Unslot is an amazing tool that allows you to efficiently fine-tune the LLAMA-3 model on your own data set. Here's a step-by-step guide on how to do it:

1. Install Required Packages: Start by cloning the Unslot GitHub repository and installing the necessary packages based on your hardware configuration.

2. Set Up Training Parameters: Define your training parameters, such as the maximum sequence length, data types, and quantization method. Unslot uses Lora adapters to enable efficient fine-tuning.

3. Format Your Training Data: Ensure your data is structured in the required format, with columns for instruction, input, and output. Unslot provides examples using the Alpaca dataset, but you can adapt it to your own data.

4. Set Up the SFT Trainer: Create an SFT (Supervised Fine-Tuning) Trainer object from the Hugging Face Transformers library, specifying the model, tokenizer, dataset, and other training parameters.

5. Train the Model: Call the train() function on the SFT Trainer object to start the fine-tuning process. Unslot's optimized memory usage and speed ensure efficient training, even on limited GPU resources.

6. Perform Inference: After training, you can use the Unslot-specific FastLanguageModel class to generate responses from your fine-tuned model. Unslot also provides options to save the model and load the Lora adapters for future use.

7. Explore Additional Features: Unslot offers advanced features, such as the ability to use the model with other frameworks like PyTorch Lightning and the option to convert the model to GGML format for use with LLAMA-CPP or GoLLAMA.

The Unslot approach to fine-tuning LLAMA-3 is highly efficient, leveraging optimized memory usage and speed. It provides a user-friendly and comprehensive solution, making it an excellent choice for fine-tuning large language models on your own data.

First, we need to import the necessary classes from the unslot library:

from unslot.models.fast_language_model import FastLanguageModel

Next, we set up the training parameters:

• max_sequence_length: The maximum sequence length for the input. We set it to 248 tokens, as the dataset we're using has relatively short text.
• data_type: We use 4-bit quantization for efficient training.

max_sequence_length = 248
data_type = "4bit"

Unslot uses Lora adapters to enable efficient fine-tuning. There are two options:

1. Use a pre-loaded model from the unslot Hugging Face repository, which already has the Lora adapters merged.
2. Use a model from the Hugging Face repository and add the Lora adapters yourself.

In this case, we'll use the pre-loaded model, so we don't need to do any additional steps.

model = FastLanguageModel.from_pretrained("unslot/alpaca-7b")

If you need to use a different model and add the Lora adapters yourself, you can uncomment the following section and provide the necessary parameters.

# model_id = "your-hugging-face-model-id"
# model = FastLanguageModel.from_pretrained(model_id)
# model.add_lora_adapters()

Now we're ready to move on to the next step: formatting the training data.

To fine-tune the Lama 3 model using Unslot, you need to format your training data in a specific way. The data set used in the example has three columns: instruction, input, and output.

The instruction column contains the task description that the model should complete. The input column provides additional context for the task, and the output column contains the expected response from the model.

When formatting your own data, make sure to structure it in the same way, with the instruction, input, and output columns. If the input is missing for a particular example, that's fine, as the instruction alone can provide the necessary information for the model to generate the output.

After downloading the data, you need to transform the three columns into a single text string that follows a specific format. This format includes special tokens for the instruction, input, and response. The code in the example demonstrates how to perform this transformation, creating a single column that can be used to train the model.

It's important to note that while the example uses the standard Alpaca data set, you can also structure your data using other prompt templates, such as the ChatML format introduced by OpenAI. Just make sure to properly format your input examples, as they will be fed into the language model during training.

First, we need to set up the training parameters. We'll import the FastLanguageModel class from Unslot and define the max sequence length, data types, and quantization method.

Next, we'll handle the case where we need to add Lora adapters to the model. If we're using a model from the Hugging Face repository, we may need to provide a token to accept the terms of service.

Now, we need to format the training data. The dataset should have three columns: instruction, input, and output. We'll download the data from Hugging Face and map it to this format.

We'll then set up the Supervised Fine-Tuning Trainer from the Hugging Face Transformers library. This trainer will accept the model object, tokenizer, dataset, and other parameters like the optimizer, learning rate schedule, and output directory.

Finally, we'll call the train() function on the trainer object. We'll observe the training loss decreasing, indicating that the model is learning. Note that we're only running a small subset of the data for this example, but you'll want to run it for at least an epoch or two to get better results.

After training, we can save the model and load the Lora adapters for inference. Unslot also provides options to use the model with other frameworks like PyTorch Lightning and TensorFlow.

Once the model is trained, you can use it for inference. Unslot provides a simple interface for this:

1. Import the FastLanguageModel class from Unslot.
2. Provide the trained model and tell it to perform inference.
3. Tokenize the input using the Alpaca format (instruction, input, and expected output).
4. Move the inputs to the GPU to leverage the available resources.
5. Call the generate function, providing the tokenized inputs, the maximum number of tokens to generate, and whether to use caching.

The model will then generate a response based on the provided input. You can also use the TextStreamer class to stream the text response.

After training, you can save the model in different ways:

• Push the model to the Hugging Face Hub, which will save the Lora adapters separately.
• Save the model locally, again saving the Lora adapters separately.

To load the saved model for inference, you can set a flag to merge the Lora adapters with the model.

Unslot also provides alternative options for inference, such as using the AutoModelForCausalLM class from the Hugging Face Transformers library, which may be slower but allows you to use the model with other tools like LLaMA-CPP or GoLLaMA.

Once the model is trained, you can save it in various ways to use it for inference later. Unslot provides several options for saving and loading the fine-tuned model:

1. Save to Hugging Face Hub: You can push the fine-tuned model to the Hugging Face Hub, which allows you to share and use the model with others. To do this, you need to provide your Hugging Face token.

model.push_to_hub("your-model-name")

2. Save Locally: You can also save the model locally, which will only save the Lora adapters, not the entire model. This allows you to easily load the Lora adapters and merge them with the base model later.

model.save_pretrained("path/to/save/model")

3. Load Saved Lora Adapters: When you want to use the fine-tuned model for inference, you can load the saved Lora adapters and merge them with the base model.

model = FastLLamaForCausalLM.from_pretrained("path/to/base/model")
model.load_adapter("path/to/saved/model")

4. Convert to GGML Format: Unslot also provides the ability to convert the fine-tuned model to the GGML format, which can be used with tools like Llama-CPP or GoLlama. This allows you to use the model on CPU-only environments.

model.save_pretrained("path/to/save/model", quantization_method="nf16")

By leveraging these options, you can easily save, load, and use the fine-tuned model for various use cases, including deployment in different environments and sharing with the community.

The article provides a comprehensive guide on how to fine-tune the Lama 3 model using the Unslot package. The key points covered in the section are:

1. Unslot offers an efficient and optimized way to fine-tune Lama 3 and other language models, with features like reduced memory usage and faster training.
2. The article walks through the steps to set up the training environment, format the data, and train the model using the Unslot-specific classes and methods.
3. It also demonstrates how to perform inference using the fine-tuned model, both through the Unslot interface and by converting the model to other formats like ONNX for use with other inference tools.
4. The author highlights the advantages of Unslot, such as its ability to handle GPU constraints and the ease of use compared to other fine-tuning options like AutoTrain.
5. The article concludes by encouraging readers to explore Unslot and other fine-tuning tools, and invites them to reach out with any questions or issues they encounter.