Update README.md

README.md

@@ -59,6 +59,72 @@ The model was evaluated on the test split of the `LLMTrace Classification datase
 | Mean Accuracy         | 98.46   |
 | TPR @ FPR=0.01        | 97.93   |
 
+
+## Quick start
+
+Requirements:
+- python3.11
+- [gigacheck](https://github.com/ai-forever/gigacheck)
+
+```bash
+pip install git+https://github.com/ai-forever/gigacheck
+```
+
+### Inference with transformers (with trust_remote_code=True)
+
+```python
+from transformers import AutoModel
+import torch
+
+gigacheck_model = AutoModel.from_pretrained(
+    "iitolstykh/GigaCheck-Classifier-Multi", 
+    trust_remote_code=True, 
+    device_map="cuda:0",
+    torch_dtype=torch.bfloat16
+)
+
+text = """To be, or not to be, that is the question:
+Whether ’tis nobler in the mind to suffer
+The slings and arrows of outrageous fortune,
+Or to take arms against a sea of troubles
+And by opposing end them.
+"""
+
+output = gigacheck_model([text])
+
+print([gigacheck_model.config.id2label[int(c_id)] for c_id in output.pred_label_ids])
+
+```
+
+### Inference with gigacheck
+
+```python
+import torch
+from transformers import AutoConfig
+from gigacheck.inference.src.mistral_detector import MistralDetector
+
+model_name = "iitolstykh/GigaCheck-Classifier-Multi"
+
+config = AutoConfig.from_pretrained(model_name)
+model = MistralDetector(
+    max_seq_len=config.max_length,
+    with_detr=config.with_detr,
+    id2label=config.id2label,
+    device="cpu" if not torch.cuda.is_available() else "cuda:0",
+).from_pretrained(model_name)
+
+text = """To be, or not to be, that is the question:
+Whether ’tis nobler in the mind to suffer
+The slings and arrows of outrageous fortune,
+Or to take arms against a sea of troubles
+And by opposing end them.
+"""
+
+output = model.predict(text)
+print(output)
+```
+
+
 ## Citation
 
 If you use this model in your research, please cite our papers: