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@@ -169,7 +169,7 @@ Developers should apply responsible AI best practices and are responsible for en
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  ### Model
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- * Architecture: Phi-3 Small-128K-Instruct has 7B parameters and is a dense decoder-only Transformer model. The model is fine-tuned with Supervised fine-tuning (SFT) and Direct Preference Optimization (DPO) to ensure alignment with human preferences and safety guidlines.
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  * Inputs: Text. It is best suited for prompts using chat format.
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  * Context length: 128K tokens
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  * GPUs: 1024 H100-80G
@@ -248,7 +248,7 @@ We take a closer look at different categories across 80 public benchmark dataset
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  * [Triton](https://github.com/openai/triton)
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  ## Hardware
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- Note that by default, the Phi-3-Small model uses flash attention, which requires certain types of GPU hardware to run. We have tested on the following GPU types:
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  * NVIDIA A100
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  * NVIDIA A6000
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  * NVIDIA H100
 
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  ### Model
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+ * Architecture: Phi-3 Small-128K-Instruct has 7B parameters and is a dense decoder-only Transformer model with alternating dense and blocksparse attentions. The model is fine-tuned with Supervised fine-tuning (SFT) and Direct Preference Optimization (DPO) to ensure alignment with human preferences and safety guidlines.
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  * Inputs: Text. It is best suited for prompts using chat format.
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  * Context length: 128K tokens
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  * GPUs: 1024 H100-80G
 
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  * [Triton](https://github.com/openai/triton)
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  ## Hardware
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+ Note that by default, the Phi-3-Small model uses flash attention 2 and Triton blocksparse attention, which requires certain types of GPU hardware to run. We have tested on the following GPU types:
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  * NVIDIA A100
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  * NVIDIA A6000
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  * NVIDIA H100