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README.md

@@ -9,3 +9,16 @@ Mixture of Tokens is a fully-differentiable model that retains the benefits of M
 
 ## Tips:
 During inference, the model's computational performance is derived from combining tokens across batches into groups of a specified size, denoted as `group_size` in the model configuration. If the batch size is not evenly divisible by `group_size`, the model will internally pad the batch to ensure divisibility. To achieve optimal performance, it is advisable to conduct batched inference using a batch size that is a multiple of `group_size`.
+
+
+## Usage example
+
+The example generated by the model hub may be incorrect. To get started, try running:
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+tokenizer = AutoTokenizer.from_pretrained("jaszczur/mixture_of_tokens", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("jaszczur/mixture_of_tokens", trust_remote_code=True)
+pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
+pipe("Is mixture of tokens better than a dense model?")
+```