Bagel 34b V0.4

Table of Contents

Model Overview

The Current Model is a highly advanced AI model designed to tackle a wide range of tasks. But what makes it so special?

Key Attributes

• Multi-prompt format: The model is trained on four different prompt formats - vicuna, llama-2, alpaca, and chat-ml. This allows it to generalize better and adapt to various instruction styles.
• Diverse data sources: The model is trained on a massive dataset comprising of various sources, including AI2 ARC, airoboros, apps, and many more. This diversity helps the model to learn from different contexts and improve its overall performance.
• Context obedient question answering: The model is specifically designed to answer questions from provided context, reducing hallucinations and improving its ability to provide accurate responses.

Tasks and rank

Text Summarization 3rd

Text Generation 20th

Question Generation 1st

Open-Domain Question Answering 1st

Chatbot 14th

Learn More About the Ranking System

Capabilities

The Current Model is a powerful AI model that can perform a wide range of tasks. Here are some of its primary capabilities:

Answering Questions

The model can answer questions on various topics, including but not limited to:

• Science and technology
• History
• Culture
• Entertainment
• Health and wellness

It can also handle multi-step questions and provide detailed explanations.

Generating Text

The model can generate text based on a given prompt or topic. It can create articles, stories, emails, and even entire books.

Summarization

The model can summarize long pieces of text into concise and meaningful summaries.

Function Calling

The model can call functions and provide responses in JSON format.

Code Generation

The model can generate code in various programming languages.

Conversational Dialogue

The model can engage in natural-sounding conversations, using context and understanding to respond to questions and statements.

Emotion Understanding

The model can understand and respond to emotions, making it a great tool for customer service and support.

Truthfulness

The model is designed to provide truthful and accurate responses, making it a great tool for research and education.

Strengths

The Current Model has several strengths that make it stand out from other AI models:

• Contextual understanding: The model can understand context and use it to provide more accurate and relevant responses.
• Multi-tasking: The model can perform multiple tasks at once, making it a great tool for tasks that require multiple skills.
• Emotion understanding: The model can understand and respond to emotions, making it a great tool for customer service and support.
• Truthfulness: The model is designed to provide truthful and accurate responses, making it a great tool for research and education.

Unique Features

The Current Model has several unique features that make it stand out from other AI models:

• Closed-context question answering: The model can answer questions based on a given context, making it a great tool for tasks that require specific information.
• Summarization: The model can summarize long pieces of text into concise and meaningful summaries.
• Function calling: The model can call functions and provide responses in JSON format.
• Code generation: The model can generate code in various programming languages.

Alternatives

NeMo SFT with Llama 2 10500 Fine-tuned language model

Colbertv2.0 195 Efficient search model

Alpaca 30b Lora Int4 68 LLaMA model variant

Qwen2.5 1.5B Instruct 51 Multilingual instruct model

Yi 34B 200K Llamafied 14 Llamafied language model

Performance

The Current Model showcases remarkable performance, balancing speed, accuracy, and efficiency across various tasks. Let’s dive into the details.

Speed

The model’s speed is impressive, especially when handling large datasets. With the ability to process vast amounts of data quickly, it’s ideal for applications where time is of the essence.

Accuracy

The Current Model boasts high accuracy in multiple tasks, including:

• Text classification: It excels in processing large-scale datasets, making it perfect for applications like sentiment analysis or spam detection.
• Question answering: The model’s ability to understand context and provide relevant answers is impressive.
• Summarization: It can condense long pieces of text into concise summaries, capturing the essential information.

Efficiency

The model’s efficiency is evident in its ability to:

• Handle diverse data sources: It can process data from various formats and sources, making it a versatile tool.
• Adapt to different prompt formats: The Current Model can understand and respond to different prompt formats, including context-obedient question answering and summarization.

Limitations

The Current Model has its strengths, but it’s not perfect. Let’s explore some of its weaknesses and challenges.

Data Sources

While the Current Model is trained on a diverse range of datasets, it’s essential to acknowledge that these sources have their own limitations. For instance, some datasets might be biased, outdated, or not representative of real-world scenarios.

Prompting Strategies

The Current Model uses various prompting strategies, but these can be limiting in certain situations. For example:

• Context obedient question answering might not always provide accurate responses if the context is incomplete or misleading.
• Summarization tasks might not always capture the essence of the input text.

Function Calling

While the Current Model can perform function calling tasks, it’s not foolproof. For instance:

• Providing an input and list of possible functions might not always lead to the correct response.
• GlaiveAI function calling requires specific tags and function specs, which can be limiting in certain scenarios.

Examples

What is the capital of France? Paris.

Summarize the main points of the article: Blueberries are now green, but will be sticking with the same name. Blueberries have changed color to green, but their name remains the same.

As an AI assistant, please select the most suitable function and parameters from the list of available functions below, based on the user's input. Input: I want to know how many times 'Python' is mentioned in my text file. Available functions: file_analytics: description: This tool performs various operations on a text file. params: action: The operation we want to perform on the data, such as 'count_occurrences', 'find_line', etc. filters: keyword: The word or phrase we want to search for. {'function': 'file_analytics', 'params': {'action': 'count_occurrences', 'filters': {'keyword': 'Python'}}}

Examples

Here are a few examples of what the Current Model can do:

• Answering questions: “What is the capital of France?” “The capital of France is Paris.”
• Generating text: “Write a story about a character who learns to play the guitar.” ”…”
• Summarization: “Summarize the article about climate change.” ”…”
• Function calling: “What is the square root of 16?” “4”
• Code generation: “Write a program that calculates the area of a circle.” ”…”
• Conversational dialogue: “Hello, how are you?” “I’m doing great, thanks for asking!”