Update README.md

README.md

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 # Phi-3-mini-128k-instruct-FP8
 
 ## Model Overview
-* <h3 style="display: inline;">Model Architecture:</h3> Based on and identical to the Phi-3-mini-128k-instruct-FP8 architecture
+* <h3 style="display: inline;">Model Architecture:</h3> Based on and identical to the Phi-3-mini-128k-instruct architecture
 * <h3 style="display: inline;">Model Optimizations:</h3> Weights and activations quantized to FP8
 * <h3 style="display: inline;">Release Date:</h3> June 29, 2024
 * <h3 style="display: inline;">Model Developers:</h3> Neural Magic
 
-Phi-3-mini-128k-instruct-FP8 quantized to FP8 weights and activations using per-tensor quantization through the [AutoFP8 repository](https://github.com/neuralmagic/AutoFP8), ready for inference with vLLM >= 0.5.0. 
+Phi-3-mini-128k-instruct quantized to FP8 weights and activations using per-tensor quantization through the [AutoFP8 repository](https://github.com/neuralmagic/AutoFP8), ready for inference with vLLM >= 0.5.0. 
 Calibrated with 10 repeats of each token in the tokenizer in random order to achieve 99% performance recovery on the Open LLM Benchmark evaluations. 
 Reduces space on disk by ~50%.
 Part of the [FP8 LLMs for vLLM collection](https://huggingface.co/collections/neuralmagic/fp8-llms-for-vllm-666742ed2b78b7ac8df13127).