All You Need To Know About Running LLMs Locally
TLDRThe video discusses the practicality of running AI language models (LMs) locally, contrasting the subscription-based AI services with the potential of free, locally-run alternatives. It introduces various user interfaces like UABA, Silly Tarvin, LM Studio, and Axel AO, each with its unique features and use cases. The video also covers the importance of choosing the right model based on its parameters and the ability to run on a GPU. It explains different model formats like ggf, awq, safe tensors, EXL 2, and their impact on model performance and memory usage. The concept of context length in AI models is discussed, emphasizing its role in providing necessary information for the model to process prompts accurately. The video also touches on CPU offloading as a method to run large models on systems with limited VRAM. It highlights the use of fine-tuning for specific tasks and the importance of quality training data. Finally, it mentions the NVIDIA RTX 480 super giveaway for attendees of a virtual GTC session, encouraging viewers to participate.
Takeaways
- π The 2024 job market has more hiring opportunities despite the subscription-based AI services becoming more prevalent.
- π€ Subscription to AI services like 'green Jor' is a monthly expense some find unnecessary when alternatives to run AI models locally exist.
- π Choosing the right user interface is crucial for different levels of expertise, with options like uaba, silly Tarvin, LM Studio, and Axel AO available.
- π οΈ uaba is a versatile text generation web UI that supports various modes and is compatible with multiple operating systems and hardware.
- π LM Studio offers native functions like Hugging Face model browser, making it easier to find and use AI models as an API for other applications.
- π― Axel AO is the command-line interface of choice for fine-tuning AI models, providing robust support for this specific task.
- π Hugging Face provides a vast array of free and open-source models, with model names indicating their size and capabilities.
- π§ Model formats like safe tensors, ggf, EXL 2, and awq are designed to optimize model size and performance for different use cases and hardware.
- π§ Understanding context length is essential for AI models as it affects the amount of information the model can use to process prompts effectively.
- π‘ CPU offloading allows running large models on systems with limited VRAM by offloading parts of the model to the CPU and system RAM.
- π Fine-tuning AI models can be done efficiently with Kora, which focuses on training a fraction of the model's parameters, saving time and resources.
Q & A
What was the initial expectation for the job market in 2024?
-The initial expectation for the job market in 2024 was that it was going to be very challenging.
Why might someone consider running AI models locally instead of using a subscription service?
-Running AI models locally can be more cost-effective and provides greater control over the usage time and the specific models used, without the constraints of subscription services.
What are the three modes offered by the uaba text generation web UI?
-The three modes offered by the uaba text generation web UI are default (basic input/output), chat (dialogue format), and notebook (text completion).
What is the main focus of the Silly Tarvin interface?
-Silly Tarvin focuses on the front-end experience of using AI chat bots, offering features like chatting, role-playing, and visual novel-like presentation.
How does LM Studio differ from other interfaces mentioned?
-LM Studio is an interface with native functions like the Hugging Face model browser for easier model discovery and quality of life features that inform users about model compatibility and usability.
What is the advantage of using Axel AO for fine-tuning AI models?
-Axel AO offers the best support for fine-tuning AI models, as it allows users to fine-tune without training the entire model, which is more efficient and less resource-intensive.
What does 'Cuda out of memory' error indicate?
-A 'Cuda out of memory' error indicates that the GPU memory is insufficient to run the model, which can happen when the model's parameter count is too large for the available GPU memory.
How can CPU offloading help in running large models with limited GPU memory?
-CPU offloading allows models to be offloaded onto the CPU and system RAM, enabling the execution of models that would otherwise be too large for the available GPU memory, albeit with a trade-off in speed.
What is the significance of context length in AI models?
-Context length is crucial as it refers to the amount of information, including instructions, input prompts, and conversation history, that a model can use to process a prompt. A longer context length allows the AI to utilize more information for tasks like summarizing papers or tracking previous conversations.
What are some hardware acceleration frameworks that can improve the speed of running AI models?
-Some hardware acceleration frameworks include VM Inference Engine, which is great for handling parallel requests and can increase model speed significantly, and Nvidia's TensorRT, which can improve inference speed for certain models.
