| # Llamba Models | |
| The Llamba models are part of Cartesia's [Edge](https://github.com/cartesia-ai/edge) library, designed for efficient, high-performance machine learning applications. | |
| For more details, refer to the [paper](#). | |
| --- | |
| ## Usage | |
| ### Llamba on PyTorch | |
| To use Llamba with PyTorch: | |
| 1. Install the required package: | |
| ```bash | |
| pip install --no-binary :all: cartesia-pytorch | |
| ``` | |
| 2. Load and run the model | |
| ```python | |
| from transformers import AutoTokenizer | |
| from cartesia_pytorch.Llamba.llamba import LlambaLMHeadModel | |
| model = LlambaLMHeadModel.from_pretrained("AvivBick/Llamba-1B", strict=True).to('cuda') | |
| tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-1B") | |
| input_ids = tokenizer("Hello, my name is", return_tensors="pt").input_ids | |
| input_ids = input_ids.to('cuda') | |
| output = model.generate(input_ids, max_length=100)[0] | |
| print(tokenizer.decode(output, skip_special_tokens=True)) | |
| ``` | |
| ### Llamba on MLX | |
| To run Llamba with the Metal framework: | |
| _(Add specific instructions here when available.)_ | |
| --- | |
| ### Evaluations | |
| Details on model performance, benchmarks, and evaluation metrics can be found in the [paper link](#). | |
| _(Expand on this section if specific results or datasets are available.)_ |