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| Metric | Value |
|---|---|
| Global Steps | 14480 |
| Training Loss | 0.3822 |
| Training Runtime | 15150.72 seconds |
| Training Samples per Second | 11.47 samples/sec |
| Training Steps per Second | 0.96 steps/sec |
| Total FLOPs | 4.54e+16 |
| Epoch | 5.0 |
1!pip install transformers torch
2
3from transformers import AutoModelForCausalLM, AutoTokenizer
4import torch
5
6model_name = "lafarizo/indo_medical_gpt2_v2"
7model = AutoModelForCausalLM.from_pretrained(model_name)
8tokenizer = AutoTokenizer.from_pretrained(model_name)
9
10device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
11model.to(device)
12
13if tokenizer.pad_token is None:
14 tokenizer.pad_token = tokenizer.eos_token
15
16input_text = input("Pertanyaan: ")
17
18inputs = tokenizer(input_text, return_tensors="pt", truncation=True, padding=True, max_length=512)
19
20input_ids = inputs['input_ids'].to(device)
21attention_mask = inputs['attention_mask'].to(device)
22
23outputs = model.generate(
24 input_ids=input_ids,
25 attention_mask=attention_mask,
26 max_length=512,
27 num_beams=5,
28 temperature=0.7,
29 top_k=50,
30 top_p=0.9,
31 no_repeat_ngram_size=2,
32 do_sample=True,
33 eos_token_id=tokenizer.eos_token_id,
34 pad_token_id=tokenizer.pad_token_id
35)
36
37generated_answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
38
39if generated_answer.lower().startswith(input_text.lower()):
40 generated_answer = generated_answer[len(input_text):].strip()
41
42print("Jawaban: ", generated_answer)