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Model: khazarai/SympQwen-0.5B
Source: Original Platform

README.md

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+---
+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),
+)
+```