📝 Paper • 🤗 Hugging Face • 🧩 Github • 🪄 Project

🎬 Get Started

To work with AIMI Foundation Models for Radiology, you need to follow these steps:

Step 1: Import Required Libraries

The first step is to import necessary libraries from the Transformers library. These libraries will help you utilize the AIMI models for your applications.

from transformers import AutoTokenizer
from transformers import AutoModelForCausalLM

Step 2: Load the Pre-trained Model and Tokenizer

Next, you need to load the pre-trained model and tokenizer. Here’s how you do it:

tokenizer = AutoTokenizer.from_pretrained("StanfordAIMIRadLLaMA-7b", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("StanfordAIMIRadLLaMA-7b")

Step 3: Prepare Your Prompt

Now, you need to set up your prompt by defining a conversation structure with a designated “from” source. Think of this as preparing your question or statement that you’d like to feed into the model.

prompt = "Hi"
conv = [{"from": "human", "value": prompt}]

Step 4: Apply the Chat Template and Generate Response

The model needs to understand your input in a specific format. Once you’ve prepared the input, you can generate a response from the model:

input_ids = tokenizer.apply_chat_template(conv, add_generation_prompt=True, return_tensors="pt")
outputs = model.generate(input_ids)
response = tokenizer.decode(outputs[0])

Step 5: Print the Response

Finally, once the model has generated a response, you can print it to see the AI output:

print(response)

🔧 Troubleshooting and Tips

If you encounter issues while implementing AIMI Foundation Models, consider the following troubleshooting tips:

• Ensure that all required libraries are installed. You can do this using pip:

pip install transformers

• If the model doesn’t load properly, verify your internet connection and ensure the model’s name is correctly spelled.
• For issues with prompts, check that you are following the format specified in steps above.
• Keep an eye on the Hugging Face page for the model for updates or common issues reported by users.
• If the model produces unexpected outputs, consider revising your prompt for clarity.

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Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.