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How to fine tune Llama 2 LLM models just 5 minutes

How to easily fine-tune Llama 2 LLM models just 5 minutes

If you are interested in learning more about how to fine-tune large language models such as Llama 2 created by Meta. You are sure to enjoy this quick video and tutorial created by Matthew Berman on how to fine-tune Llama 2 in just five minutes.  Fine-tuning AI models, specifically the Llama 2 model, has become an essential process for many businesses and individuals alike.

Fine tuning an AI model involves feeding the model with additional information to train it for new use cases, provide it with more business-specific knowledge, or even to make it respond in certain tones. This article will walk you through how you can fine-tune your Llama 2 model in just five minutes, using readily available tools such as Gradient and Google Colab.

Gradient is a user-friendly platform that offers $10 in free credits, enabling users to integrate AI models into their applications effortlessly. The platform facilitates the fine-tuning process, making it more accessible to a wider audience. To start, you need to sign up for a new account on Gradient’s homepage and create a new workspace. It’s a straightforward process that requires minimal technical knowledge.

Gradient AI

“Gradient makes it easy for you to personalize and build on open-source LLMs through a simple fine-tuning and inference web API. We’ve created comprehensive guides and documentation to help you start working with Gradient as quickly as possible. The Gradient developer platform provides simple web APIs for tuning models and generating completions. You can create a private instance of a base model and instruct it on your data to see how it learns in real time. You can access the web APIs through a native CLI, as well as Python and Javascript SDKs.  Let’s start building! “

How to easily fine tune Llama 2

The fine-tuning process requires two key elements: the workspace ID and an API token. Both of these can be easily located on the Gradient platform once you’ve created your workspace. Having these in hand is the first step towards fine-tuning your Llama 2 model.

Other articles we have written that you may find of interest on the subject of fine tuning LLM AI models :

 

Google Colab

The next step takes place on Google Colab, a free tool that simplifies the process by eliminating the need for any coding from the user. Here, you will need to install the Gradient AI module and set the environment variables. This sets the stage for the actual fine-tuning process. Once the Gradient AI module is installed, you can import the Gradient library and set the base model. In this case, it is the Nous-Hermes, a fine-tuned version of the Llama 2 model. This base model serves as the foundation upon which further fine-tuning will occur.

Creating the model adapter

The next step is the creation of a model adapter, essentially a copy of the base model that will be fine-tuned. Once this is set, you can run a query. This is followed by running a completion, which is a prompt and response, using the newly created model adapter. The fine-tuning process is driven by training data. In this case, three samples about who Matthew Berman is were used. The actual fine-tuning occurs over several iterations, three times in this case, using the same dataset each time. The repetition ensures that the model is thoroughly trained and able to respond accurately to prompts.

Checking your fine tuned AI model

After the fine-tuning, you can generate the prompt and response again to verify if the model now has the custom information you wanted it to learn. This step is crucial in assessing the effectiveness of the fine-tuning process. Once the process is complete, the adapter can be deleted. However, if you intend to use the fine-tuned model for personal or business use, it is advisable to keep the model adapter.

Using ChatGPT to generate the datasets

For creating the data sets for training, OpenAI’s ChatGPT is a useful tool as it can help you generate the necessary data sets efficiently, making the process more manageable. Fine-tuning your Llama 2 model is a straightforward process that can be accomplished in just five minutes, thanks to platforms like Gradient and tools like Google Colab. The free credits offered by Gradient make it an affordable option for those looking to train their own models and use their inference engine.

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Easily install custom AI Models locally with Ollama

Easily install custom AI Models locally with Ollama

If you are just getting started with large language models and would like to easily install different AI models currently available you should deftly check out Ollama. It’s really easy-to-use and takes just a few minutes to install and set up your first large language model. One word of warning is that your computer will need at least 8GB RAM and as much as you can spare for some models, as LLMs use large amounts of memory for each request.

Ollama currently supports easy installation of a wide variety of AI models including : llama 2, llama 2-uncensored, codellama, codeup, everythinglm, falcon, llama2-chinese, mistral, mistral-openorca, samantha-mistral, stable-beluga, wizardcoder and more. however you can also install custom AI models locally with Ollama as well.

Installing custom AI models locally with Ollama

Ollama is an AI model management tool that allows users to easily install and use custom models. One of the key benefits of Ollama is its versatility. While it comes pre-loaded with a variety of models, it also allows users to install custom models that are not available in the Ollama library. This opens up a world of possibilities for developers and researchers to experiment with different models and fine-tunes.

Other articles we have written that you may find of interest on the subject of Ollama :

One such custom model that can be installed in Ollama is Jackalope. Jackalope is a 7B model, a fine-tuning of the Mistral 7B model. It is recommended to get the quantized version of the model, specifically in GGUF format. Formerly known as GGML, GGUF is a quantized version of models used by the project LLaMA CPP, which Ollama uses for models.

The process of installing Jackalope, or any other custom model in Ollama, starts with downloading the model and placing it in a model’s folder for processing. Once the model is downloaded, the next step is to create a model file. This file includes parameters and points to the downloaded file. It also includes a template for a system prompt that users can fill out when running the model.

After creating and saving the model file, the process of creating a model using the model file begins. This process includes passing the model file, creating various layers, writing the weights, and finally, seeing a success message. Once the process is complete, the new model, in this case, Jackalope, can be seen in the model list and run just like any other model.

