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from transformers import AutoTokenizer, AutoModelForCausalLM import torch tokenizer = AutoTokenizer.from_pretrained("health360/Sehty360-llama-3-8b-arabic-health-instruct") model = AutoModelForCausalLM.from_pretrained("health360/Sehty360-llama-3-8b-arabic-health-instruct", device_map='auto', torch_dtype=torch.bfloat16)
text = """
Input:
سلام عليكم اشعر بضيق في التنفس واعاني من كثرة البلغم
Response:
""" stop_word = "###END###"
Encode the input text
inputs = tokenizer(text, return_tensors='pt').to('cuda:0')
Remove token type ids if present, not all models use them
inputs.pop("token_type_ids", None)
Generating outputs with stopping criteria
outputs = model.generate( **inputs, max_new_tokens=512, do_sample=False, early_stopping=True, temperature=0.8, top_k=50, top_p=0.95, num_return_sequences=1, eos_token_id=tokenizer.encode(stop_word, add_special_tokens=False)[0] # Set EOS token to your stop word ) outputs = tokenizer.decode(outputs[0], skip_special_tokens=True) print(outputs)
Input:
سلام عليكم اشعر بضيق في التنفس واعاني من كثرة البلغم
Response:
وعليكم السلام! أنا هنا لمساعدتك. ضيق التنفس مع وجود بلغم يمكن أن يكون مؤشراً على وجود عدوى في الرئة أو القصبات.
أوصي بأن تقوم بزيارة طبيب مختص بأمراض الرئة والصدرية للحصول على تشخيص دقيق. يمكن للطبيب أن يطلب إجراء فحوصات دم، أشعة على الصدر، أو حتى اختبارات أخرى مثل تخطيط الرئة لتحديد نوع العدوى والمضاد المناسب لها.
إذا كنت ترغب، يمكنني مساعدتك في العثور على طبيب مختص بأمراض الرئة والصدرية في منطقتك. هل تود معرفة معلومات عن الأطباء المتاحين في منطقتك؟
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