Mel-Iza0/Llama2-7B_ZeroShot-20K_classe_other_port
Mel-Iza0/Llama2-7B_ZeroShot-20K_classe_other_port is a Llama 2-based language model developed by Mel-Iza0. This model was trained using 4-bit quantization with the nf4 quantization type and double quantization enabled, leveraging bfloat16 for computation. It is specifically fine-tuned for zero-shot classification tasks on a 20K dataset, focusing on the 'other' class. Its primary application is efficient classification in resource-constrained environments due to its optimized training configuration.
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Model Overview
Mel-Iza0/Llama2-7B_ZeroShot-20K_classe_other_port is a Llama 2-based model developed by Mel-Iza0, specifically fine-tuned for zero-shot classification. The training process utilized advanced quantization techniques to optimize for efficiency.
Key Training Details
- Quantization: The model was trained with
bitsandbytes4-bit quantization (load_in_4bit: True). - Quantization Type: It employs
nf4(NormalFloat4) quantization, known for its efficiency in deep learning. - Double Quantization:
bnb_4bit_use_double_quantwas enabled, further reducing memory footprint during training. - Compute Data Type: Training computations were performed using
bfloat16(bnb_4bit_compute_dtype: bfloat16). - Framework: PEFT (Parameter-Efficient Fine-Tuning) version 0.4.0 was used, indicating an efficient fine-tuning approach.
Intended Use
This model is designed for zero-shot classification tasks, particularly for identifying the 'other' class within a 20,000-sample dataset. Its optimized training with 4-bit quantization makes it suitable for applications where computational resources or memory are a concern, offering efficient inference for classification tasks.