Create fine-tuned models with NO-CODE for Ollama & LMStudio!

Tim Carambat
22 Jul 202421:51

TLDRTimothy Carat, founder of Implex Labs, introduces a no-code solution for fine-tuning AI models using 'anything llm'. The feature, available on the Dockerized version on GitHub, allows users to chat with any model and create a fine-tuned model locally. Despite not running locally, the process is cost-effective and user-friendly, offering a powerful tool for enhancing AI systems without the need for technical expertise. The video demonstrates how to fine-tune a model with as few as 14 chats, showcasing its accuracy and potential for customizing AI knowledge bases.

Takeaways

  • 🚀 Timothy Carat, the founder of Implex Labs, introduces a no-code method for fine-tuning AI models using 'Anything LLM'.
  • 💡 The feature for fine-tuning is available on the Dockerized version of the software on GitHub, not yet on the desktop app.
  • 💻 Users can interact with various AI models and create a fine-tuned model based on chat outputs, enhancing the model with local knowledge.
  • 🔒 The fine-tuning process is cloud-based and ensures data privacy with encryption and prompt deletion post-training.
  • 💰 The service is a one-time cost of $250, providing a .ggf file for local use, which is considered reasonable compared to other fine-tuning services.
  • 📈 Fine-tuning enhances the base model's ability to answer niche questions accurately and can be combined with RAG for even more powerful results.
  • 🔧 The process is designed for non-technical users who want to improve their AI models without the complexity of setting up a fine-tuning pipeline.
  • 📚 The script demonstrates the process using a workspace with documents and chats related to 'Anything LLM', including web search integration.
  • 🔄 The fine-tuned model is tested in both Ollama and LM Studio, showing improved accuracy in responses compared to the base model.
  • 🛠️ Instructions are provided for loading custom models into Ollama and LM Studio, showcasing the ease of use for non-technical users.
  • 🔗 The video concludes with a teaser for a future tutorial on how to perform local fine-tuning on consumer-grade GPUs.

Q & A

  • What is the main feature being introduced in the video?

    -The main feature being introduced is a no-code way to produce a fine-tuned model using the Anything LLM platform.

  • When will the feature be available on the desktop app version?

    -The feature will be available on the desktop app version when the version number is higher than 1.5.1.

  • Why is the fine-tuning process not running locally in the current demonstration?

    -The fine-tuning process is not running locally because GPUs for training are expensive and fine-tuning is complex, requiring specialized knowledge.

  • What are the two main reasons mentioned for not running fine-tuning locally?

    -The two main reasons are the cost of GPUs for training and the complexity of setting up a fine-tuning pipeline and infrastructure.

  • What is the difference between fine-tuning and RAG as explained in the video?

    -Fine-tuning is like educating and learning subjects, where information becomes background knowledge. RAG, on the other hand, is more about perfect citation accurate recall. Combining both provides a model that behaves in a certain way and can cite answers with background knowledge.

  • How many chats were used in the example to create a fine-tuned model?

    -In the example, 14 chats were used to create a fine-tuned model.

  • What is the cost mentioned for creating a fine-tuned model in the video?

    -The one-time cost mentioned for creating a fine-tuned model is $250.

  • How long does it take to complete the fine-tuning process in the video example?

    -In the video example, the fine-tuning process took about 22 minutes, from 3:30 to 3:52.

  • What is the size of the 8-bit quantized model file downloaded in the video?

    -The 8-bit quantized model file is about 8 gigs in size.

  • How can the fine-tuned model be used in applications like Olama and LM Studio?

    -The fine-tuned model can be loaded into Olama and LM Studio by following specific instructions provided in the video, which involves copying the model file into the appropriate directories and using command line or UI applications to load the model.

