Qwen3-1.7B-medicaldataset

4
1
1.7B
license:mit
by
XformAI-india
Other
OTHER
1.7B params
New
4 downloads
Early-stage
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Edge AI:
Mobile
Laptop
Server
4GB+ RAM
Mobile
Laptop
Server
Quick Summary

AI model with specialized capabilities.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
2GB+ RAM

Code Examples

💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
💡 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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