sminchoi/Llama-2-7b-hf__sql-create-context-llama2-78k__231017
sminchoi/Llama-2-7b-hf__sql-create-context-llama2-78k__231017 is a 7 billion parameter Llama-2-based language model developed by sminchoi. This model was trained using 4-bit quantization with the nf4 type and float16 compute dtype. Its specific fine-tuning objective and primary use case are not detailed in the provided information, but it leverages a 4096 token context length.
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Model Overview
This model, sminchoi/Llama-2-7b-hf__sql-create-context-llama2-78k__231017, is a 7 billion parameter language model built upon the Llama-2 architecture. While specific details regarding its fine-tuning dataset or primary objective are not provided in the available documentation, the model was trained with a focus on efficient quantization techniques.
Training Details
The training process for this Llama-2-7b-hf variant utilized bitsandbytes 4-bit quantization. Key configurations include:
- Quantization Type:
nf4 - Compute Dtype:
float16 - Double Quantization: Not used (
bnb_4bit_use_double_quant: False) - PEFT Version:
0.4.0
These settings indicate an optimization for reduced memory footprint during training and potentially inference, making it suitable for environments with limited computational resources. The model maintains a context length of 4096 tokens.
Potential Use Cases
Given the Llama-2 base and the quantization strategy, this model is likely intended for applications where a balance between performance and resource efficiency is crucial. Without explicit fine-tuning objectives, its general-purpose language understanding and generation capabilities, inherited from Llama-2, would be its primary strength, with the quantization making it more accessible for deployment.