Mel-Iza0/Llama2-7B_ZeroShot-20K_classe_nenhuma_port
Mel-Iza0/Llama2-7B_ZeroShot-20K_classe_nenhuma_port is a 7 billion parameter Llama 2 model, fine-tuned with a ZeroShot-20K dataset. This model leverages 4-bit quantization using bitsandbytes for efficient deployment and inference. Its primary differentiation lies in its specific fine-tuning approach, making it suitable for tasks requiring zero-shot capabilities within its trained domain. The model is optimized for performance with bfloat16 compute dtype and double quantization.
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Model Overview
Mel-Iza0/Llama2-7B_ZeroShot-20K_classe_nenhuma_port is a 7 billion parameter language model based on the Llama 2 architecture. It has been fine-tuned using a ZeroShot-20K dataset, indicating a focus on tasks that require generalization without explicit in-context examples. The model is configured for efficient deployment and inference through 4-bit quantization.
Key Technical Details
- Base Model: Llama 2 (7B parameters)
- Quantization: Utilizes
bitsandbytesfor 4-bit quantization (bnb_4bit_quant_type: nf4,bnb_4bit_use_double_quant: True). - Compute Dtype:
bfloat16for computation during quantization. - Context Length: Supports a context length of 4096 tokens.
- Framework: Developed with PEFT version 0.4.0.
Potential Use Cases
This model is particularly suited for applications where:
- Resource Efficiency is Critical: The 4-bit quantization allows for reduced memory footprint and faster inference compared to full-precision models.
- Zero-Shot Generalization is Required: Its fine-tuning on a ZeroShot-20K dataset suggests an ability to perform tasks without extensive task-specific examples.
- Llama 2 Ecosystem Integration: Benefits from the broad compatibility and community support of the Llama 2 family.