Outlines

00:00

🚀 Introduction to No-Code Fine-Tuning with Implex Labs

Timothy Carat, the founder of Implex Labs, introduces a groundbreaking no-code method for fine-tuning large language models (LLMs). He explains that this feature, although not yet available in the desktop app, will be accessible via the dockerized version on GitHub. The current desktop app version is 1.5.1, and the feature will be live once the version number is higher. Interested users can download the app from anythinglm.com. The process allows users to interact with any model and create a fine-tuned model based on chat outputs, which can then be loaded locally. However, the fine-tuning process itself does not run locally due to the costs and complexities associated with GPU training. Timothy also mentions that a future video will cover how to perform local fine-tuning on a consumer-grade GPU for those who prefer not to use the cloud-based service.

05:00

📈 Fine-Tuning vs. Rag: Understanding the Differences and Benefits

The script delves into the distinction between fine-tuning and Rag, using an analogy of education to explain how fine-tuning works. Fine-tuning is likened to acquiring background knowledge from reading, which isn't always explicitly cited but informs responses. Rag, on the other hand, is compared to having direct citations for answers. The combination of fine-tuning and Rag results in a system that behaves in a certain way and can provide accurate, well-informed responses. The process of obtaining a fine-tuned model is simplified and does not require technical knowledge, making it accessible to a broader audience. Privacy is emphasized, with data used solely for training and deleted immediately after the model is complete or if unfinished.

10:00

💰 The Cost and Process of Obtaining a Fine-Tuned Model

The video script outlines the cost and process of obtaining a fine-tuned model. The one-time cost for the service is $250, which includes the fine-tuned model file and instructions for running it locally in applications like AMA and LM Studio. The process is described as simple and quick, with the model being ready in under an hour. The script emphasizes the value of this cost compared to the expense and effort of obtaining a fine-tuned model through other means. It also mentions the option to export data for local fine-tuning for those who prefer a self-sufficient approach.

15:01

🔧 Loading a Custom Fine-Tuned Model into OLAMA and Testing Its Accuracy

The script provides a step-by-step guide on how to load a custom fine-tuned model into OLAMA, a platform for running LLMs. It details the process of downloading the model, extracting it, and setting up the necessary files. The script then demonstrates the difference in output quality between the fine-tuned model and the original Llama 3 model by asking what 'anything LM' is. The fine-tuned model provides an accurate response, showcasing the effectiveness of the fine-tuning process even with a small dataset of 14 chats.

20:02

🖥️ Transitioning to LM Studio: Loading and Utilizing the Fine-Tuned Model

The final part of the script focuses on loading the custom fine-tuned Llama 3 8B model into LM Studio, a user interface application for managing and running models. It explains the process of extracting the model files, placing them in the correct directory, and selecting the model within LM Studio. The script then shows how to use the model in a playground environment, emphasizing the importance of using the correct prompt template and settings. The effectiveness of the fine-tuned model is again demonstrated by asking it about 'anything LM,' receiving an accurate response that would not be possible with the base Llama 3 model.

🌟 Conclusion and Future Plans for Anything LM and Fine-Tuning

In conclusion, the script highlights the ease of obtaining and using a no-code fine-tuned model with Anything LM, OLAMA, and LM Studio. It emphasizes the potential for improving LLM responses by up to 20% with fine-tuning. The script also mentions that Anything LM is an open-source project with a growing community on GitHub. Lastly, it teases future content that will cover how to perform local fine-tuning on a GPU, providing an alternative for those who prefer not to use the cloud-based fine-tuning service.

Mindmap

Keywords

💡Fine-tuned model

A fine-tuned model in the context of machine learning refers to a pre-trained model that has been further trained on a specific task or dataset to improve its performance on that task. In the video, the creator discusses a novel no-code approach to fine-tuning large language models (LLMs), allowing users to enhance the model's understanding of specific topics or data through a streamlined process without the need for programming skills.

💡No-code

No-code is a term used to describe software platforms that allow users to create applications or systems without writing any code. In the video, the focus is on enabling users to fine-tune AI models without coding, which simplifies the process and makes it accessible to a broader audience, including those without a technical background.

