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| المواصفة | القيمة |
|---|---|
| Base Model | EleutherAI/gpt-neo-125M |
| Dataset | medalpaca/medical_meadow_medqa |
| LoRA Rank (r) | 8 |
| LoRA Alpha | 16 |
| Block Size | 256 |
| Epochs | 3 |
| Learning Rate | 2e-4 |
from transformers import AutoTokenizer, AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("hazemgalal1/gptneo125m-medical-qa-merged")
tokenizer = AutoTokenizer.from_pretrained("hazemgalal1/gptneo125m-medical-qa-merged")
prompt = (
"### Instruction:\nAnswer the following medical question.\n\n"
"### Question:\nWhat is the first-line treatment for hypertension?\n\n"
"### Answer:\n"
)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100, do_sample=True, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))