labradorite-13b
Maintainer: ibm
73
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The labradorite-13b is a large language model developed by IBM Research using a novel synthetic data-based alignment tuning method called Large-scale Alignment for chatBots (LAB). The model is a derivative of the LLaMA-2-13b model, which was further trained using the LAB methodology with the Mixtral-8x7B-Instruct model as the teacher.
The key aspects of the LAB approach are a taxonomy-driven data curation process, a large-scale synthetic data generator, and a two-phased training with replay buffers. This allows the model to incrementally learn new knowledge and skills without suffering from catastrophic forgetting. Unlike previous approaches that uniformly draw seed examples from the entire pool, LAB uses the taxonomy to drive the sampling process, which helps the teacher model better exploit the task distributions defined by the local examples.
The labradorite-13b model outperforms other instruction-tuned models like Orca-2, WizardLM-13B-V1.2, and Mistral-7B-Instruct-v0.1 on several benchmark tasks, including MMLU, ARC-C, HellaSwag, Winogrande, and GSM8K.
Model inputs and outputs
Inputs
- Text inputs, which can be prompts, instructions, or conversations
Outputs
- Generated text, which can be responses, answers, or continuations of the input
Capabilities
The labradorite-13b model has shown strong performance on a variety of language understanding and generation tasks, particularly those involving instruction following, reasoning, and open-ended conversation. It has been trained to be helpful, harmless, and honest, making it suitable for use cases such as virtual assistants, chatbots, and content generation.
What can I use it for?
The labradorite-13b model can be used for a wide range of applications that require natural language processing and generation, such as:
- Conversational AI: Building chatbots and virtual assistants that can engage in open-ended conversations, answer questions, and follow instructions.
- Content Generation: Generating articles, stories, poems, and other forms of creative writing.
- Task Completion: Helping users complete various tasks by understanding instructions and providing relevant information or step-by-step guidance.
- Knowledge Retrieval: Answering questions and providing information on a wide range of topics by leveraging the model's broad knowledge base.
Things to try
One interesting aspect of the labradorite-13b model is its ability to learn new knowledge and skills incrementally through the LAB approach, without suffering from catastrophic forgetting. This suggests that the model could be fine-tuned or adapted for specialized domains or use cases, allowing developers to expand its capabilities over time.
Additionally, the model's strong performance on tasks like HellaSwag and Winogrande indicates that it possesses robust reasoning and language understanding capabilities, which could be leveraged for applications that require more advanced natural language processing.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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