Llama.cpp Fine Tune for Custom Tasks and Datasets

Fine tuning Llama.cpp is a crucial step to tailor the model to your specific needs. You can fine tune Llama.cpp for custom tasks and datasets using a variety of techniques.

One approach is to use a smaller dataset that is more relevant to your task, such as a dataset of customer reviews for a specific product. This can help the model learn more efficiently and effectively.

Fine tuning Llama.cpp can be done using a variety of algorithms, including the Adam optimizer and the RMSprop optimizer. The choice of optimizer will depend on the specific task and dataset you are working with.

By fine tuning Llama.cpp, you can significantly improve the model's performance on your custom task or dataset.

Data Preparation is a crucial step in fine-tuning your llama.cpp model. Make sure your training and validation data sets consist of input and output examples for how you would like the model to perform.

To fine-tune models effectively, ensure a balanced and diverse dataset. This involves maintaining data balance, including various scenarios, and periodically refining training data to align with real-world expectations.

Different model types require a different format of training data. For Meta-Llama-3.1-70B-Instruct, the fine-tuning dataset must be formatted in the conversational format used by the Chat completions API.

Your training and validation data must be formatted as a JSON Lines (JSONL) document. This is a specific format that your model needs to function properly.

To delimit each prompt part by hashtags, you can use a function to process the prompts into tokenized ones. This will create input sequences of uniform length that are suitable for fine-tuning the language model.

To fine-tune LLaMA 2 models, you need to configure them properly. The LLaMA 2 models come in different flavors, which are 7B, 13B, and 70B, and your choice can be influenced by your computational resources.

Larger models require more resources, memory, processing power, and training time. You need to make sure you have enough resources to handle the chosen model.

To download the model you've been granted access to, you need to log in to the Hugging Face model hub. This is done by using the huggingface-cli login command.

The configuration will also require a bitsandbytes configuration, which will be defined later.

The original Llama model weights are not available, but they were leaked and adapted for use with the HuggingFace Transformers library.

We'll be using the decapoda-research weights, which are a pre-trained version of the Llama model. These weights are loaded using the LlamaForCausalLM class from the HuggingFace Transformers library.

The load_in_8bit=True parameter is used to load the model using 8-bit quantization, which reduces memory usage and improves inference speed.

To load the tokenizer for the same Llama model, we use the LlamaTokenizer class and set some additional properties for padding tokens.

The pad_token_id is set to 0 to represent unknown tokens, and the padding_side is set to "left" to pad sequences on the left side.

Once you've finished training, you'll need to merge the LoRA weights with your base model weights. This involves merging the adapter weights with the base model weights.

The LoRA adapter weights are output in a directory called final_checkpoint, which contains two files: adapter_config.json and adapter_model.bin.

The adapter_model.bin file is relatively small, at only 17mb, but it's an important part of the LoRA process.

Fine-tuning is a powerful technique that can help improve the performance of Llama models. It involves adjusting the model's parameters to better fit a specific task or dataset.

Fine-tuning can be used to improve the model's ability to output structured data, as seen in the example of fine-tuning for better structured outputs. This can be achieved through fine-tuning for function calling or structured output fine-tuning.

To fine-tune a model, you can use techniques such as gradient-based fine-tuning with PEFT or Modal for cloud compute. You can also use LlamaIndex for inference abstractions.

Here are some examples of fine-tuning:

Fine-tuning can also be used to improve the model's ability to generate embeddings, as seen in the example of finetuning embeddings. This can be achieved by generating a synthetic question/answer dataset using LlamaIndex over any unstructured context, fine-tuning the model, and evaluating the model.

Fine-tuning can also be used to improve the model's ability to understand humor and generate dad jokes, as seen in the example of fine-tuning for dad jokes. This can be achieved by using an N-Shot prompt to prime the model with more examples of the task.

Fine-tuning for structured outputs can be a game-changer. By fine-tuning a model, you can make it better at outputting structured data.

You can use fine-tuning for OpenAI function calling, which can improve the model's ability to produce structured outputs. This can be a huge advantage in many applications.

One example of fine-tuning for structured outputs is OpenAI function calling fine-tuning. This type of fine-tuning is designed to make the model more accurate and effective at producing structured outputs.

