Model Card for Model ID

Model Details

SympQwen-0.5B is a fine-tuned variant of the Qwen2.5-0.5B-Instruct language model—adapted specifically for the task of medical symptom-to-diagnosis mapping. It is trained to generate plausible diagnoses from patient-like descriptions of symptoms, based on the labeled examples from the gretelai/symptom_to_diagnosis dataset. This makes it suitable for assisting with clinical symptom interpretation in research or educational settings.

Uses

Direct Use

Primary Use Cases:

Assisting medical students in practicing diagnostic reasoning.
Providing clinicians or educators with a tool for generating potential diagnostic hypotheses from symptom descriptions.
Serving as a base model for research in AI-based clinical decision support systems.

Out-of-Scope Use

Direct clinical diagnosis or patient self-assessment.
Medical decision-making without oversight from qualified professionals.
Use in high-stakes environments where erroneous diagnosis could result in harm.

Bias, Risks, and Limitations

Small Dataset: With just over 1,000 examples, the model may not generalize well to rare, atypical, or unseen symptom presentations.
Imbalanced Labels: Some diagnoses (e.g., jaundice, migraine) are under-represented, which may bias outputs toward more frequent classes.
Synthetic Language: Symptom descriptions are LLM-generated—not actual patient narratives—and may lack real-world variability or nuance.
Not a Diagnostic Tool: This model is intended for research and educational augmentation only. It should not replace professional medical evaluation or diagnostic workflows.

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

tokenizer = AutoTokenizer.from_pretrained("unsloth/Qwen2.5-0.5B-Instruct",)
base_model = AutoModelForCausalLM.from_pretrained(
    "unsloth/Qwen2.5-0.5B-Instruct",
    device_map={"": 0}
)

model = PeftModel.from_pretrained(base_model,"khazarai/SympQwen-0.5B")

question = "I have a rash on my skin that is itchy and has a different color than the rest of my skin. I also have some firm pimples or breakouts on my skin."

messages = [
    {"role" : "user",   "content" : question}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize = False,
    add_generation_prompt = True,
)

from transformers import TextStreamer
_ = model.generate(
    **tokenizer(text, return_tensors = "pt").to("cuda"),
    max_new_tokens = 512,
    streamer = TextStreamer(tokenizer, skip_prompt = True),
)

For pipeline:

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

tokenizer = AutoTokenizer.from_pretrained("unsloth/Qwen2.5-0.5B-Instruct")
base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2.5-0.5B-Instruct")
model = PeftModel.from_pretrained(base_model, "khazarai/SympQwen-0.5B")

question = "I have a rash on my skin that is itchy and has a different color than the rest of my skin. I also have some firm pimples or breakouts on my skin."

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
messages = [
    {"role": "user", "content": question}
]
pipe(messages)