inarikami/gemma-12b-medical-mc-qlora-v2

Gemma 12B Medical MC QLoRA v2

A medical multiple choice focused fine-tuned version of Google's Gemma 3 12B Instruct model using QLoRA (Quantized Low-Rank Adaptation) techniques, specifically trained on medical multiple choice datasets.

Model Details

Model Description

This model is a QLoRA adapter fine-tuned on Google's Gemma 3 12B Instruct model specifically for medical multiple choice applications. The model has been trained for 7000 steps on medical multiple choice datasets to understand and generate medical content in a multiple choice format while maintaining the conversational capabilities of the base model.

• Developed by: inarikami
• Model type: Causal Language Model (QLoRA Adapter)
• Language(s): English
• Base Model: google/gemma-3-12b-it
• License: Same as base model (Gemma License)
• Checkpoint: 7000 steps

Model Sources [optional]

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• Demo [optional]: [More Information Needed]

Uses

Direct Use

This model is designed for medical question-answering, educational purposes, and healthcare-related conversations. It can be used to:

• Answer general medical questions
• Provide educational content about health topics
• Assist with medical terminology explanations
• Support healthcare professionals with information retrieval

Downstream Use

The model can be further fine-tuned for specific medical domains such as:

• Clinical documentation
• Medical summarization tasks
• Specialized medical subspecialties
• Integration into healthcare chatbots or educational platforms

Out-of-Scope Use

Important: This model should NOT be used for:

• Medical diagnosis or treatment recommendations
• Emergency medical situations
• Replacing professional medical advice
• Making clinical decisions without human oversight
• Patient care without qualified medical supervision

Bias, Risks, and Limitations

This model has several important limitations:

• Medical Accuracy: While trained on medical data, the model may generate inaccurate or outdated medical information
• Training Data Bias: The model may reflect biases present in medical literature and training data
• Language Limitations: Primarily trained on English medical content
• Hallucination Risk: Like all large language models, it may generate plausible-sounding but incorrect information
• Regional Variations: Medical practices and guidelines vary by region; the model may not reflect local standards

Recommendations

Critical Safety Guidelines:

• Always verify medical information with qualified healthcare professionals
• Use only as a supplementary educational tool, never for medical decision-making
• Implement human oversight for any healthcare-related applications
• Regularly update and validate outputs against current medical guidelines
• Be aware of potential biases in medical recommendations across different populations

How to Get Started with the Model

from peft import PeftModel
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Load base model and tokenizer
base_model = "google/gemma-3-12b-it"
model = AutoModelForCausalLM.from_pretrained(
    base_model,
    torch_dtype=torch.float16,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(base_model)

# Load QLoRA adapter
model = PeftModel.from_pretrained(model, "your-username/gemma-12b-medical-qlora-v2")

# Generate medical responses
prompt = "What are the symptoms of diabetes?"
inputs = tokenizer(prompt, return_tensors="pt")
with torch.no_grad():
    outputs = model.generate(**inputs, max_length=200, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Training Details

Training Data

This model was fine-tuned on a medical multiple choice question dataset. The training data consists of:

• Medical multiple choice questions and answers
• Healthcare-related Q&A pairs in multiple choice format
• Medical terminology and concept explanations structured as multiple choice problems

Note: The model's training on multiple choice data means it may be particularly well-suited for medical education scenarios involving multiple choice questions, but may require additional fine-tuning for other medical text generation tasks.

Training Procedure

Preprocessing [optional]

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Training Hyperparameters

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Speeds, Sizes, Times [optional]

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

• Hardware Type: [More Information Needed]
• Hours used: [More Information Needed]
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• Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

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Software

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Citation [optional]

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Glossary [optional]

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Framework versions

• PEFT 0.16.0