Create README.md

README.md

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+---
+base_model:
+- unsloth/Qwen3-14B-unsloth-bnb-4bit
+---
+
+This fine-tuned LLM is intended for the task of geocoding complex location references, and accompanies [Coordinates from Context: Using LLMs to Ground Complex Location References](https://arxiv.org/pdf/2510.08741) (Masis & O'Connor, EACL 2026).
+
+### Model description
+The base model is a quantized Qwen3-14B model (```unsloth/Qwen3-14B-unsloth-bnb-4bit```), which has been fine-tuned for geocoding, i.e. linking a location reference to an actual geographic location. 
+The model was trained using parameter-efficient fine-tuning via low-rank adaptation. 
+It was trained for our 'Geoparser-augmented' approach, where a separate geoparsing tool augments the inputs with the center coordinates of mentioned locations;
+our fine-tuned model then uses both the original location reference and the mentioned locations' coordinates to generate the described location's bounding box. 
+For more details, please see the accompanying paper.
+
+### Training data
+The model is trained on 13k examples from the training subset of the [GeoCoDe dataset](https://github.com/EgoLaparra/geocode-data), where the input is a complex location reference and the center coordinates of each mentioned location and the output is the location's corresponding bounding box. 
+
+### Intended uses and limitations
+Due to data limitations, this model has been trained and evaluated for our task only in Mainstream American English.
+
+
+### Usage
+We have included sample code below to use the model. For the system prompt and example prompts, please see the appendices in the accompanying paper. 
+
+```
+from unsloth import FastLanguageModel
+import torch
+
+# Load model from Huggingface Hub
+model, tokenizer = FastLanguageModel.from_pretrained(
+  model_name = "tmasis/geocoding-complex-location-references",
+  max_seq_length = 2048,
+  load_in_4bit = True)
+FastLanguageModel.for_inference(model)
+
+messages = [{"role": "system", "content": <system_prompt>},
+    {"role": "user", "content": <prompt>}]
+text = tokenizer.apply_chat_template(messages, tokenizer=False,
+    add_generation_prompt = True, enable_thinking = False)
+outputs = model.generate(**tokenizer(text, return_tensors="pt").to("cuda"),
+    max_new_tokens=1024, temperature=0.7, top_p=0.8, top_k=20)
+response = tokenizer.batch_decode(outputs)[0]
+```