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mostafaalimohmed/
gpt-oss-medical-qa

Name: gpt-oss-medical-qa
Availability: InStock

safetensors

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library_name: transformers

tags:

medical
lora
qlora
causal-lm
parameter-efficient-fine-tuning
tinyllama

Medical Diagnosis LoRA (TinyLlama-1.1B)

Model Details

Model Description

This model is a medical-domain adapted conversational language model built using Parameter-Efficient Fine-Tuning (LoRA) on top of TinyLlama-1.1B-Chat. The model is trained to generate concise final medical diagnoses (≤5 words) from open-ended, verifiable medical questions.

Developed by: Mostafa Ali Mohamed
Model type: Causal Language Model (Auto-regressive Transformer)
Language(s): English
License: Same as base model (TinyLlama-1.1B-Chat)
Finetuned from model: TinyLlama/TinyLlama-1.1B-Chat-v1.0

Model Sources

Base Model Repository: https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0
Training Dataset: FreedomIntelligence/medical-o1-verifiable-problem

Uses

Direct Use

This model is intended for research and experimentation in medical-domain adaptation of large language models, specifically for generating short diagnostic-style answers from structured medical questions.

Downstream Use

The model can be integrated into research prototypes, educational tools, or experimental medical QA systems requiring concise diagnostic outputs.

Out-of-Scope Use

This model must NOT be used for:

Real clinical diagnosis
Medical decision-making
Patient-facing healthcare systems
Emergency or safety-critical applications

It is a research prototype and not a certified medical tool.

Bias, Risks, and Limitations

Trained on only 500 samples, which limits generalization.
May hallucinate incorrect diagnoses.
Constrained to ≤5-word outputs, which may oversimplify complex medical cases.
Inherits biases from the base model and training dataset.

Recommendations

Outputs should always be verified by qualified medical professionals. This model should be used strictly for research and educational purposes.

How to Get Started with the Model

python

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

base_model = AutoModelForCausalLM.from_pretrained(
    "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
)

model = PeftModel.from_pretrained(
    base_model,
    "mostafaalimohmed/gpt-oss-medical-qa"
)

tokenizer = AutoTokenizer.from_pretrained(
    "mostafaalimohmed/gpt-oss-medical-qa"
)