INTELLECT-1-Instruct-FP8-dynamic
Model Optimizations
This model was obtained by quantizing the weights and activations of INTELLECT-1-Instruct to FP8 data type. This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%.
Only the weights and activations of the linear operators within transformers blocks are quantized. Weights are quantized using a symmetric per-channel scheme, whereas quantizations are quantized using a symmetric per-token scheme. LLM Compressor is used for quantization.
Model Details
Model Description
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Training Details
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Environmental Impact
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