Abinesh/Llama-2_Vicuna_LoRA-13b
Abinesh/Llama-2_Vicuna_LoRA-13b is a 13 billion parameter language model based on the Llama-2 architecture, fine-tuned using the Vicuna dataset and LoRA. This model leverages 4-bit quantization with bfloat16 compute dtype for efficient deployment. It is designed for general-purpose conversational AI tasks, offering a balance of performance and resource efficiency.
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Model Overview
Abinesh/Llama-2_Vicuna_LoRA-13b is a 13 billion parameter language model built upon the robust Llama-2 architecture. It has been fine-tuned using the Vicuna dataset, a collection of user-shared conversations, which enhances its conversational capabilities. The model employs Low-Rank Adaptation (LoRA) for efficient fine-tuning, making it adaptable for various downstream tasks.
Technical Specifications
This model was trained with specific quantization configurations to optimize its size and inference speed:
- Quantization Method:
bitsandbytes4-bit quantization (nf4type). - Double Quantization: Enabled for further memory efficiency.
- Compute Data Type:
bfloat16for numerical stability and performance.
These configurations allow the model to run effectively with reduced memory footprint while maintaining a good level of performance, making it suitable for environments with limited computational resources.
Training Frameworks
The fine-tuning process utilized:
- PEFT: Version
0.4.0.dev0for parameter-efficient fine-tuning.
Use Cases
This model is well-suited for:
- Conversational AI: Engaging in dialogue, answering questions, and generating human-like text.
- Resource-constrained deployments: Its 4-bit quantization makes it a viable option for applications where memory and computational power are limited.
- Further fine-tuning: Can serve as a strong base model for domain-specific adaptations due to its LoRA fine-tuning.