You can also fine-tune Llama2 for structured output, which can help the model produce more accurate and structured results. This can be especially useful in applications where structured data is critical.

Here are some specific examples of fine-tuning for structured outputs:

Fine-Tuning Text-to-SQL is a process that involves training a model on a specific dataset to improve its performance on a particular task. This can be done using a base model like OpenLLaMa.

The Llama 2 model can be fine-tuned for text-to-SQL tasks using various tools and technologies. For instance, PEFT is a tool that can be used for fine-tuning.

The stack for fine-tuning Llama 2 includes sql-create-context as the training dataset. This dataset is used to train the model on the specific task of text-to-SQL.

To fine-tune Llama 2, you can use Modal for cloud compute. This allows you to scale up your training process and improve the model's performance.

Here are some examples of fine-tuning Llama 2 for text-to-SQL:

Finetuning embeddings can be a game-changer for your RAG application.

You can achieve a 5-10% increase in retrieval evaluation metrics by finetuning the model. This is a significant boost that can make a real difference in your application's performance.

To get started, you'll need to generate a synthetic question/answer dataset using LlamaIndex over any unstructured context. This is the first step in the finetuning process.

The process itself consists of three main steps: generating a synthetic dataset, finetuning the model, and evaluating the model. It's a straightforward process that can be completed with the right tools and guidance.

By plugging this fine-tuned model into your RAG application with LlamaIndex, you can unlock its full potential and take your application to the next level.

Fine-tuning is all about asking the right questions, like "why" can a base model be improved. The Task: Dad-Joke LLM is a great example of this.

To fine-tune an LLM, you need to identify what it can't do on its own. In this case, the base model was unable to complete joke setups with punchlines.

Everyone has a different sense of humor, making comedy a fun example of fine-tuning. What may be funny to one person might not be funny to another.

The model kind of got the gist of the joke, but couldn't complete the task. This shows that fine-tuning can help spice up a language model that's too dry and literal.

An N-Shot prompt is a great way to prime a model with more examples of a task you want to perform. It's quicker to iterate on than fine-tuning, and can be done in a single run.

This approach involves giving the Large Language Model (LLM) more examples of the task, which can be processed in a single run. It's slightly more expensive, but faster than fine-tuning, which can take a couple of hours.

Fine-tuning can be a time-consuming process, but N-Shot prompting can help you get faster results. You can explore the full dataset to get a better understanding of what you're working with.

The example of an N-Shot prompt shows how it can be used to get more specific results, like finding a dad joke. With more examples, the model can learn to recognize patterns and provide better responses.

To fine-tune the LLaMA model, you'll need to prepare your training and validation data. This data should be formatted as a JSON Lines (JSONL) document.

Ensure all your training examples follow the expected format for inference, and maintain data balance by including various scenarios. Periodically refining your training data will help align with real-world expectations, leading to more accurate and balanced model responses.

The LLaMA 2 model comes in different flavors, including 7B, 13B, and 70B. Your choice of model size will be influenced by your computational resources.

To download the LLaMA 2 model, you'll need to be logged in to the Hugging Face model hub. You can do this by using the huggingface-cli login command, which is a requirement mentioned earlier.

The LLaMA 2 model comes in different flavors, including 7B, 13B, and 70B. Your choice of model will be influenced by your available computational resources.

Larger models require more resources, memory, processing power, and training time. This means you'll need to have sufficient resources to support the model you choose.

To download the model you've been granted access to, you can use the following function, which also requires a bitsandbytes configuration that we'll define later.

Finetuning a model means updating the model itself over a set of data to improve the model in various ways. This can include improving the quality of outputs, reducing hallucinations, and reducing latency/cost.

The core of our toolkit revolves around in-context learning / retrieval augmentation. This involves using models in inference mode without training the models themselves.

Finetuning can be used to "augment" a model with external data, and it can complement retrieval augmentation in various ways.

Finetuning a model is all about updating it to make it better at producing quality outputs, reducing errors, and learning more from data.

This process can improve the model's ability to memorize data holistically, which means it can retain more information and use it more effectively.

Finetuning can also help reduce latency and cost, making the model more efficient and practical to use.

In-context learning and retrieval augmentation are key concepts in our toolkit, and they involve using the model in inference mode without training it from scratch.

Finetuning can complement retrieval augmentation by providing additional information and improving the model's performance in various ways.