LLAMA-3 🦙: EASIET WAY To FINE-TUNE ON YOUR DATA 🙌

The video provides a comprehensive guide on how to fine-tune the LLaMA-3 model using the Unso framework, which promises up to 30 times faster training. The presenter walks viewers through the process, starting with installing necessary packages, setting up training parameters, and preparing the data set in a specific format. They then demonstrate how to use the SFT trainer from Hugging Face for supervised fine-tuning, highlighting the optimized memory usage and speed of Unso. The video also covers inference using the trained model, saving the model to Hugging Face Hub or locally, and converting it for use with other platforms. The presenter emphasizes the efficiency of Unso, especially for those with GPU constraints, and encourages viewers to try it out.

Takeaways

• 🦙 **LLaMa-3 Model Overview**: LLaMa-3 is a powerful open weights model that can be further enhanced by fine-tuning it to a specific dataset.
• 🔧 **Fine-Tuning Options**: There are multiple tools available for fine-tuning LLaMa-3, including Auto Train, LLaMa Factory, and unslot, with unslot offering up to 30 times faster training.
• 📚 **End-to-End Learning**: Unslot's official notebook is recommended for its comprehensive and user-friendly guide to fine-tuning various models, including LLaMa-3.
• 💻 **Local Machine Compatibility**: The fine-tuning process can be run locally, but requires an Nvidia GPU and does not yet support Apple silicon.
• 📏 **Training Parameters**: It's important to set up training parameters such as max sequence length and data types, with 4-bit quantization being an efficient option for fine-tuning.
• 📈 **Model Selection and Adoption**: Unslot has pre-integrated LLaMa-3 models with LoRA adapters, but for other models from Hugging Face, you may need to add LoRA adapters manually.
• 📝 **Data Formatting**: The training dataset should be structured with clear columns for instructions, user input, and model output, which is crucial for effective model training.
• 🚀 **Efficient Training**: Unslot is optimized for memory usage and speed, which is evident in the low VRAM usage during training.
• 📉 **Training Loss**: The training loss decreases as the model learns, indicating the effectiveness of the fine-tuning process.
• ⏱️ **Training Time**: For adequate learning, the model should be run for more epochs or steps, although the example provided runs for only 60 steps for demonstration purposes.
• 📊 **Memory Usage**: The training session in the example used approximately 4GB out of a possible 9GB, showcasing efficient memory management.
• 🔌 **Inference Interface**: Unslot provides a straightforward interface for inference, allowing the use of the trained model to generate responses.
• 📁 **Model Saving Options**: Once trained, the model can be saved locally or pushed to the Hugging Face Hub, with options to merge LoRA adapters for easier inference.
• 🔄 **Model Conversion**: Unslot allows for easy conversion of the model to various formats, such as GGF for use with LLaMa CPP or Go LLaMa, with quantization options available.
• ⚙️ **Optimized Implementation**: Unslot's performance is attributed to its optimized kernel implementation, which is expected to receive further enhancements.

Q & A

What is LLaMA-3 and what makes it better when fine-tuned?

LLaMA-3 is an open weights model that becomes more effective when fine-tuned to a user's specific data set, allowing it to better suit the user's needs.

What are the different tools mentioned for fine-tuning LLaMA-3?

The tools mentioned for fine-tuning LLaMA-3 include Auto Train, Xela, and Unso, with Unso being highlighted for its fast training capabilities.

Why is Unso considered an efficient option for fine-tuning LLaMA-3?

Unso is efficient due to its optimized memory usage and speed, which allows for faster training times and less GPU resource consumption.

What is the significance of the Alpaca dataset in the context of fine-tuning LLaMA-3?

The Alpaca dataset is used as an example to demonstrate how a user's data should be structured for fine-tuning. It includes columns for instructions, user input, and model output.

How does the user format their own data set for fine-tuning with Unso?

The user's data set should be formatted with instructions, input, and output in a single text column, following the Alpaca dataset structure, with special tokens for each section.

What is the role of the 'SFT trainer' in the fine-tuning process?

