Mel-Iza0/Llama2-7B_ZeroShot-20K_classe_bias_port
Mel-Iza0/Llama2-7B_ZeroShot-20K_classe_bias_port is a 7 billion parameter Llama 2 model developed by Mel-Iza0. This model was trained using bitsandbytes 4-bit quantization with nf4 quantization type and bfloat16 compute dtype. Its primary focus is on zero-shot classification tasks, specifically addressing class bias, as indicated by its training methodology.
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Model Overview
Mel-Iza0/Llama2-7B_ZeroShot-20K_classe_bias_port is a 7 billion parameter Llama 2 model. It was developed by Mel-Iza0 with a specific focus on zero-shot classification, particularly in the context of class bias.
Training Details
The model underwent training utilizing bitsandbytes 4-bit quantization. Key configurations for this process included:
- Quantization Type:
nf4 - Double Quantization: Enabled (
bnb_4bit_use_double_quant: True) - Compute Data Type:
bfloat16(bnb_4bit_compute_dtype: bfloat16)
This training approach suggests an optimization for efficient deployment while maintaining performance for its intended zero-shot classification tasks.
Key Characteristics
- Architecture: Llama 2 (7B parameters)
- Training Method: Quantized using
bitsandbytes(4-bit, nf4, bfloat16) - Focus: Zero-shot classification with an emphasis on class bias mitigation or analysis.
Potential Use Cases
This model is likely suitable for applications requiring:
- Zero-shot text classification: Categorizing text without explicit prior examples for each category.
- Bias analysis: Investigating and potentially mitigating class-related biases in classification outcomes.
- Resource-efficient deployment: The 4-bit quantization makes it more amenable to environments with limited computational resources.