Update README.md

README.md

@@ -23,10 +23,42 @@ The model is trained on 13k examples from the training subset of the [GeoCoDe da
 ### Limitations
 Due to data limitations, this model has been trained and evaluated for our task only in Mainstream American English. 
 
-
-### Usage
+### Usage (unsloth)
 The following code snippet illustrates how to use the model. For the system prompt we used and for example prompts, please see the appendices in the accompanying paper. 
 
+```python
+from unsloth import FastLanguageModel
+import torch
+
+model_name = "tmasis/geocoding-complex-location-references"
+
+# Load model and tokenizer from Huggingface Hub
+model, tokenizer = FastLanguageModel.from_pretrained(model_name)
+    model_name = model_name,
+    max_seq_length = 2048,
+    load_in_4bit = True,
+)
+FastLanguageModel.for_inference(model)
+
+# Prepare model input
+messages = [{"role": "system", "content": <system_prompt>},
+    {"role": "user", "content": <prompt>}]
+text = tokenizer.apply_chat_template(messages,
+    tokenize=False,
+    add_generation_prompt = True,
+    enable_thinking = False
+)
+
+# Conduct text generation
+outputs = model.generate(**tokenizer(text, return_tensors="pt").to(model.device),
+    max_new_tokens=1024, temperature=0.7, top_p=0.8, top_k=20)
+response = tokenizer.batch_decode(outputs)[0]
+print(response)
+```
+
+### Usage (HuggingFace transformers)
+Alternatively, you can use the HuggingFace transformers library. 
+
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer