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@@ -48,8 +48,6 @@ NexaQuant on Reasoning Benchmarks Compared to BF16 and LMStudio's Q4_K_M
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  <img src="https://cdn-uploads.huggingface.co/production/uploads/66abfd6f65beb23afa427d8a/Cyh1zVvDHNBT598IkLHkd.png" width="80%" alt="Example" />
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  </div>
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- The general capacity has also greatly improved:
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  **General Capacity:**
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  | Benchmark | Full 16-bit | llama.cpp (4-bit) | NexaQuant (4-bit)|
@@ -114,9 +112,9 @@ Get the latest version from the [official website](https://lmstudio.ai/).
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  ## What's next
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- 1. Inference Nexa Quantized Deepseek-R1 distilled model on NPU.
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- 2. This model is designed for complex problem-solving, which is why it has a longer thinking process. We understand this can be an issue in some cases, and we're actively working on improvements.
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  ### Follow us
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  <img src="https://cdn-uploads.huggingface.co/production/uploads/66abfd6f65beb23afa427d8a/Cyh1zVvDHNBT598IkLHkd.png" width="80%" alt="Example" />
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  </div>
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  **General Capacity:**
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  | Benchmark | Full 16-bit | llama.cpp (4-bit) | NexaQuant (4-bit)|
 
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  ## What's next
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+ 1. This model is built for complex problem-solving, which is why it sometimes takes a long thinking process even for simple questions. We recognize this and are working on improving it in the next update.
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+ 2. Inference Nexa Quantized Deepseek-R1 distilled model on NPU.
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  ### Follow us
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