LLAMA-3 🦙: EASIET WAY To FINE-TUNE ON YOUR DATA 🙌
TLDRIn this informative video, the presenter introduces LLaMa-3, an open weights model, and discusses how to fine-tune it using various tools like Auto Train, LLaMa Factory, and Unslot. The focus is on Unslot, which offers up to 30 times faster training. The video provides a step-by-step guide on using Unslot's official notebook to fine-tune LLaMa-3, including setting up training parameters, formatting the training set, and using the SFT Trainer from Hugging Face's Transformer Library. The presenter also demonstrates how to perform inference with the fine-tuned model and save it for future use. The video highlights Unslot's optimized memory usage and speed, making it an excellent choice for those with GPU constraints. The presenter encourages viewers to try Unslot and offers to answer any questions in the comments section.
Takeaways
- 🦙 **LLaMa-3 Model**: Lama 3 is an open weights model that can be further enhanced by fine-tuning on your own dataset.
- 🛠️ **Fine-Tuning Options**: There are several tools available for fine-tuning, including Auto Train, Xela, and Unslot, with Unslot offering up to 30 times faster training.
- 📚 **Training Notebook**: Unslot's official notebook is recommended for its comprehensive and user-friendly guide on fine-tuning models.
- 💻 **Local Machine Training**: The training can be done locally, but requires an Nvidia GPU and the installation of necessary packages.
- 🔍 **Data Formatting**: The training data must be structured with specific columns for instructions, user input, and model output.
- 🧩 **Model Preparation**: Unslot uses LoRA adapters for efficient fine-tuning, and you can either use pre-merged models from Unslot or add LoRA to a Hugging Face model.
- 🔢 **Training Parameters**: Set up the max sequence length and data types, and choose the quantization method (e.g., 4-bit) for training.
- ⏱️ **Efficient Training**: Unslot optimizes for memory usage and speed, allowing training on less powerful GPUs like the T4 on Google Colab.
- 📉 **Training Loss**: The training loss should decrease over time, indicating that the model is learning from the data.
- 🔧 **Inference Interface**: Unslot provides a straightforward interface for inference, allowing you to generate responses using the trained model.
- 💾 **Model Saving**: The trained model can be saved locally or pushed to the Hugging Face Hub, with options to convert it for use with other inference tools.
Q & A
What is LLaMA-3 and how can it be improved for personal use?
-LLaMA-3 is an open weights model that can be fine-tuned using your own dataset to better suit your specific needs. Personalizing it can be done through various tools such as Auto Train, Xela, and Unso, with the latter promising up to 30 times faster training.
What are the advantages of using Unso for fine-tuning LLaMA-3?
-Unso offers optimized memory usage and speed, making it an excellent choice for fine-tuning LLaMA-3, especially when there are constraints on GPU resources.
How does the Unso official notebook help in fine-tuning LLaMA-3?
-The Unso official notebook provides an end-to-end guide in a user-friendly manner, covering all necessary steps to fine-tune LLaMA-3, making it accessible for users to follow along.
What are the required packages for fine-tuning LLaMA-3 on a local machine?
-To fine-tune LLaMA-3 on a local machine, you need to install the required packages by cloning the GitHub repo of Unso. The specific packages installed depend on the type of hardware you have.
What is the significance of the max sequence length in fine-tuning LLaMA-3?
-The max sequence length determines the maximum number of tokens the model can process. LLaMA-3 supports up to 8,000 tokens out of the box, but for datasets with shorter text, a reduced sequence length like 248 tokens can be used.
How does Unso utilize quantization for efficient fine-tuning?
-Unso uses 4-bit quantization under the hood, which is a method that reduces the precision of the model's weights to speed up training and inference without significantly impacting accuracy.
What is the process of adding LoRA adapters to a model for fine-tuning if using a model from Hugging Face?
-If using a model from Hugging Face that doesn't already have LoRA adapters, you need to provide your Hugging Face token ID, especially for gated models. You then define the necessary parameters or uncomment the relevant section of the code to add the adapters.
How should the training data be structured for fine-tuning LLaMA-3?
-The training data should be structured in three columns: instruction, user input, and model output. This structure is crucial as it directly feeds into the LLaMA-3 model for training.
What is the role of the Supervised Fine-Tuning (SFT) trainer from Hugging Face in the fine-tuning process?
-The SFT trainer is responsible for accepting the model object, tokenizer, dataset, and other parameters to control the training process, such as the optimizer and learning rate schedule, and performs the actual training.
