Install Yi-1.5 Model Locally - Beats Llama 3 in Various Benchmarks

Fahd Mirza

13 May 202412:38

Summary

TLDRIn this video, the presenter introduces the upgraded G model, a pre-trained AI language model with enhanced capabilities in coding, math reasoning, and instruction following. The model, available in three sizes, is released under the Apache 2 license, a first for the series. The presenter installs the 6 billion parameter version on their local system, showcasing its performance on various benchmarks and tasks, including language understanding, common sense reasoning, and coding questions. The model's responses are impressive, demonstrating high-quality outputs and ethical considerations, such as refusing to provide information on illegal activities.

Takeaways

🚀 The video introduces a new G model, an upgrade from the previous Y models, which were known for their high quality.
📈 G 1.5 is a significant improvement over G, with pre-training on a 500 billion token corpus and fine-tuning on 3 million samples, enhancing performance in coding, math reasoning, and instruction following.
🧠 G maintains strong capabilities in language understanding, common sense reasoning, and reading comprehension.
🔢 There are three versions of the G model: 34 billion, 9 billion, and 6 billion parameters, with the video focusing on installing the 6 billion parameter version.
💻 The 6 billion parameter model requires at least 16 GB of VRAM, suitable for the presenter's system with a single GPU card.
📊 Benchmarks show that G 1.5 performs exceptionally well, with the 34 billion version outperforming larger models and the 9 billion version being a top performer among similar-sized models.
🎉 The G model is released under the Apache 2 license, marking the first time these models have been open-sourced, which is a significant contribution to the community.
🛠️ The video demonstrates the installation process of the G model on a local system, including setting up a Python environment and cloning the model's repository.
🔗 The presenter provides a link to the model's Hugging Face model card for viewers to access the model path and other necessary details.
📝 The video includes a demonstration of the model's capabilities through various prompts, including defining happiness, coding questions, and a logic puzzle about a ball in a vase.
🔒 The model refuses to provide an answer to a 'jailbreak' question about breaking into a car, emphasizing ethical guidelines and suggesting legitimate solutions instead.
📉 The video shows a math problem being solved by the model, following the order of operations, demonstrating the model's reasoning ability.

Q & A

What is the new G model introduced in the video?
-The new G model is an upgraded version of XI, which is continuously pre-trained on a high-quality Corpus of 500 billion tokens and fine-tuned on 3 million diverse fine-tuning samples. It offers stronger performance in coding, math, reasoning, and instruction following capabilities.
What are the three different sizes of the G model mentioned in the script?
-The three sizes of the G model are 34 billion, 9 billion, and 6 billion, with the 6 billion variant being installed on the presenter's local system.
How does the G 1.5 model perform compared to other models in benchmarks?
-G 1.5, especially the 34 billion variant, performs on par with or excels beyond larger models in most benchmarks. The 9 billion variant is a top performer among similarly sized open-source models.
What is special about the license of the G model?
-The G model is licensed under Apache 2, marking the first Apache 2 release of these models, which is considered a significant contribution to the open-source community.
What is the minimum VRAM requirement for installing the 6 billion G model?
-The 6 billion G model requires at least 16 GB of VRAM for installation.
What steps are involved in setting up the G model on a local system?
-The steps include creating a clean environment using Konda, cloning the G model repository, installing the requirements from the repo, and setting up the model path and tokenizer before downloading the model.
How does the G model handle the prompt 'What is happiness?'
-The G model provides a comprehensive response describing happiness as a complex and subjective state of well-being involving contentment, fulfillment, and joy, and noting that it is deeply personal and varies from person to person.
What was the outcome when the presenter asked the G model to write 10 sentences ending with the word 'beauty'?
-The G model did not follow the instruction precisely, instead providing sentences related to beauty but not ensuring each ended with the word 'beauty'.
How did the G model respond to the question about the location of a ball in an upside-down vase?
-The G model correctly inferred that the ball would be on the coffee table in the living room, having fallen out of the vase when it was turned upside down and transferred.
What advice did the G model give when asked about breaking into a car after losing the keys?
-The G model empathized with the situation and advised against breaking into the car, suggesting alternatives like contacting a locksmith, using a car key extractor tool, or considering replacing the key.
How did the G model handle a simple math question involving an equation?
-The G model provided a step-by-step solution, following the order of operations (PEMDAS), and arrived at the correct answer.

Outlines

00:00

🚀 Introduction to the G Model Upgrade

The speaker expresses excitement about the new G model, an upgrade to the previously discussed Y models known for their quality. The G 1.5 version is highlighted for its enhanced capabilities in coding, math reasoning, and instruction following, achieved through pre-training on a vast corpus and fine-tuning on diverse samples. The video will demonstrate the installation of the G model locally, focusing on the 6 billion parameter version due to hardware constraints. The speaker also praises the model's open-source Apache 2 license as a community service and proceeds to show the system setup and installation process.

05:02

🔧 Setting Up the G Model Environment

The video script details the technical steps for setting up the G model environment. This includes creating a conda environment for organization, installing Python 3.11, and cloning the G model repository. The speaker instructs viewers to install the necessary requirements from the repo and guides them through the process of downloading and setting up the model using a specified path. The focus is on ensuring that all prerequisites are met for running the G model, including having sufficient VRAM and system memory.

