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--- |
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datasets: |
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- erfanzar/UltraChat-Mixin |
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language: |
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- en |
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- fr |
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- es |
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metrics: |
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- accuracy |
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pipeline_tag: text-generation |
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tags: |
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- code |
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--- |
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# LinguaMatic |
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LinguaMatic is an advanced AI model designed to handle a wide range of Natural Language Processing (NLP) tasks. With its powerful capabilities, LinguaMatic can assist with tasks such as text classification, sentiment analysis, language translation, question answering, and much more. |
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## EasyDel |
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The model is finetuned Using a custom version of UltraChat on TPU-v4 POD using [EasyDel](https://github.com/erfanzar/EasyDeL) |
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## Prompting Method |
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LinguaMatic utilizes the OC prompting method to generate responses. This method, named after the friendly and intelligent llama, enhances the model's ability to engage in meaningful conversations. The `prompt_model` function provided below demonstrates how the llama2 prompting method is implemented: |
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```python |
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def prompt_model( |
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message: str, |
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chat_history: Optional[List[str] | List[List[str]]] = None, |
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system_prompt: Optional[str] = None |
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): |
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if chat_history is None: |
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chat_history = [] |
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system = f"<|system|>\n{system_prompt}</s>" if system_prompt is not None else "" |
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ua = "" |
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for user_input, response in chat_history: |
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ua += f"<|user|>\n{user_input}</s>\n" + f"<|assistant|>\n{response}</s>\n" |
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return system + ua + f"<|user|>\n{message}</s>\n<|assistant|>\n" |
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``` |
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The `prompt_model` function takes a `message` as input, along with the `chat_history` and `system_prompt`. It generates a formatted text that includes the system prompt, user inputs, and the current message. This approach allows LinguaMatic to maintain context and provide more coherent and context-aware responses. |
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## Contributing |
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We welcome contributions to enhance LinguaMatic's capabilities and improve its performance. If you encounter any issues or have suggestions for improvement, please feel free to submit a pull request or open an issue on [EasyDel](https://github.com/erfanzar/EasyDeL) GitHub repository. |
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