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Update README.md

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@@ -54,17 +54,20 @@ Meta developed and publicly released the Llama 2 family of large language models
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  **License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
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  **Research Paper** ["Llama-2: Open Foundation and Fine-tuned Chat Models"](arxiv.org/abs/2307.09288)
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- '''
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- pip install llama-cpp-python==0.2.6
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- '''
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- ### Sample Code
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- '''python
 
 
 
 
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  from llama_cpp import Llama
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  llm = Llama(model_path="./models/7B/llama-model.gguf")
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  output = llm("Q: Name the planets in the solar system? A: ", max_tokens=32, stop=["Q:", "\n"], echo=True)
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  print(output)
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- '''
 
 
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  {
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  "id": "cmpl-xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx",
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  "object": "text_completion",
@@ -84,6 +87,7 @@ print(output)
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  "total_tokens": 42
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  }
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  }
 
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  ## Intended Use
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  **Intended Use Cases** Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
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  **License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
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  **Research Paper** ["Llama-2: Open Foundation and Fine-tuned Chat Models"](arxiv.org/abs/2307.09288)
 
 
 
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+
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+ ```
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+ pip install llama-cpp-python==0.2.6
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+ ```
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+ Then you can use the model like this:
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+ ```python
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  from llama_cpp import Llama
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  llm = Llama(model_path="./models/7B/llama-model.gguf")
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  output = llm("Q: Name the planets in the solar system? A: ", max_tokens=32, stop=["Q:", "\n"], echo=True)
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  print(output)
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+ ```
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+ ###EXample Output of the above code
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+ ```
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  {
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  "id": "cmpl-xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx",
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  "object": "text_completion",
 
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  "total_tokens": 42
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  }
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  }
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+ ```
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  ## Intended Use
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  **Intended Use Cases** Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
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