The SFT (Supervised Fine-Tuning) trainer from Hugging Face is used to perform the fine-tuning. It accepts the model object, tokenizer, data set, and other parameters to control the training process.

How does Unso's memory optimization benefit the training process?

Unso's memory optimization allows for efficient fine-tuning with lower GPU memory usage, which is beneficial for users with constraints on GPU resources.

What are the steps to perform inference using a fine-tuned LLaMA-3 model with Unso?

To perform inference, use the fast language model class from Unso, provide the trained model, tokenize the input in the Alpaca format, and then call the generate function with the tokenized inputs.

How can a fine-tuned LLaMA-3 model be saved for future use?

The model can be saved by pushing it to the Hugging Face Hub or saving it locally. The LoRA adapters are saved separately and merged with the model when needed for inference.

What are the options for using a fine-tuned LLaMA-3 model with other inference tools?

The fine-tuned model can be used with Auto Model for causal LM, and it can also be converted to ONNX or GGF for use with LLaMA CPP or Go LLaMA.

Why is it recommended to use Unso for inference even after the model is trained?

Unso is recommended for inference because it is optimized for speed and efficiency with LLaMA-3 models, whereas other classes might be slower.

Outlines

00:00

🚀 Introduction to Fine-Tuning Lama 3 with Unslot

The video introduces Lama 3 as an impressive open weights model and suggests that a personalized fine-tuned version could be even better. It outlines various tools for fine-tuning, including AutoTrain, Xela, and Unslot, with a focus on the latter for its speed and efficiency. The video promises a series of tutorials on fine-tuning Lama 3 and begins with a walkthrough using Unslot's official notebook, which is praised for its comprehensive and user-friendly approach. The process involves installing necessary packages, setting up training parameters, and choosing a model with LoRA adapters already integrated. The video also covers how to format a custom dataset for training, emphasizing the importance of structuring the data correctly.

05:02

📚 Formatting Data and Setting Up the Trainer

The paragraph explains the crucial step of formatting the dataset into a single text column with special tokens for instructions, inputs, and responses. It mentions the standard Alpaca dataset and other prompt templates like ChatML, advising viewers to structure their inputs correctly for training. The video then demonstrates setting up a Supervised Fine-Tuning (SFT) trainer from Hugging Face, which requires specifying the model, tokenizer, dataset, and other parameters for the training process. It highlights Unslot's optimized memory usage and speed, particularly when using a T4 GPU on Google Colab. The training loss is monitored to ensure the model is learning, and suggestions are given for adjusting the learning rate and batch size to improve convergence.

10:03

🤖 Model Inference and Saving the Trained Model

After training, the video shows how to perform inference using Unslot's simple interface. It details the process of tokenizing inputs in the Alpaca format and generating responses using the GPU. The model's response to a Fibonacci sequence continuation task is provided as an example. The video also discusses saving the trained model, either to the Hugging Face Hub or locally, and emphasizes that only the LoRA adapters are saved, not merged with the model. It explains how to load the LoRA adapters for inference and provides an example using a question about a famous tall tower in Paris. The video mentions alternative options for inference, such as using Auto Model for causal language modeling, and the ability to convert the model to ONNX for use with LLMs like LLMa CPP or GoLLaMa.

15:05

📢 Conclusion and Invitation for Feedback

The video concludes by summarizing the process of training and fine-tuning the Lama 3 model using Unslot, noting its efficiency even with GPU resource constraints. It encourages viewers to explore Unslot for its impressive optimization and upcoming enhancements. The presenter invites viewers to leave any issues or questions in the comments section and thanks them for watching, promising to see them in the next video.

Keywords

Fine-tune

Fine-tuning refers to the process of adjusting a pre-trained machine learning model to work specifically on a user's data set. In the context of the video, fine-tuning Lama 3 involves customizing the model to perform better on the user's specific tasks by training it with their data. This is crucial as it allows the model to 'learn' from the user's data and improve its performance on those tasks.

Lama 3

Lama 3 is an open weights model, which means it is a pre-trained machine learning model that is made publicly available for use. The video discusses how to enhance this model further by fine-tuning it on a user's own data set. Lama 3 is likely a reference to a specific version or iteration of a larger language model.

