gemma coffee model

gemma_Ko_coffee_load_model_tokenizerChange.py

@@ -0,0 +1,48 @@
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+
+# 베이스 모델에서 토크나이저 불러오기
+# tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it")
+tokenizer = AutoTokenizer.from_pretrained("beomi/gemma-ko-2b")
+
+
+# 저장한 모델 경로
+model_dir = './gemma_outputs/gemma-ko-2b-beans-20240915-01'
+model = AutoModelForCausalLM.from_pretrained(model_dir)
+# tokenizer = AutoTokenizer.from_pretrained(model_dir)
+
+# 모델을 CPU로 이동 (만약 GPU를 쓴다면 'cuda'로 바꿔줘)
+model.to("cpu") #cpu
+
+conversation_pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512)
+
+
+def chat_with_model(input_text):
+    # 대화용 프롬프트를 생성
+    messages = [{"role": "user", "content": input_text}]
+
+    # 토크나이저로 입력을 프롬프트 형태로 변환
+    prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+
+    # 모델이 응답을 생성
+    # response = conversation_pipeline(prompt, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
+    response = conversation_pipeline(prompt, do_sample=True, temperature=0.2, top_k=50, top_p=0.95, add_special_tokens=True)
+
+    # 모델의 생성된 응답 추출
+    generated_text = response[0]["generated_text"]
+    model_response = generated_text[len(prompt):]  # 입력 프롬프트를 제거하고 응답만 반환
+    return model_response
+
+
+# 대화를 계속 이어나갈 수 있는 구조
+def interactive_chat():
+    print("대화형 모드에 오신 것을 환영합니다! '종료'라고 입력하면 대화가 종료됩니다.")
+    while True:
+        user_input = input("사용자: ")  # 사용자 입력 받기
+        if user_input.lower() == "종료":  # '종료'라고 입력하면 대화 종료
+            print("대화를 종료합니다.")
+            break
+        model_reply = chat_with_model(user_input)  # 모델의 응답 받기
+        print(f"모델: {model_reply}")  # 모델의 응답 출력
+
+# 대화 시작
+interactive_chat()

gemma_outputs/gemma-2b-it-sum-ko-beans-1/config.json

@@ -0,0 +1,28 @@
+{
+  "_name_or_path": "google/gemma-2b-it",
+  "architectures": [
+    "GemmaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 2,
+  "eos_token_id": 1,
+  "head_dim": 256,
+  "hidden_act": "gelu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 16384,
+  "max_position_embeddings": 8192,
+  "model_type": "gemma",
+  "num_attention_heads": 8,
+  "num_hidden_layers": 18,
+  "num_key_value_heads": 1,
+  "pad_token_id": 0,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "torch_dtype": "float16",
+  "transformers_version": "4.38.1",
+  "use_cache": true,
+  "vocab_size": 256000
+}

gemma_outputs/gemma-2b-it-sum-ko-beans-1/generation_config.json

@@ -0,0 +1,7 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 2,
+  "eos_token_id": 1,
+  "pad_token_id": 0,
+  "transformers_version": "4.38.1"
+}

gemma_outputs/gemma-2b-it-sum-ko-beans-1/model-00001-of-00002.safetensors

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gemma_outputs/gemma-2b-it-sum-ko-beans-1/model-00002-of-00002.safetensors

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