LLAMA-3 🦙: EASIET WAY To FINE-TUNE ON YOUR DATA 🙌
TLDRThe video introduces LLaMa-3, an open weights model, and discusses the benefits of fine-tuning it with one's own dataset. Several tools for fine-tuning are mentioned, including Auto Train, LLaMa Factory, and Unso, with a focus on the latter for its efficiency and speed. The video provides a step-by-step guide on using Unso's official notebook for fine-tuning, covering installation, setting training parameters, and formatting the dataset. It also demonstrates how to perform inference using the fine-tuned model and how to save and use it for future tasks. The video concludes by highlighting Unso's optimized memory usage and speed, making it an excellent choice for users with GPU constraints.
Takeaways
- 🦙 **LLaMa-3 Model**: The video discusses the fine-tuning of the LLaMa-3 model, an open weights model, to better suit one's own dataset.
- 🔧 **Fine-Tuning Options**: Several tools are available for fine-tuning, including Auto Train, LLaMa Factory, and UnSo, with UnSo offering up to 30 times faster training.
- 📚 **End-to-End Guide**: The video provides an end-to-end guide using UnSo's official notebook, which is user-friendly and covers everything from installation to inference.
- 💻 **Local Machine Training**: Training can be done locally with the necessary packages installed, and an Nvidia GPU is required for optimal performance.
- 🔗 **GitHub Repository**: The process involves cloning the UnSo GitHub repository and installing packages based on the hardware available.
- 🔢 **Training Parameters**: It's important to set up training parameters such as max sequence length and data types, with 4-bit quantization being used for efficiency.
- 📈 **Model Selection**: The UnSo version of LLaMa-3 already includes LoRA adapters, but if using a different model from Hugging Face, a token ID is needed for gated models.
- 📝 **Data Formatting**: The dataset must be structured with instructions, input, and output in a specific format for the model to understand and learn from the data.
- 🚀 **Efficient Training**: UnSo is highlighted for its optimized memory usage and speed, with training on a T4 GPU using under 60% of available resources.
- ⏱️ **Training Time**: The video demonstrates a quick training example for 60 steps, but for better learning, the model should be run for more steps or epochs.
- 📊 **Inference Interface**: UnSo offers a simple interface for inference, allowing the use of the model for tasks like continuing a sequence or answering questions about famous landmarks.
Q & A
What is LLaMA-3?
-LLaMA-3 is an open weights model that can be fine-tuned for specific datasets to improve its performance on those datasets.
What are some tools mentioned for fine-tuning LLaMA-3?
-The tools mentioned for fine-tuning LLaMA-3 include Auto Train, LLaMA Factory, and unslot.
What is unslot and how does it help in fine-tuning models?
-Unslot is a tool that offers up to 30 times faster training on the pair version. It provides an official notebook that covers end-to-end training in a user-friendly way and is optimized for memory usage and speed.
What hardware is recommended for using unslot?
-An Nvidia GPU is recommended for using unslot, as it does not yet support Apple silicon. Additionally, a GPU with support for CUDA is needed for local machine training.
How does one install required packages for unslot?
-To install required packages for unslot, one needs to clone the GitHub repo of unslot and then install different types of packages depending on the hardware they 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 at once. LLaMA-3 supports up to 8,000 tokens out of the box, but for datasets with shorter text, a lower max sequence length like 248 tokens can be used.
How does one format their training data for fine-tuning with unslot?
-The training data should be structured with three columns: instruction, user input, and model output. The data needs to be transformed into a single text column with special tokens for instruction, input, and response sections.
What is the role of 4-bit quantization in fine-tuning with unslot?
-4-bit quantization is a technique used by unslot to optimize the model's efficiency during fine-tuning. It helps in reducing the computational resources required for training without significantly compromising the model's performance.
How does unslot handle memory usage during training?
-Unslot is optimized for memory usage and speed. It uses less Video RAM (VRAM) during training, even on free GPUs like the T4 available on Google Colab.
What are the steps to perform inference with a fine-tuned LLaMA-3 model using unslot?
-To perform inference, one needs to use the fast language model class from unslot, provide the trained model, tokenize the input using the correct format, and then call the generate function with the tokenized inputs and desired parameters.
