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change the requirement for the version of `transformers`.

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@@ -52,7 +52,7 @@ We will provide a detailed guide later on how to modify your `modeling_xxx.py` f
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  ### 2.2 Quick Start
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  #### 2.2.1 Environment Setup
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- You need to install `transformers>=4.53`, and we recommend using `lm_eval>=0.4.9` for running evaluations. We suggest managing your Python environment with `conda` for better dependency control.
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  ```bash
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  conda create -n sepcache python=3.10
@@ -64,7 +64,7 @@ pip install lm_eval==0.4.9
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  You can use `SepCache` by specifying `custom_generate="transformers-community/sep_cache"` or `custom_generate="Gausson/sep_cache"` when calling the `generate` function. In our demo, we have already prepared sample monkey patching for the `Llama 3 series` models and provided some common parameters for initializing `SepCache`.
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  ```python
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- # requires `transformers>=4.53.0`
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  from transformers import AutoModelForCausalLM, AutoTokenizer
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  # Preparing model, tokenizer, and model inputs
 
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  ### 2.2 Quick Start
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  #### 2.2.1 Environment Setup
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+ You need to install `transformers>=4.53.0,<4.54.0`, and we recommend using `lm_eval>=0.4.9` for running evaluations. We suggest managing your Python environment with `conda` for better dependency control.
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  ```bash
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  conda create -n sepcache python=3.10
 
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  You can use `SepCache` by specifying `custom_generate="transformers-community/sep_cache"` or `custom_generate="Gausson/sep_cache"` when calling the `generate` function. In our demo, we have already prepared sample monkey patching for the `Llama 3 series` models and provided some common parameters for initializing `SepCache`.
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  ```python
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+ # requires `transformers>=4.53.0,<4.54.0`
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  from transformers import AutoModelForCausalLM, AutoTokenizer
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  # Preparing model, tokenizer, and model inputs