Create README.md

README.md

@@ -0,0 +1,47 @@
+---
+license: gemma
+library_name: transformers
+pipeline_tag: text-generation
+extra_gated_heading: Access Gemma on Hugging Face
+extra_gated_prompt: >-
+  To access Gemma on Hugging Face, you’re required to review and agree to
+  Google’s usage license. To do this, please ensure you’re logged in to Hugging
+  Face and click below. Requests are processed immediately.
+extra_gated_button_content: Acknowledge license
+tags:
+- conversational
+base_model:
+- google/gemma-3-270m-it
+---
+
+# gemma3-270m-it-sms-verification_code_extraction
+#### SMS에서 인증번호를 추출 하는 gemma3 SFT 모델
+- 모바일 내장을 위한 Quantization 모델입니다. 일반 모델은 아래 링크를 참조해주세요
+  - https://huggingface.co/sg2023/gemma3-270m-it-sms-verification_code_extraction 
+
+- Input / Output
+  - `"본인인증번호는 315611 입니다. 정확히 입력해주세요."` -> `315611`
+  - `"안녕하세요"` -> `0`
+
+
+```python
+from transformers import Gemma3ForCausalLM, AutoTokenizer
+
+repo_id = "sg2023/gemma3-270m-it-sms-verification_code_extraction"
+tokenizer = AutoTokenizer.from_pretrained(repo_id)
+model = Gemma3ForCausalLM.from_pretrained(repo_id)
+model.eval()
+
+prompt = "본인인증번호는 315611 입니다. 정확히 입력해주세요."
+request_template = [{"role": "user", "content": prompt}]
+response_template = tokenizer.apply_chat_template(request_template, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(response_template, return_tensors="pt").to(model.device)
+input_ids = inputs["input_ids"][0]  # Tensor shape: (seq_len,)
+input_len = input_ids.shape[0]
+
+outputs = model.generate(**inputs, max_new_tokens=64)[0]
+outputs = outputs[input_len:]
+outputs = tokenizer.decode(outputs, skip_special_tokens=True)
+outputs = outputs.strip()
+print(outputs)  # 315611
+```