Markr-AI/pub-llama-13B-v3
Markr-AI/pub-llama-13B-v3 is a 13 billion parameter auto-regressive language model developed by Kyujin Han as part of the LLM research consortium between Media Group Saramgwasup and Markr. Based on the LLaMA2 transformer architecture, this model is specifically trained on the pub_COT-2000 dataset, indicating an optimization for tasks involving Chain-of-Thought reasoning. It processes text input to generate text output, making it suitable for applications requiring structured reasoning or complex problem-solving.
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Markr-AI/pub-llama-13B-v3 Overview
Markr-AI/pub-llama-13B-v3 is a 13 billion parameter auto-regressive language model developed by Kyujin Han, originating from an LLM research consortium involving Media Group Saramgwasup and Markr. It is built upon the robust LLaMA2 transformer architecture, designed to process text inputs and generate text outputs.
Key Characteristics
- Architecture: Utilizes the LLaMA2 transformer architecture, providing a strong foundation for language understanding and generation.
- Parameter Count: Features 13 billion parameters, balancing performance with computational requirements.
- Training Data: Specifically trained on the HumanF-MarkrAI/pub_COT-2000 dataset, suggesting a focus on Chain-of-Thought (CoT) reasoning capabilities.
- Input/Output: Handles text-only input and produces text-only output.
- License: Distributed under the
cc-by-nc-salicense.
Potential Use Cases
This model is particularly well-suited for applications that benefit from structured reasoning, such as:
- Complex question answering.
- Problem-solving requiring intermediate steps.
- Tasks where explicit reasoning paths are beneficial.