Selected Dataset : OpenOrca

Among many datasets currently available for LLMs, I have selected the OpenOrca Dataset for LLM training and fine-tuning. This dataset presents a unique combination of scale, diversity, and depth, making it an exceptional resource for advancing the capabilities of large language models.

Dataset Summary

The OpenOrca dataset is a comprehensive collection of augmented data from the FLAN Collection, encompassing approximately 1 million GPT-4 completions and 3.2 million GPT-3.5 completions. It has been designed to align with the distributions described in the ORCA paper, facilitating a broad range of NLP tasks such as language modeling, text generation, and text augmentation. This dataset is an invaluable resource for training and evaluating Large Language Models (LLMs).

Dataset Availability

The OpenOrca dataset is available on both Kaggle and Hugging Face, allowing for versatile access and usage:

• Kaggle Dataset
• Hugging Face Dataset
• LLMDataHub on GitHub: LLMDataHub: Awesome Datasets for LLM Training.

Selection Criteria for LLM Training

Data Size

With its extensive compilation of more than 4 million entries, the OpenOrca dataset offers a vast pool of data. This volume is crucial for the effective training or fine-tuning of LLMs, providing a diverse range of linguistic patterns and contexts.

Relevance to Real-world Applications

This dataset supports a wide range of task categories, including:

• Conversational
• Text classification
• Token classification
• Table question answering
• Question answering
• Zero-shot classification
• Summarization
• Feature extraction
• Text generation
• Text2text generation

The dataset's orientation towards tasks such as language modeling and text generation makes it directly applicable to real-world scenarios. It facilitates the development of models capable of understanding and generating human-like text, thereby enhancing machine interaction in various domains.

Potential for Creative Exploration

• OpenOrca's structure, based on questions and responses generated by advanced models like GPT-3.5 and GPT-4, opens up possibilities for creative prompt engineering. This feature is key for developing models with nuanced comprehension and response generation capabilities, tailored to specific applications or exploratory research.

• OpenOrca's structure, rooted in the detailed reasoning capabilities highlighted by the Flan Collection: Designing Data and Methods for Effective Instruction Tuning, enables creative prompt engineering. This fosters the development of models with enhanced comprehension and generative abilities, tailored to diverse research and application needs.

• Furthermore, according to the information presented in "Open Source ORCA Dataset available - Explanation Tuning", the OpenOrca dataset is particularly valuable for Explanation Tuning. This approach is considered superior to Instruction Tuning as the use of system instructions in Explanation Tuning allows for a more detailed understanding of language model reasoning processes. This makes OpenOrca an essential resource for developers and researchers aiming to advance the capabilities of LLMs through a deeper comprehension of their internal reasoning.

Continuous Improvement

The dataset's ongoing expansion ensures it remains a cutting-edge resource for NLP research and development. Its evolving nature reflects the latest advancements in language model training, making it a strategic choice for projects aiming at innovation.

The OpenOrca dataset's considerable size, real-world relevance, and adaptability make it an excellent resource for the training and fine-tuning of Large Language Models. Its availability on both Kaggle and Hugging Face platforms enhances its accessibility for a wide range of NLP applications and research endeavors.

Selected Model: Mistral-7B-OpenOrca

Introduction

The Mistral-7B-OpenOrca model represents a significant achievement in the realm of Large Language Models (LLMs), having been fine-tuned on the OpenOrca dataset. This model is distinguished by its remarkable performance, surpassing all models below 30B parameters and achieving 98% of Llama2-70B-chat's performance.

Mistral 7B: Base Model Overview

• Parameters: 7.3 billion
• License: Apache 2.0
• Performance Highlights:
  • Surpasses Llama2-13B across all benchmark tasks.
  • Exhibits competitive performance against CodeLlama-7B in code-related tasks.
  • Maintains high proficiency in English language tasks.

Fine-tuning with OpenOrca

Fine-tuned on the OpenOrca dataset, Mistral-7B-OpenOrca leverages the rich data pool designed to mirror Microsoft Research's Orca Paper dataset. As of its release, it ranked #2 on the HuggingFace Leaderboard among models smaller than 30B.

How to Run Mistral-7B-OpenOrca

Utilize Clarifai’s Python SDK to interact with the Mistral-7B-OpenOrca model. Ensure your Personal Access Token (PAT) is set as an environment variable in your shell:

export CLARIFAI_PAT={your personal access token}

Then, run the model prediction using the following Python code:

from clarifai.client.model import Model

# Initialize and use the model
model_prediction = Model("https://clarifai.com/mistralai/completion/models/mistral-7B-OpenOrca").predict_by_bytes(b"Write a tweet on future of AI", "text")

Performance Evaluation

Mistral-7B-OpenOrca demonstrates outstanding performance across several benchmarks:

• HuggingFace Leaderboard: Achieves 105% of the base model's performance with an average score of 65.33.
• AGIEval: Indicates a performance of 129% of the base model's, with an average score of 0.397.
• BigBench-Hard: Shows strong capability with 119% of the base model's performance, scoring 0.416.
• GPT4ALL Leaderboard: Leads with an average score of 72.38, showcasing a slight edge over previous releases.
• MT-Bench: On par with Llama2-70b-chat, achieving an average score of 6.86.

Fine-Tuning Mistral-7B-OpenOrca

Fine-tuning the Mistral-7B-OpenOrca model allows you to tailor its capabilities for specific tasks or datasets. To get started with fine-tuning this model, consider exploring the following comprehensive tutorials which provide step-by-step instructions:

• Fine-tune a Mistral 7B Model with Direct Preference Optimization on Towards Data Science: This tutorial offers insights into fine-tuning Mistral 7B using Direct Preference Optimization, providing a detailed walkthrough on optimizing the model's performance for specific preferences.

• Mistral 7B Tutorial on DataCamp: This tutorial provides a beginner-friendly guide to fine-tuning Mistral 7B, covering the essentials of model adjustment to enhance its applicability to a wide range of NLP tasks.

• Fine-tuning Mistral-7b with DPO(kaggle)

These tutorials are excellent resources for anyone looking to harness the full potential of Mistral-7B through fine-tuning. Whether you're optimizing for specific types of text generation or aiming to improve task-specific performance, following these guides will provide a solid foundation for your efforts. For more detailed instructions and examples, please refer to these tutorials linked above

Conclusion

The Mistral-7B-OpenOrca model, through its strategic fine-tuning on the OpenOrca dataset, sets a new standard for LLM performance and application potential. Its impressive achievements across diverse benchmarks underscore its leadership in the evolving landscape of AI technology.

For more detailed information about the benchmarks and access to the model, please visit Clarifai , huggingface, MistralOrca 7B - The NEW Best 7B & 13B model?.