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1from transformers import AutoModelForCausalLM, AutoTokenizer
2
3# 모델과 토크나이저 로드
4model = AutoModelForCausalLM.from_pretrained(
5 "iscreamai/gemma-3-1b-agentbank-checkpoint-57500",
6 torch_dtype="auto",
7 device_map="auto"
8)
9tokenizer = AutoTokenizer.from_pretrained("iscreamai/gemma-3-1b-agentbank-checkpoint-57500")
10
11# 추론 예시
12prompt = "안녕하세요! 무엇을 도와드릴까요?"
13inputs = tokenizer(prompt, return_tensors="pt")
14outputs = model.generate(**inputs, max_length=100, do_sample=True, temperature=0.7)
15response = tokenizer.decode(outputs[0], skip_special_tokens=True)
16print(response)1{
2 "checkpoint_path": "outputs/google_gemma_3_1b_it-agentbank-20250717_231738/checkpoint-57500",
3 "checkpoint_name": "checkpoint-57500",
4 "upload_timestamp": "2025-07-18T22:17:26.745422",
5 "framework": "transformers",
6 "training_framework": "AgentBank",
7 "model_type": "gemma3_text",
8 "architectures": [
9 "Gemma3ForCausalLM"
10 ],
11 "vocab_size": 262144,
12 "hidden_size": 1152,
13 "num_hidden_layers": 26,
14 "torch_dtype": "bfloat16",
15 "transformers_version": "4.53.2",
16 "global_step": 57500,
17 "epoch": 0.3093745187040436,
18 "total_flos": 3.3421318490571264e+17,
19 "train_batch_size": 4,
20 "learning_rate": null,
21 "last_training_log": {
22 "epoch": 0.3093745187040436,
23 "grad_norm": 11.375,
24 "learning_rate": 4.9302202076188976e-05,
25 "loss": 2.41,
26 "step": 57500
27 },
28 "file_count": 12,
29 "total_size_bytes": 6035148749,
30 "total_size_gb": 5.62
31}