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Medico
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Model Details
Model Provider
Medico
Model Tree
Qwen/Qwen3.8-27B
Input Modalities
Output Modalities
Supported Functionality
Medico/Qwen3.8-27B-Radiology-Impression is a dense 27-Billion parameter clinical foundation model fine-tuned on 659,381 real-world clinical findings-to-impression pairs across diverse imaging modalities (CT, MRI, Chest Radiography, Ultrasound, and PET/CT).
Qwen/Qwen3.8-27B (Dense 27B model, qwen3_5 architecture)You can chat with this model with unlimited access for free using any of the following methods:
Open the official Hugging Face Space: 👉 https://huggingface.co/spaces/Medico/Qwen3.8-27B-Radiology-Impression-Demo
Run directly on your local machine with GPU/CPU:
bash
ollama run hf.co/Medico/Qwen3.8-27B-Radiology-ImpressionRun this Python snippet on any free GPU environment (Tesla T4, L4, A10G):
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfigfrom peft import PeftModel BASE_MODEL = "Qwen/Qwen3.8-27B"LORA_REPO = "Medico/Qwen3.8-27B-Radiology-Impression" bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.float16, bnb_4bit_use_double_quant=True) tokenizer = AutoTokenizer.from_pretrained(LORA_REPO, trust_remote_code=True)base_model = AutoModelForCausalLM.from_pretrained( BASE_MODEL, quantization_config=bnb_config, device_map="auto", trust_remote_code=True)model = PeftModel.from_pretrained(base_model, LORA_REPO) # Clinical Promptingfindings = """CTA CHEST: Large saddle pulmonary embolus straddling the bifurcation with RV/LV ratio of 1.4 and interventricular flattening.""" messages = [ {"role": "system", "content": "You are an expert diagnostic radiologist. Generate a structured clinical IMPRESSION."}, {"role": "user", "content": f"FINDINGS:\n{findings}"}] prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer(prompt, return_tensors="pt").to("cuda") with torch.no_grad(): outputs = model.generate(**inputs, max_new_tokens=300, temperature=0.2) print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))This model is released for research, clinical decision support benchmarking, and medical NLP exploration. It should not replace independent radiological interpretation by certified medical professionals.
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfigfrom peft import PeftModel BASE_MODEL = "Qwen/Qwen3.8-27B"LORA_REPO = "Medico/Qwen3.8-27B-Radiology-Impression" bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.float16, bnb_4bit_use_double_quant=True) tokenizer = AutoTokenizer.from_pretrained(LORA_REPO, trust_remote_code=True)base_model = AutoModelForCausalLM.from_pretrained( BASE_MODEL, quantization_config=bnb_config, device_map="auto", trust_remote_code=True)model = PeftModel.from_pretrained(base_model, LORA_REPO) # Clinical Promptingfindings = """CTA CHEST: Large saddle pulmonary embolus straddling the bifurcation with RV/LV ratio of 1.4 and interventricular flattening.""" messages = [ {"role": "system", "content": "You are an expert diagnostic radiologist. Generate a structured clinical IMPRESSION."}, {"role": "user", "content": f"FINDINGS:\n{findings}"}] prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer(prompt, return_tensors="pt").to("cuda") with torch.no_grad(): outputs = model.generate(**inputs, max_new_tokens=300, temperature=0.2) print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))