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Update README.md

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@@ -24,7 +24,7 @@ Gemma v2 is a large language model released by Google on Jun 27th 2024.
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  - Original model: [google/gemma-2-27b-it](https://huggingface.co/google/gemma-2-27b-it)
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  The model is packaged into executable weights, which we call
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- [llamafiles](https://github.com/Mozilla-Ocho/llamafile)). This makes it
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  easy to use the model on Linux, MacOS, Windows, FreeBSD, OpenBSD, and
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  NetBSD for AMD64 and ARM64.
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@@ -75,11 +75,9 @@ of the README.
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  When using the browser GUI, you need to fill out the following fields.
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- Prompt template:
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  ```
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- <start_of_turn>system
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- {{prompt}}<end_of_turn>
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  {{history}}
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  <start_of_turn>{{char}}
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  ```
@@ -100,6 +98,12 @@ The Belobog Academy has discovered a new, invasive species of algae that can dou
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  '
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  ```
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  ## About llamafile
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  llamafile is a new format introduced by Mozilla Ocho on Nov 20th 2023.
@@ -110,8 +114,10 @@ AMD64.
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  ## About Quantization Formats
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  This model works should work well with any quantization format. Q6\_K is
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- the best choice overall here. But since this is a Google model, the
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- Google Brain floating point format (BF16) provides maximum quality.
 
 
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  ---
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  - Original model: [google/gemma-2-27b-it](https://huggingface.co/google/gemma-2-27b-it)
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  The model is packaged into executable weights, which we call
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+ [llamafiles](https://github.com/Mozilla-Ocho/llamafile). This makes it
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  easy to use the model on Linux, MacOS, Windows, FreeBSD, OpenBSD, and
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  NetBSD for AMD64 and ARM64.
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  When using the browser GUI, you need to fill out the following fields.
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+ Prompt template (note: this is for chat; Gemma doesn't have a system role):
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  ```
 
 
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  {{history}}
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  <start_of_turn>{{char}}
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  ```
 
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  '
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  ```
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+ ## About Upload Limits
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+
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+ Files which exceed the Hugging Face 50GB upload limit have a .cat𝑋
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+ extension. You need to use the `cat` command locally to turn them back
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+ into a single file, using the same order.
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+
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  ## About llamafile
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  llamafile is a new format introduced by Mozilla Ocho on Nov 20th 2023.
 
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  ## About Quantization Formats
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  This model works should work well with any quantization format. Q6\_K is
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+ the best choice overall. We tested that it's able to produce identical
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+ responses to the Gemma2 27B model that's hosted by Google themselves on
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+ aistudio.google.com. If you encounter any divergences, then try using
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+ the BF16 weights, which have the original fidelity.
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  ---
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