---
library_name: transformers
tags:
- sft
- lora
- unsloth
license: apache-2.0
datasets:
- gretelai/symptom_to_diagnosis
language:
- en
base_model:
- unsloth/Qwen2.5-0.5B-Instruct
pipeline_tag: text-generation
---

# Model Card for SympQwen-0.5B


## 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.

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("khazarai/SympQwen-0.5B")
model = AutoModelForCausalLM.from_pretrained(
    "khazarai/SympQwen-0.5B",
    device_map={"": 0}
)

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),
)
```