How does fine-tuning a model differ from training a model from scratch?
-Fine-tuning involves adjusting a pre-trained model to better suit a specific task, using only a fraction of the model's parameters. This is more efficient and less costly than training a model from scratch, which requires training all parameters.
What is the importance of data formatting when fine-tuning a model?
-Data formatting is crucial as it must follow the original format of the dataset used to train the model. This ensures that the fine-tuning process produces a model that can effectively handle the intended tasks and data structures.
Outlines
π€ AI Services and Local Deployment Overview
The first paragraph discusses the unexpected job market conditions in 2024 and the rise of AI services, particularly subscription-based ones. It introduces the concept of a 'green Jor' bot for coding and email writing, and questions the value of such services when free alternatives exist. The importance of choosing the right user interface for AI models is emphasized, with options like text generation web UI (uaba), Silly Tarvin for a better front-end experience, LM Studio for straightforward execution, and Axel AO for fine-tuning. The paragraph also covers various model formats and optimization methods, including safe tensors, ggf, awq, EXL 2, and gbq, and their impact on model performance and memory usage.
π§ Context Length and Model Optimization Techniques
The second paragraph delves into the significance of context length for AI models, explaining how it affects the model's ability to process prompts and maintain conversation history. It discusses the technical aspects of context length in terms of tokens and VRAM usage, and how certain models can optimize this with techniques like gqa. The paragraph also explores CPU offloading as a method to run large models on systems with limited VRAM. It mentions various hardware acceleration frameworks and tools, including VM inference engine, Nvidia's tensor rtlm, and the chat with RTX app, which offers local document scanning and video content analysis. The importance of fine-tuning AI models with tools like Kora is highlighted, along with the necessity of quality training data and adherence to the original dataset format.
π NVIDIA RTX 480 Super Giveaway and Community Acknowledgment
The third paragraph announces a giveaway for an NVIDIA RTX 480 Super, with the requirement to attend a virtual GTC session and provide proof of attendance. It provides instructions on how to participate, including taking a selfie or showing a unique gesture during the session. The paragraph also acknowledges various individuals who have supported the speaker through Patreon or YouTube and encourages following on Twitter for future updates.
Mindmap
Keywords
LLMs
AI Services
Locally
UI
Hugging Face
Model Parameters
Quantization
Context Length
CPU Offloading
Fine-Tuning
Gradio
Hardware Acceleration
Highlights
The job market in 2024 is experiencing an increase in hiring opportunities despite previous concerns.
AI Services are becoming more prevalent, offering services like coding assistance and email writing for a monthly fee.
Running AI chatbots and language models (LMs) locally can be a cost-effective alternative to subscription-based AI services.
UABA is a popular user interface for text generation, offering modes like default, chat, and notebook.
Silly Tarvin focuses on the front-end experience for AI chatbots, providing a visually appealing interface.
LM Studio offers native functions like the Hugging Face model browser for easier model discovery.
Axel AO is a command-line interface that provides the best support for fine-tuning AI models.
Hugging Face allows users to browse and download free and open-source models.
Models have different parameter counts, which can indicate their suitability for running on a GPU.
Model formats like GGF, AWQ, and GPTQ allow for reduced memory usage, making it possible to run larger models.
Context length is crucial for AI models to understand and process prompts effectively.
CPU offloading allows models to run on systems with limited VRAM by utilizing system RAM.
Hardware acceleration frameworks like VM Inference Engine can significantly increase model speed.
NVIDIA's TensorRT can enhance inference speed on RTX GPUs.
Chat with RTX is a local UI that can connect a model to local documents for privacy and convenience.
Fine-tuning AI models with tools like Kora can be more efficient than training from scratch.
The quality of fine-tuning depends on the organization of the training data and the fit of the chosen model.
Different fine-tuning techniques serve various purposes, from generating morally aligned responses to preferred human-like answers.
Running local LMs can save money and offer performance without relying on subscription services.
NVIDIA is giving away an RTX 480 Super to one lucky participant who attends a virtual GTC session.