While Ollama offers a significant degree of flexibility in terms of the models it can handle, it’s important to note that some models may not work. However, fine-tunes of LLaMA2, Mistral 7B, and Falcon models should work. This limitation, while it may seem restrictive, still allows users to try out a vast array of different models from the hugging face hub.

Ollama provides a user-friendly platform for installing and using custom AI models. The process, while it may seem complex at first glance, is straightforward and allows users to experiment with a variety of models. Whether it’s the Jackalope model or any other custom model, the possibilities are vast with Ollama. However, users should be aware of potential limitations with some models and ensure they are using compatible models for optimal performance.

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Llama 2 70B vs Zephyr-7B LLM models compared

Llama 2 70B vs Zephyr-7B LLM models compared

A new language model known as Zephyr has been created. The Zephyr-7B-α large language model, has been designed to function as helpful assistants, providing a new level of interaction and utility in the realm of AI. This Llama 2 70B vs Zephyr-7B overview guide and comparison video will provide more information on the development and performance of Zephyr-7B. Exploring its training process, the use of Direct Preference Optimization (DPO) for alignment, and its performance in comparison to other models.  In Greek mythology, Zephyr or Zephyrus is the god of the west wind, often depicted as a gentle breeze bringing in the spring season.

Zephyr-7B-α, the first model in the Zephyr series, is a fine-tuned version of Mistral-7B-v0.1. The model was trained on a mix of publicly available, synthetic datasets using Direct Preference Optimization (DPO), a technique that has proven to be effective in enhancing the performance of language models. Interestingly, the developers found that removing the in-built alignment of these datasets boosted performance on MT Bench and made the model more helpful. However, this also means that the model is likely to generate problematic text when prompted to do so, and thus, it is recommended for use only for educational and research purposes.

Llama 2 70B vs Zephyr-7B

If you are interested in learning more the Prompt Engineering YouTube channel has created a new video comparing it with  the massive Llama 2 70B AI model.

 Previous articles we have written that you might be interested in on the subject of the Mistral and Llama 2 AI models :

The initial fine-tuning of Zephyr-7B-α was carried out on a variant of the UltraChat dataset. This dataset contains a diverse range of synthetic dialogues generated by ChatGPT, providing a rich and varied source of data for training. The model was then further aligned with TRL’s DPOTrainer on the openbmb/UltraFeedback dataset, which contains 64k prompts and model completions that are ranked by GPT-4.

It’s important to note that Zephyr-7B-α has not been aligned to human preferences with techniques like RLHF or deployed with in-the-loop filtering of responses like ChatGPT. This means that the model can produce problematic outputs, especially when prompted to do so. The size and composition of the corpus used to train the base model (mistralai/Mistral-7B-v0.1) are unknown, but it is likely to have included a mix of Web data and technical sources like books and code.

When it comes to performance, Zephyr-7B-α holds its own against other models. A comparison with the Lama 270 billion model, for instance, shows that Zephyr’s development and training process has resulted in a model that is capable of producing high-quality outputs. However, as with any AI model, the quality of the output is largely dependent on the quality and diversity of the input data.

Testing of Zephyr’s writing, reasoning, and coding abilities has shown promising results. The model is capable of generating coherent and contextually relevant text, demonstrating a level of understanding and reasoning that is impressive for a language model. Its coding abilities, while not on par with a human coder, are sufficient for basic tasks and provide a glimpse into the potential of AI in the field of programming.

The development and performance of the Zephyr-7B-α AI model represent a significant step forward in the field of AI language models. Its training process, use of DPO for alignment, and performance in comparison to other models all point to a future where AI models like Zephyr could play a crucial role in various fields, from education and research to programming and beyond. However, it’s important to remember that Zephyr, like all AI models, is a tool and its effectiveness and safety depend on how it is used and managed.

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Volkswagen ID.4 and ID.5 models updated

Volkswagen ID.4 and ID.5

Volkswagen has announced that it is updating its ID.4 and ID.5 models, both models now come with a new generation Infotainment system and there is also a new drive system for all of the Pro and GTX models in the range.

Imelda Labbé, Member of the Volkswagen Brand Board of Management with responsibility for Sales, Marketing and Aftersales, says: “Our customers can look forward to exceptional technical performance with the new ID.4 and the new ID.5. Software and infotainment are state-of-the-art. In addition, there are extensive assistance systems that leave nothing to be desired. The further improved ID.4 and ID.5 models are at the top of the segment with their new technologies and the acknowledged balance of driving, comfort and spaciousness.”

Volkswagen has significantly enhanced the cockpit landscape of the ID.4 and ID.5. The focus here was on intuitive operation. Against this background, both product lines have received brand new latest-generation software which is much faster and offers more functions. In addition, both models are equipped with a new standard infotainment system with a screen diagonal that has been increased to 32.8 centimetres (12.9 inches). The infotainment system impresses with a completely new menu structure while the Digital Cockpit (digital instruments as standard) and the optional augmented reality head-up display have been enhanced. Touch sliders for the air conditioning and volume control are now illuminated and the multifunction steering wheel with new operating logic is also new.

You can find out more information about the new Volkswagen ID.4 and ID.5 models over at Volakwagen at the link below, the car maker is now taking orders on them ahead of them going on sale.

Source VW

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