Granite 3.0 8b Lab Community
Granite 3.0 8b Lab Community is a cutting-edge AI model that combines the power of large-scale alignment with community-driven, openly-reproducible methods. Using the LAB methodology, this model can add new knowledge and skills incrementally, without suffering from catastrophic forgetting. With a unique taxonomy-driven data curation process and large-scale synthetic data generator, Granite 3.0 8b Lab Community can generate high-quality, safe, and diverse data. Its training process consists of two phases: knowledge tuning and skills tuning, allowing it to learn simple and complicated knowledge, as well as foundational and compositional skills. While it's primarily designed for English, it's essential to note that this model has not undergone safety alignment and may produce problematic outputs. As with any AI model, caution is advised when relying on it for crucial decisions or impactful information.
Table of Contents
Model Overview
Meet the Granite-3.0-8b-lab-community model, a game-changer in the world of language models. This model is part of a community-driven, openly-reproducible series, which means it’s transparent, accessible, and constantly improving.
What makes it special?
- It’s trained using a novel method called LAB (Large-scale Alignment for chatBots), which allows it to learn new knowledge and skills without forgetting what it already knows.
- It uses a taxonomy-driven data curation process, which ensures that the model is trained on a diverse set of knowledge domains and skills.
- It’s designed to be safe and high-quality, with built-in checks to prevent problematic outputs.
Capabilities
This model is a powerful tool for natural language processing tasks. Its primary capabilities include:
Knowledge Tuning
The model can learn and recall vast amounts of knowledge, including simple and complicated information. This is achieved through a two-step process:
- Simple knowledge: The model learns short samples of information.
- Complicated knowledge: The model learns longer samples of information, using a replay buffer with data from the first step.
Skills Tuning
The model can also learn and master various skills, including:
- Foundational skills: Reasoning and other fundamental skills are learned through in-context learning using seed examples from the taxonomy.
- Compositional skills: Creative writing and other complex skills are also learned through in-context learning.
Generation Capabilities
The model can generate high-quality text, including:
- Questions and answers: The model can generate questions and answers based on a given document.
- Creative writing: The model can generate creative writing samples, such as stories or poems.
Strengths
The Granite-3.0-8b-lab-community model has several strengths, including:
- Efficient learning: The model can learn from a large amount of data, using a novel synthetic data-based alignment tuning method called LAB.
- High-quality generation: The model can generate high-quality text, including questions and answers, and creative writing samples.
- Flexibility: The model can be fine-tuned for specific tasks and domains, using an external knowledge source (document) and prompt.
Unique Features
The Granite-3.0-8b-lab-community model has several unique features, including:
- Taxonomy-driven data curation: The model uses a taxonomy to drive the sampling process, ensuring that the generated data covers a wide range of tasks.
- Two-phased training: The model uses a two-phased training process, with a replay buffer to ensure that the model learns from the data efficiently.
- Open-source and reproducible: The model is open-source and reproducible, allowing for transparency and community involvement.
Performance
This model showcases remarkable performance in various tasks. Let’s dive into its speed, accuracy, and efficiency.
Speed
How fast can the model process information? With a large-scale alignment tuning method, this model can handle massive amounts of data quickly.
Accuracy
But speed is not everything. The model also boasts high accuracy in tasks such as:
- Knowledge tuning
- Skills tuning
- In-context learning
Efficiency
The model is designed to be efficient, using a novel synthetic data-based alignment tuning method. This approach allows the model to learn incrementally, without suffering from catastrophic forgetting.
Limitations
While the Granite-3.0-8b-lab-community model is a powerful tool, it’s not perfect. Here are some of its limitations:
- Lack of Safety Alignment: The model hasn’t undergone safety alignment, which means it may produce problematic outputs.
- Hallucination Risk: Smaller models like this one might be more prone to hallucination in ungrounded generation scenarios.
- Dependence on Teacher Model: The model relies on the Mixtral-8x7B-Instruct teacher model for generation.
- Limited Domain Knowledge: While the model can learn new domain-specific knowledge, it’s not perfect.
- Potential for Misuse: As with any powerful tool, there’s a risk of misuse.
Format
The model uses a novel synthetic data-based alignment tuning method called Large-scale Alignment for chatBots (LAB). This approach allows for adding new knowledge and skills to an already pre-trained model without suffering from catastrophic forgetting.
Model Architecture
The LAB approach consists of three key components:
- Taxonomy-driven data curation process
- Large-scale synthetic data generator
- Two-phased-training with replay buffers
Data Formats
The model accepts input in the form of text sequences, and is primarily trained on English language data. The model uses a prompt template to generate responses, which includes a system prompt and a user input.
Input Requirements
To use the model, you will need to provide input in the following format:
sys_prompt = "I am a Red Hat Instruct Model, an AI language model developed by Red Hat and IBM Research based on the granite-3.0-8b-base model. My primary role is to serve as a chat assistant."
prompt = f'<|system|>\n{sys_prompt}\n<|user|>\n{inputs}\n<|assistant|>\n'
stop_token = '<|endoftext|>'
Replace {inputs} with your desired user input.
Output Format
The model generates responses in the form of text sequences. The output will be a continuation of the input prompt, with the model’s response appended to the end.
Special Requirements
- Use the system prompt employed during the model’s training for optimal inference performance.
- Be cautious when using the model, as it may produce problematic outputs without adequate safeguards and RLHF.