Auto Train

Auto Train is mentioned as one of the options for fine-tuning a model. It suggests a tool or service that allows users to train models with less hands-on coding, possibly automating the training process. This can be beneficial for users who want to fine-tune models but may not want to engage deeply with the technical details of the training process.

XeLoda

XeLoda is described as another option for fine-tuning models with advanced features. It is not a widely recognized term in the context of machine learning as of my knowledge cutoff in 2023, but in the video, it is presented as a tool that can be used for the fine-tuning process, indicating it may be a specific platform or library for machine learning model optimization.

Unslot

Unslot is highlighted as a tool that promises up to 30 times faster training on the GPU version. The video focuses on using Unslot for fine-tuning Lama 3, noting its efficiency in terms of speed and memory usage. Unslot seems to be a specialized tool for training models, particularly optimized for GPU acceleration.

4-bit quantization

4-bit quantization is a technique used to reduce the precision of the model's weights, which can significantly reduce the model's size and potentially improve training speed. In the video, it is mentioned as one of the options for efficient fine-tuning under the hood by Unslot, which suggests that it is a method to optimize model training performance.

Hugging Face

Hugging Face is a company that provides tools and libraries for natural language processing (NLP). In the context of the video, it is mentioned in relation to obtaining models and tokenizers for fine-tuning. Hugging Face is known for its Transformers library, which is a widely used resource in the machine learning community for tasks involving language.

Tokenizer

A tokenizer is a tool that breaks down text into individual units, known as tokens, which can be words, phrases, or other elements. In the video, the tokenizer is important for preparing the input data for the model in the correct format, particularly when using the alpaca format where instructions, inputs, and expected outputs are structured in a specific way.

Supervised Fine-Tuning (SFT)

SFT, or Supervised Fine-Tuning, is a method of training where the model is provided with labeled data and learns to predict the correct output based on this input. In the video, an SFT trainer from Hugging Face is set up to fine-tune the model using the user's data set, which involves mapping the data to a text column and specifying parameters for the training process.

VRAM

VRAM, or Video RAM, is the memory used by the GPU (Graphics Processing Unit). In the context of the video, VRAM usage is discussed in relation to the efficiency of Unslot, which is noted to use a relatively low amount of VRAM during training. This is significant as it indicates that the tool is optimized for GPU usage, allowing for faster and more efficient training of models.

Inference

Inference, in the context of machine learning, refers to the process of using a trained model to make predictions or generate outputs based on new, unseen data. The video explains how, once the model is trained using Unslot, it can be used for inference, providing examples of how the model generates responses to given inputs.

Highlights

Lama 3 is an open weights model that can be fine-tuned for personal use.

Auto Train, Xela, and Unslot are tools that can be used for fine-tuning Lama 3.

Unslot offers up to 30 times faster training on the pair version.

The official Unslot notebook is user-friendly and covers end-to-end training.

Unslot is optimized for memory usage and speed, even on free GPUs like the T4.

Lama 3 supports up to 8,000 tokens, but for shorter texts, a sequence length of 248 tokens is used.

Unslot uses Lora adapters for efficient fine-tuning.

If using a Hugging Face model, a Hugging Face token ID may be required for gated models.

Data sets need to be formatted with instructions, input, and output columns for training.

Missing input in the data set is acceptable as the instruction defines the expected output.

Unslot's training process is shown with a decreasing training loss, indicating learning.

The training can be further optimized by adjusting the learning rate and batch size.

Unslot provides a simple interface for inference after the model is trained.

The model can be saved to Hugging Face Hub or locally after training.

Unslot does not need to be used for inference; other options like Auto Model for Causal LM are available.

Unslot is more efficient for inference compared to the base Hugging Face Transformer model.

The model can be converted to ONNX for use with LLMa CPP or Go LLa.

Unslot is an excellent option for fine-tuning LLMs, especially with GPU constraints.

The video provides a comprehensive guide on fine-tuning Lama 3 using Unslot.