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1from transformers import AutoTokenizer, AutoModelForCausalLM
2tokenizer = AutoTokenizer.from_pretrained('your-hf-id/model-name')
3model = AutoModelForCausalLM.from_pretrained('your-hf-id/model-name')
4
5inputs = tokenizer("local currentTargetEntity = self.Entity.AI", return_tensors='pt')
6outputs = model.generate(**inputs)
7print(tokenizer.decode(outputs))| Before | After | Change | |
|---|---|---|---|
| Perplexity | 46.14 | 5.34 | ↓8.6x |
| Eval loss | 3.83 | 1.68 | ↓ |
| Speed(sec) | 1.30s | 1.28s | - |
1from transformers import AutoTokenizer, AutoModelForCausalLM
2tokenizer = AutoTokenizer.from_pretrained('your-hf-id/model-name')
3model = AutoModelForCausalLM.from_pretrained('your-hf-id/model-name')
4
5inputs = tokenizer("local currentTargetEntity = self.Entity.AI", return_tensors='pt')
6outputs = model.generate(**inputs)
7print(tokenizer.decode(outputs))| 학습 전 | 학습 후 | 변화폭 | |
|---|---|---|---|
| Perplexity | 46.14 | 5.34 | ↓8.6배 |
| Eval loss | 3.83 | 1.68 | ↓ |
| 평가속도 | 1.30s | 1.28s | - |