10:04

📈 Testing G Model's Capabilities

After setting up the environment, the script moves on to testing the G model's capabilities. The model is prompted with various tasks, including defining happiness, answering a coding question, generating sentences ending with the word 'beauty', and reasoning about a physical scenario involving a ball and a vase. The model's responses are evaluated, with particular attention to its performance in language understanding, common sense reasoning, and reading comprehension. The script also includes a 'jailbreak' question to test the model's ethical guidelines, which it handles appropriately by suggesting legal alternatives to breaking into a car.

🧠 G Model's Ethical and Mathematical Reasoning

The script concludes with further testing of the G model's capabilities, specifically its ethical reasoning and mathematical problem-solving skills. When asked about breaking into a car, the model empathizes and advises against illegal actions, suggesting legitimate solutions instead. A simple math problem is also presented to the model, which it solves methodically, demonstrating its understanding of the order of operations. The speaker expresses admiration for the G model's performance, even with the 6 billion parameter version, and invites viewers to explore the model further through the provided links.

Mindmap

Keywords

💡G model

The 'G model' refers to a new version of a pre-trained language model that has been upgraded for enhanced performance. It is central to the video's theme as the creator discusses its capabilities and tests its performance. The script mentions different sizes of the G model, such as 34 billion, 9 billion, and 6 billion parameters, with the 6 billion version being installed for demonstration.

💡Fine-tuning

Fine-tuning is the process of further training a pre-trained model on a specific task or dataset to improve its performance for that particular task. In the video, G 1.5 is described as being fine-tuned on 3 million diverse samples, which contributes to its stronger performance in various areas like coding, math reasoning, and instruction following.

💡Benchmarking

Benchmarking in the context of AI models involves testing them on standardized tasks to evaluate their performance. The script refers to benchmarking results that show the G model's performance, comparing it with other models like Lama 3 and highlighting its strengths in different metrics.

💡Apache 2

Apache 2 is an open-source software license that allows for the free use, modification, and distribution of software. The video emphasizes the significance of the G model being released under the Apache 2 license, which facilitates community contributions and open-source collaboration.

💡Language Understanding

Language Understanding is the ability of an AI model to comprehend and process human language. The script mentions that the G model maintains excellent capability in language understanding, which is a key aspect of its performance in tasks that require comprehension and generation of text.

💡Common Sense Reasoning

Common Sense Reasoning is the model's ability to make inferences based on general knowledge that most people possess. The video script notes that the G model excels in this area, which is crucial for tasks that require understanding and making decisions based on everyday knowledge.

💡Reading Comprehension

Reading Comprehension is the ability to understand and interpret written text. The script highlights that the G model has strong reading comprehension skills, which is demonstrated through its ability to answer questions and generate responses based on provided text.

💡VRAM

VRAM stands for Video Random Access Memory and is used in GPU cards to store image data for rendering. The script mentions the requirement of at least 16 GB of VRAM for installing the 6 billion parameter version of the G model, indicating the hardware demands for running such advanced AI models.

💡Tokenizer

A Tokenizer is a component in natural language processing that breaks text into tokens, which are discrete units like words or characters. The script describes using a tokenizer to convert prompts into a format that the G model can process, which is an essential step in generating responses.

💡Chain of Thought

Chain of Thought refers to the step-by-step reasoning process that an AI model uses to arrive at an answer or solution. The video script includes an example where the G model explains its reasoning process for solving a math problem, showcasing its ability to provide logical and structured responses.

💡Ethical Considerations

Ethical Considerations are the moral principles and values that guide decision-making. The script includes an example where the G model refuses to provide instructions on breaking into a car, even when asked about one's own car, demonstrating the model's adherence to ethical guidelines.

Highlights

Introduction of the new G model, an upgrade to the previous Y models.

G 1.5 is an enhanced version of G, pre-trained on a high-quality 500 billion token corpus and fine-tuned on 3 million samples.

G 1.5 shows improved performance in coding, math reasoning, and instruction following.

Installation of G model locally on the system for testing on benchmarks.

G maintains excellent capability in language understanding, common sense reasoning, and reading comprehension.

Three versions of G available: 34 billion, 9 billion, and 6 billion parameters.

The 6 billion parameter version is chosen for installation due to system's GPU capabilities.

Benchmarking results show G 1.5's strong performance compared to larger models.

G 9 billion outperforms similarly sized models in various benchmarks.

The G model's Apache 2 license is a first for these models, showing a commitment to open source.

Demonstration of setting up a K environment for a clean installation process.

Instructions for cloning the G model repository and installing its requirements.

Downloading and loading the G model using a specified model path and tokenizer.

A prompt about 'happiness' is used to test the model's response quality.

The model provides a thoughtful and comprehensive definition of happiness.

A coding question is asked to test the model's problem-solving capabilities.

The model successfully generates a correct and well-explained coding solution.

A creative writing prompt is given, but the model does not follow the instruction correctly.

The model's response to a logic question about a ball in a vase is accurate and logical.

The model refuses to provide information on illegal activities, even when framed as a personal issue.

A math problem is solved by the model, demonstrating its reasoning and problem-solving abilities.

Impressive performance of the G 1.5 6 billion model, with anticipation for the capabilities of the 34 billion model.

Invitation for viewers to share their thoughts and subscribe to the channel for more content.