How does Unso optimize memory usage during the training of LLaMA-3?
-Unso optimizes memory usage through its efficient implementation, which includes writing custom kernels to reduce the memory footprint, allowing it to use less than 60% of the available resources on a T4 GPU instance.
What are the options for saving a fine-tuned LLaMA-3 model?
-After training, the model can be saved either by pushing it to the Hugging Face Hub or saving it locally. Unso also allows direct conversion of the model to ONNX format for use with LLaMA CPP or Go LLaMA.
How can one perform inference using a fine-tuned LLaMA-3 model?
-Inference can be performed using the Fast Language Model class from Unso. The user provides the trained model and tokenizes the input according to the format used during training. The model then generates responses based on the input.
Outlines
🤖 Fine-Tuning Lama 3 with Unso
The video introduces the concept of fine-tuning the Lama 3 model using Unso, a tool that promises up to 30 times faster training. It discusses the process of fine-tuning using Unso's official notebook, which is user-friendly and comprehensive. The video covers the installation of required packages, setting up training parameters, and the option to use different models from Hugging Face. It also explains the process of formatting the training set and the importance of adhering to a specific structure for the data. The video concludes with a demonstration of how to use the fine-tuned model for inference.
📈 Training and Optimizing with Unso
This paragraph details the steps involved in setting up a supervised fine-tuning trainer using Hugging Face's library and an Unso-specific model object. It emphasizes the importance of formatting input examples correctly for training and discusses the setup of an SFT trainer, including specifying the model object, tokenizer, dataset, and other training parameters like the optimizer and learning rate schedule. The video also highlights Unso's optimization of memory usage and speed, especially when using a GPU. It demonstrates the training process, showing how the loss decreases over time, and touches on the possibility of adjusting the learning rate and batch size for better convergence. Finally, it explains how to perform inference using the trained model and the Unso interface.
📝 Inference and Model Saving with Unso
The video script explains how to use the trained model for inference by providing an example of continuing the Fibonacci sequence. It outlines the process of using the 'fast language model' class from Unso and tokenizing the input in the Alpaca format. The model's response is generated using the GPU for efficiency. The paragraph also discusses different methods for saving the trained model, either by pushing it to the Hugging Face Hub or saving it locally. It mentions the option to load the model with the Lora adapters for inference and the ability to use the model for inference without Unso, although it notes that Unso is recommended for better performance. The video concludes with additional options for using the trained model, such as converting it to a format compatible with LLMs like LLMa CPP or GoLLaMa.
📚 Conclusion and Further Assistance
The final paragraph of the video script offers a conclusion to the tutorial on fine-tuning the Lama 3 model with Unso. It encourages viewers to ask questions or report issues in the comment section if they encounter any difficulties. The video presenter thanks the viewers for watching and teases the next video, which will cover Auto Train, another tool for fine-tuning models without the need to manually run code blocks. The presenter expresses hope that the viewers found the video useful and looks forward to their next interaction.
Mindmap
Keywords
💡LLAMA-3
💡Fine-tune
💡Auto Train
💡XeLoda
💡Unslot
💡Quantization
💡Hugging Face
💡Tokenizer
💡Supervised Fine-Tuning (SFT)
💡Inference
💡Streaming Response
Highlights
Lama 3 is an open weights model that can be fine-tuned for personal use.
Auto Train, xelot Lama Factory, and unslot are tools for fine-tuning Lama 3.
Unslot offers up to 30 times faster training on the pair version.
Unslot's official notebook provides an end-to-end user-friendly guide for fine-tuning.
Nvidia GPU is required for local machine training, with no support for Apple silicon yet.
Unslot uses Lura adopters for efficient fine-tuning.
If using a Hugging Face model, a Hugging Face token ID might be needed for gated models.
Training data should be formatted with instructions, input, and output columns.
Unslot's training parameters include max sequence length and 4bit quantization.
The SFT trainer from Hugging Face is used for supervised fine-tuning.
Unslot optimizes memory usage and speed during training.
Training loss decreases as the model learns, indicating effective training.
Unslot provides a simple interface for inference after training.
The model can generate responses following the alpaca format during inference.
Unslot allows saving the model to Hugging Face Hub or locally.
Unslot supports streaming responses for real-time inference.
The model can be converted to ggf for use with llama CPP or go Lama.
Unslot is optimized for GPU usage, using under 60% of T4 GPU resources.
Auto Train is recommended for no-code platforms.
Unslot is a powerful option for fine-tuning with GPU constraints.
