Update README.

README.md

@@ -46,17 +46,17 @@ We have provided an example function to generate output from OpenELM models load
 
 You can try the model by running the following command:
 ```
-python generate_openelm.py --model [OPEN_ELM_MODEL] --hf_access_token [HF_ACCESS_TOKEN] --prompt 'Once upon a time there was' --generate_kwargs repetition_penalty=1.2
+python generate_openelm.py --model [MODEL_NAME] --hf_access_token [HF_ACCESS_TOKEN] --prompt 'Once upon a time there was' --generate_kwargs repetition_penalty=1.2
 ```
 Please refer to [this link](https://huggingface.co/docs/hub/security-tokens) to obtain your hugging face access token.
 
 Additional arguments to the hugging face generate function can be passed via `generate_kwargs`. As an example, to speedup the inference, you can try [lookup token speculative generation](https://huggingface.co/docs/transformers/generation_strategies) by passing the `prompt_lookup_num_tokens` argument as follows:
 ```
-python generate_openelm.py --model [OPEN_ELM_MODEL] --hf_access_token [HF_ACCESS_TOKEN] --prompt 'Once upon a time there was' --generate_kwargs repetition_penalty=1.2 prompt_lookup_num_tokens=10
+python generate_openelm.py --model [MODEL_NAME] --hf_access_token [HF_ACCESS_TOKEN] --prompt 'Once upon a time there was' --generate_kwargs repetition_penalty=1.2 prompt_lookup_num_tokens=10
 ```
 Alternatively, model-wise speculative generation with an [assistive model](https://huggingface.co/blog/assisted-generation) can be also tried by passing a smaller model model through the `assistant_model` argument, for example:
 ```
-python generate_openelm.py --model [OPEN_ELM_MODEL] --hf_access_token [HF_ACCESS_TOKEN] --prompt 'Once upon a time there was' --generate_kwargs repetition_penalty=1.2 --assistant_model [SMALLER_MODEL]
+python generate_openelm.py --model [MODEL_NAME] --hf_access_token [HF_ACCESS_TOKEN] --prompt 'Once upon a time there was' --generate_kwargs repetition_penalty=1.2 --assistant_model [SMALLER_MODEL_NAME]
 ```