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LinjieMu/
MedCEG

Name: MedCEG
Rating: 2.5 (6 reviews)

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This code demonstrates how to generate responses using MedCEG.

python

import transformers
import torch

# 1. Load Model & Tokenizer
model_id = "XXX/MedCEG"
tokenizer = transformers.AutoTokenizer.from_pretrained(model_id)
model = transformers.AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

# 2. Define Input
question = "A 78-year-old Caucasian woman presented with..."
suffix = "\nPut your final answer in \\boxed{}."
messages = [{"role": "user", "content": question + suffix}]

# 3. Generate
input_ids = tokenizer.apply_chat_template(
    messages, 
    add_generation_prompt=True, 
    return_tensors="pt"
).to(model.device)

outputs = model.generate(input_ids, max_new_tokens=8196, do_sample=False)
decoded_response = tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True)

print(decoded_response)

LinjieMu/MedCEG

LinjieMu/
MedCEG