
# LinguaMatic

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.

## EasyDel

The model is finetuned Using a custom version of UltraChat on TPU-v4 POD using [EasyDel](https://github.com/erfanzar/EasyDeL)

## Prompting Method

LinguaMatic utilizes the llama2 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:

```python
def prompt_model(
    message: str,
    chat_history: None | list[list[str]] = [],
    system_prompt: str | None = None
) -> str:
    do_strip = False
    texts = [f"<s>[INST] <<SYS>>\n{system_prompt}\n<</SYS>>\n\n"] if system_prompt is not None else ["<s>[INST] "]
    for user_input, response in chat_history:
        user_input = user_input.strip() if do_strip else user_input
        do_strip = True
        texts.append(f'{user_input} [/INST] {response.strip()} </s><s>[INST] ')
    message = message.strip() if do_strip else message
    texts.append(f'{message} [/INST]')
    return ''.join(texts)
```

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.


## Contributing

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.
{"language": ["en", "fr", "es", "zh", "ru"], "license": "mit", "datasets": ["erfanzar/ShareGPT4", "HuggingFaceH4/no_robots"], "metrics": ["accuracy"], "pipeline_tag": "text-generation"}
transformers safetensors gguf llama text-generation en fr es zh ru dataset:erfanzar/ShareGPT4 dataset:HuggingFaceH4/no_robots license:mit autotrain_compatible endpoints_compatible text-generation-inference region:us