Update README.md

README.md

@@ -1,3 +1,47 @@
----
-license: mit
----
+---
+license: mit
+---
+
+# 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-8B", strict=True).to('cuda')
+tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B")
+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.)_