Update README.md

README.md

@@ -38,7 +38,36 @@ Post-training methods also support the safety and alignment of LLMs. This import
 
 ## How to use
 
-TODO
+If you have a CUDA GPU (>=12GB VRAM), the best way to use Relay is with the [relaylm.py]() inference script. Just run:
+```bash
+curl https://danlou.co/f/relaylm.py | python -
+```
+
+This script will select the best model for your available VRAM, download, load, and start and interactive chat session.
+It does not have any dependencies besides `transformers >= 4.45.1`.
+
+Alternatively, if you do not have a CUDA GPU (e.g., on a Mac), you can use the [GGUF versions]() through LM Studio.
+
+With [relaylm.py](), you can also use the model declaratively, outside of an interactive chat session:
+
+```python
+from relaylm import suggest_relay_model, RelayLM
+
+def favorite_holiday(relay: RelayLM, country: str) -> str:
+    relay.init_context()
+    relay.join(role='model', channel=country.lower())
+    relay.cast(role='model', desc=f"I'm from {country}.")
+    relay.message(role='input', content="What's your favorite holiday?")
+    relay.respond(role='model')
+    response = relay.get_last()
+    return response['content']
+
+model_info = suggest_relay_model()
+relay = RelayLM(**model_info)
+
+print(favorite_holiday(relay, "Portugal"))
+print(favorite_holiday(relay, "China"))
+```
 
 ## Safety testing
 
@@ -56,3 +85,16 @@ TODO
 
 TODO
 
+## Citation
+
+If you use Relay in your research, please cite it as follows:
+```
+@misc{relay2024,
+  author = {Loureiro, Daniel},
+  title = {Relay: LLMs as IRCs},
+  year = {2024},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  howpublished = {\url{https://github.com/danlou/relay}},
+}
+```