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1{
2 "SDTM_DOMAIN": "AE",
3 "SDTM_VARIABLE": "AESEV",
4 "REASONING": "AESEV represents the severity of an adverse event and is a standard variable in the AE domain."
5}
6
7## Load Model
8---------------
9
10from transformers import AutoTokenizer, AutoModelForCausalLM
11from peft import PeftModel
12
13BASE_MODEL = "Qwen/Qwen2.5-1.5B"
14LORA_REPO = "karamalanagendra/sdtm-qwen-lora"
15
16tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
17tokenizer.pad_token_id = tokenizer.eos_token_id
18
19base_model = AutoModelForCausalLM.from_pretrained(
20 BASE_MODEL,
21 device_map="cpu" # or "cuda" if GPU available
22)
23
24model = PeftModel.from_pretrained(base_model, LORA_REPO)
25model.eval()
26
27-------------------
28## Sample Test
29-------------------
30
31prompt = """
32You are an SDTM mapping engine.
33Return ONLY valid JSON.
34Do NOT explain.
35Do NOT add text outside JSON.
36
37Instruction:
38Map the raw variable to SDTM.
39
40Input:
41Table: AE_RAW
42Variable: AEACN2
43Description: Action taken with Study Medication
44
45Output JSON (return ONE object only):
46{
47 "SDTM_DOMAIN": "",
48 "SDTM_VARIABLE": "",
49 "REASONING": ""
50}
51
52
53"""
54
55raw_output = cpu_generate_json(prompt)
56parsed = extract_last_json(raw_output)
57
58print(parsed)
59
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