jsyeom/llama-2-13b-hf-smooth
The jsyeom/llama-2-13b-hf-smooth model is a 13 billion parameter Llama 2-based causal language model that has undergone SmoothQuant smoothing without any quantization. Developed by jsyeom, this model retains the full precision of the original Llama 2-13b-hf while applying a smoothing technique to its activations. This process is designed to prepare the model for potential future quantization, making it particularly suitable for research and development in efficient inference. It maintains a 4096 token context length, offering a smoothed foundation for various natural language processing tasks.
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jsyeom/llama-2-13b-hf-smooth: A Smoothed Llama 2-13B Model
This model, developed by jsyeom, is a 13 billion parameter variant of the meta-llama/Llama-2-13b-hf base model. Its primary distinguishing feature is the application of SmoothQuant smoothing to its internal activations. Crucially, no quantization has been applied; the model retains its full precision.
Key Characteristics
- Base Model:
meta-llama/Llama-2-13b-hf - Smoothing Technique: SmoothQuant smoothing applied to activations.
- Smoothing Alpha: Configured with an alpha value of
0.85, indicating the migration strength. - Act Scales Source: Utilizes
mit-han-lab/smoothquant-scalesfor activation scales. - Precision: Remains a full-precision model, as only smoothing, not quantization, has been performed.
Purpose and Potential Use Cases
This model is particularly relevant for researchers and developers exploring quantization-aware training or post-training quantization (PTQ). By providing a smoothed version of Llama 2-13B, it offers a pre-processed foundation that can potentially lead to better performance when subsequently quantized to lower bit-widths. It allows for experimentation with the benefits of SmoothQuant without the immediate performance impact of quantization, making it a valuable intermediate step in the optimization pipeline for efficient deployment of large language models.