Falcon Mamba
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Falcon Mamba
Overview :
Falcon Mamba is the first 7B large-scale model released by the Technology Innovation Institute (TII) in Abu Dhabi that does not use attention mechanisms. This model is free from the computational and storage costs that increase with longer sequences, while still maintaining performance on par with current state-of-the-art models.
Target Users :
The Falcon Mamba model is designed for researchers and developers who need to handle large-scale language models, especially in scenarios involving extensive data processing and long sequences. Its strength lies in its ability to deliver performance comparable to existing top models while overcoming the limitations of traditional attention-based models when dealing with large sequences.
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Use Cases
Researchers use Falcon Mamba for natural language processing tasks such as text generation and summarization.
Developers leverage this model to generate coherent and contextually relevant responses in conversational systems.
Companies utilize Falcon Mamba to enhance the accuracy of understanding and answering questions in knowledge-based systems.
Features
Handles sequences of arbitrary lengths without the need for attention mechanisms.
Operates on a single 24GB GPU without requiring additional storage.
The time taken to generate new tokens is independent of the context size.
Trained on approximately 5500GT of data, including refined web data and high-quality technical data.
Excels in multiple benchmark tests, competing with existing state-of-the-art models.
Supports APIs within the Hugging Face ecosystem for easy integration and use.
How to Use
1. Install the latest version of the Hugging Face transformers library or install from source.
2. Import AutoModelForCausalLM and AutoTokenizer.
3. Retrieve the Falcon Mamba model using the model_id.
4. Convert the input text into a format acceptable to the model using the tokenizer.
5. Set generation parameters, such as max_new_tokens and do_sample.
6. Call the model.generate method to generate text.
7. Use the tokenizer.decode method to convert the generated tokens back into text.
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