer will handle the fine-tuning process for us, making our lives easier!

Step 9: Start Fine-Tuning

It's time to unleash the power of fine-tuning! Let's start the training process:

trainer.train()

Sit back, relax, and watch as Jamba learns and adapts to your specific task. It's like watching a superhero in training!

And here is the complete code in the end:

from datasets import load_dataset
from trl import SFTTrainer
from peft import LoraConfig
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, BitsAndBytesConfig
import mamba_ssm

# Configure quantization
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    llm_int4_skip_modules=["mamba"]
)

# Load tokenizer and dataset
tokenizer = AutoTokenizer.from_pretrained("jamba")
dataset = load_dataset("Abirate/awesome_quotes", split="train")

# Configure training arguments
training_args = TrainingArguments(
    output_dir="./results",
    num_train_epochs=2,
    per_device_train_batch_size=2,
    gradient_accumulation_steps=8,
    optim="adamw_8bit",
    max_grad_norm=0.5,
    weight_decay=0.01,
    warmup_ratio=0.05,
    gradient_checkpointing=True,
    logging_dir='./logs',
    logging_steps=2,
    max_steps=100,
    group_by_length=True,
    lr_scheduler_type="cosine",
    learning_rate=5e-4
)

# Configure LoRA
lora_config = LoraConfig(
    lora_alpha=32,
    lora_dropout=0.1,
    init_lora_weights=True,
    r=16,
    target_modules=["embed_tokens", "x_proj", "in_proj", "out_proj"],
    task_type="CAUSAL_LM",
    bias="all"
)

# Load the Jamba model
model = AutoModelForCausalLM.from_pretrained(
    "jamba",
    trust_remote_code=True,
    device_map='auto',
    attn_implementation="flash_attention_2",
    quantization_config=quantization_config,
    use_mamba_kernels=True
)

# Create the trainer
trainer = SFTTrainer(
    model=model,
    tokenizer=tokenizer,
    args=training_args,
    peft_config=lora_config,
    train_dataset=dataset,
    max_seq_length=512,
    dataset_text_field="quote",
)

# Start fine-tuning
trainer.train()

Conclusion

There you have it! The complete code for fine-tuning Jamba, ready for you to run and enjoy. Remember to adjust the dataset, hyperparameters, and configurations based on your specific requirements.

I hope this code serves as a solid foundation for your fine-tuning adventures. Feel free to experiment, explore, and unleash the full potential of Jamba!

Happy fine-tuning, and may your AI models be as awesome as you are! 🚀✨