shuvam4849/vidhyaarthi-model
The shuvam4849/vidhyaarthi-model is an 8 billion parameter instruction-tuned causal language model, finetuned from unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit. Developed by shuvam4849, this model was trained using Unsloth and Huggingface's TRL library, enabling 2x faster training. With a context length of 8192 tokens, it is designed for general language understanding and generation tasks.
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Model Overview
The shuvam4849/vidhyaarthi-model is an 8 billion parameter instruction-tuned language model, developed by shuvam4849. It is finetuned from the unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit base model, leveraging the Llama 3.1 architecture. This model was trained with a focus on efficiency, utilizing Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process.
Key Characteristics
- Base Model: Finetuned from unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit.
- Parameter Count: 8 billion parameters.
- Context Length: Supports an 8192-token context window.
- Training Efficiency: Benefits from Unsloth's optimizations for faster training.
Potential Use Cases
This model is suitable for a variety of general-purpose natural language processing tasks, including:
- Instruction following and response generation.
- Text summarization and completion.
- Chatbot applications requiring conversational AI.
- Educational tools and content generation, given its name "vidhyaarthi" (student).