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Update README.md

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@@ -12,10 +12,10 @@ pipeline_tag: text-generation
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  base_model: speakleash/Bielik-11B-v2.1-Instruct
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  ---
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  <p align="center">
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- <img src="https://huggingface.co/speakleash/Bielik-7B-Instruct-v0.1-GGUF/raw/main/speakleash_cyfronet.png">
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  </p>
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- # Bielik-11B-v2.2-Instruct-FP8
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  This model was obtained by quantizing the weights and activations of [Bielik-11B-v.2.1-Instruct](https://huggingface.co/speakleash/Bielik-11B-v2.1-Instruct) to FP8 data type, ready for inference with vLLM >= 0.5.0 or SGLang.
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  AutoFP8 is used for quantization. This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%.
@@ -98,4 +98,4 @@ print(response)
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  ## Contact Us
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- If you have any questions or suggestions, please use the discussion tab. If you want to contact us directly, join our [Discord SpeakLeash](https://discord.gg/CPBxPce4).
 
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  base_model: speakleash/Bielik-11B-v2.1-Instruct
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  ---
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  <p align="center">
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+ <img src="https://huggingface.co/speakleash/Bielik-11B-v2/raw/main/speakleash_cyfronet.png">
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  </p>
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+ # Bielik-11B-v2.1-Instruct-FP8
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  This model was obtained by quantizing the weights and activations of [Bielik-11B-v.2.1-Instruct](https://huggingface.co/speakleash/Bielik-11B-v2.1-Instruct) to FP8 data type, ready for inference with vLLM >= 0.5.0 or SGLang.
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  AutoFP8 is used for quantization. This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%.
 
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  ## Contact Us
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+ If you have any questions or suggestions, please use the discussion tab. If you want to contact us directly, join our [Discord SpeakLeash](https://discord.gg/pv4brQMDTy).