How can the fine-tuned LLaMA-3 model be saved and used later?
-The fine-tuned model can be saved to the Hugging Face Hub or locally. When saving, the LoRA adapters are merged with the model, and the model can be saved in different formats like float16 for VM inference or as a GGF file for use with LLaMA CPP or Go LLaMA.
What are the advantages of using unslot for inference compared to other options?
-Unslot offers a very simple interface for inference and is optimized for speed and memory usage. While other options like Auto Model for causal LLM inference exist, using unslot for inference is recommended as it is faster and more efficient.
Outlines
🚀 Introduction to Fine-Tuning Lama 3 with Unslot
The video begins by highlighting Lama 3 as an impressive open weights model, but suggests that a personalized fine-tuned version could be even better. It introduces several tools for fine-tuning, including AutoTrain, Xelot Lama Factory, and Unslot, which promises up to 30 times faster training. The presenter commits to creating a series of videos about fine-tuning Lama 3 and starts with Unslot, using its official notebook for its comprehensive and user-friendly approach. The process involves installing necessary packages, setting up training parameters, and formatting the dataset correctly. The video emphasizes the need for an Nvidia GPU, as Apple silicon support is not yet available, and guides viewers through cloning the Unslot GitHub repo and installing packages accordingly.
📚 Formatting and Training with Unslot
The paragraph explains the need to format the training set into three columns: instruction, user input, and model output. It details the process of downloading and mapping the dataset to match this format, with special tokens for instructions, inputs, and responses. 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 like the optimizer and learning rate schedule. The training process is shown to be memory and speed optimized, especially when using a T4 GPU on Google Colab. The training loss is monitored to ensure the model is learning, and adjustments to the learning rate and batch size are suggested for better convergence.
🤖 Inference and Model Saving with Unslot
The video moves on to inference using the trained model with Unslot, showcasing a simple interface that requires the model and tokenized input in the Alpaca format. It demonstrates generating responses and using the GPU for efficiency. The importance of saving the trained model is emphasized, with options to push it to the Hugging Face Hub or save it locally. The video also covers how to load the model with the Lora adapters for inference and highlights the flexibility of using other inference options like Auto Model for causal LM after training with Unslot. Additionally, it mentions the ability to convert the model to ONNX for use with LLM CPP or Go LLaMa, with quantization options available.
📢 Conclusion and Next Steps
The video concludes by inviting viewers to share any issues or questions in the comments section and thanks them for watching. It teases the next video, which will cover Auto Train, another tool for fine-tuning without delving into code. The presenter expresses optimism about the availability of different packages for inference on L3 and the capability to fine-tune them, highlighting the potential for users to start with these tools on day zero.
Mindmap
Keywords
💡Fine-tune
💡LLaMA-3
💡Auto Train
💡XeLoda
💡Unslot
💡4bit quantization
💡Hugging Face
💡Max sequence length
💡Lora Adapters
💡Inference
💡Streaming response
💡Model saving and pushing
Highlights
Lama 3 is an open weights model that can be fine-tuned for individual datasets.
Fine-tuning Lama 3 can be done using Auto Train, Xelot Lama Factory, or Unslot.
Unslot offers up to 30 times faster training on the pair version.
Unslot's official notebook is user-friendly and covers end-to-end training.
For local machine training, an Nvidia GPU is required, and Apple silicon is not yet supported.
Unslot uses Lora adapters for efficient fine-tuning.
If using a Hugging Face model, a Hugging Face token ID may be needed for gated models.
Data for training needs to be formatted with instructions, input, and output structured in a specific way.
Unslot's training process is optimized for memory usage and speed.
Training loss decreases as the model learns, indicating effective training.
The learning rate and batch size can be adjusted for better convergence.
Unslot provides a simple interface for inference after model training.
Models can be saved to Hugging Face Hub or locally after training.
Unslot does not need to be used for inference; models can be used with other options like Auto Model for Causal LM.
Unslot models can be converted to ONNX for use with LLMa CPP or Go LLaMa.
Unslot is an excellent option for fine-tuning LLMs with GPU constraints.
The Unslot package is highly optimized and has more optimizations coming.
For no-code platforms, Auto Train is recommended for an alternative to manual fine-tuning.