💡Dockerized version

A Dockerized version refers to software that has been containerized using Docker, which allows it to run consistently across different computing environments. In the script, the Dockerized version of the software is mentioned as the platform where the new fine-tuning feature will be initially available, emphasizing the flexibility and portability of the application.

💡GPUs for training

GPUs, or Graphics Processing Units, are specialized hardware used for accelerating the computation of graphics and are also commonly used in machine learning for training models due to their parallel processing capabilities. The script mentions that fine-tuning is performed on cloud GPUs, indicating that the process is computationally intensive and requires significant processing power.

💡RAG (Retrieval-Augmented Generation)

RAG is a machine learning model architecture that combines the capabilities of retrieval systems with generative models. It is designed to enhance the model's ability to provide accurate and relevant responses by retrieving information from a database before generating an answer. In the video, RAG is contrasted with fine-tuning to highlight different approaches to improving model performance.

💡Workspace

In the context of the video, a workspace appears to be a virtual environment where users can interact with the AI model, upload documents, and conduct chats. The workspace is where the user's interactions and documents are stored and used as the basis for creating a fine-tuned model.

💡LLM (Large Language Model)

An LLM is a type of artificial intelligence model that is trained on vast amounts of text data and can generate human-like language. The video discusses the process of fine-tuning such models to specialize their knowledge and improve performance on specific tasks or datasets.

💡Quantization

Quantization in machine learning refers to the process of reducing the precision of the numbers used to represent a model, which can lead to smaller file sizes and faster inference times. The script mentions an '8-bit quantized version' of the fine-tuned model, indicating that the model has been optimized for deployment.

💡LM Studio

LM Studio is a platform mentioned in the video for running and managing large language models. The script describes how to load a custom fine-tuned model into LM Studio, showcasing the ease of use and the ability to utilize the model in a user-friendly interface.

💡Ollama

Ollama is a term used in the script to refer to a specific platform or tool for running large language models. The video demonstrates how to integrate a fine-tuned model into Ollama, emphasizing the flexibility and portability of the fine-tuned models across different platforms.

💡Custom model

A custom model in this context is a version of a machine learning model that has been tailored to a user's specific needs or preferences. The video script describes the process of creating and using a custom fine-tuned model, highlighting the personalization aspect of the no-code fine-tuning approach.

Highlights

Timothy Carat, founder of Implex Labs, introduces a no-code method for creating fine-tuned models.

The feature is initially available in the Dockerized version on GitHub, not in the desktop app version 1.5.1.

The fine-tuning process is innovative, allowing users to chat with any model and create a fine-tuned model from the conversation.

Fine-tuning does not run locally due to the expense of GPUs and the complexity of the process.

Timothy plans to release a video showing how to fine-tune locally on a consumer-grade GPU in the future.

The user interface for the Dockerized version of Anything LLM is the same as the desktop version.

Timothy demonstrates using GP4 or Omni from Open AI to create content and then fine-tune a model.

He shows how to combine documents, RAG chats, and web search results in a workspace for fine-tuning.

The goal is to send these chats to a cloud GPU to create a fine-tuned model of Llama 38b.

Fine-tuning versus RAG is explained as the difference between background knowledge and specific training.

The process of getting a fine-tune is simplified, focusing on the output rather than the technical details.

Privacy policies are mentioned, ensuring data is only used for training and deleted immediately after.

The fine-tuned model can be exported as a .ggf file for local use in various platforms like LLM Studio or Ollama.

Timothy demonstrates how to load a custom fine-tuned model into Ollama on a Windows machine.

The fine-tuned model provides accurate responses about Anything LLM, demonstrating the effectiveness of the process.

Loading a custom fine-tuned model into LLM Studio is shown, emphasizing the ease of use.

The fine-tuned model in LLM Studio also provides accurate responses, confirming its effectiveness.

Timothy concludes by emphasizing the no-code aspect and the potential for fine-tuning to enhance model responses.

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