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Model: khazarai/Medical-QA
Source: Original Platform

README.md

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+---
+library_name: transformers
+tags:
+- sft
+- unsloth
+- trl
+- medical
+license: apache-2.0
+datasets:
+- medalpaca/medical_meadow_medical_flashcards
+language:
+- en
+base_model:
+- unsloth/Qwen3-0.6B
+pipeline_tag: text-generation
+---
+
+# Model Card for Medical-QA
+
+
+## Model Details
+
+This model is a fine-tuned version of Qwen3-0.6B on a 34K medical Q&A dataset derived from the Anki Medical Curriculum flashcards.
+It is designed to assist with medical education and exam preparation, offering concise and contextually relevant answers to short medical questions.
+
+- **Base Model:** Qwen3-0.6B
+- **Fine-tuned on:** 34,000 question-answer pairs
+- **Domain:** Medicine & Medical Education
+- **Languages:** English
+- **License:** MIT
+
+ 
+## Uses
+
+### Direct Use
+
+- Primary use case: Medical Q&A for students, exam preparation, and knowledge review.
+- Suitable for interactive learning assistants or educational chatbots.
+- Not intended for real-world clinical decision-making or replacing professional medical advice.
+
+
+## Bias, Risks, and Limitations
+
+- The model’s knowledge is constrained to the dataset scope (flashcard-style Q&A).
+- Responses are short and exam-style rather than detailed clinical explanations.
+- Should not be relied upon for actual patient care, treatment decisions, or emergency use.
+
+## 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/Medical-QA")
+model = AutoModelForCausalLM.from_pretrained(
+    "khazarai/Medical-QA",
+    device_map={"": 0}
+)
+
+system = "Answer this question truthfully"
+
+question = """
+What can β-blockers cause or exacerbate due to excessive AV nodal inhibition?
+"""
+
+messages = [
+    {"role" : "system", "content" : system},
+    {"role" : "user",   "content" : question}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize = False,
+    add_generation_prompt = True, 
+    enable_thinking = False,
+)
+
+from transformers import TextStreamer
+_ = model.generate(
+    **tokenizer(text, return_tensors = "pt").to("cuda"),
+    max_new_tokens = 512,
+    temperature = 0.7,
+    top_p = 0.8,
+    top_k = 20,
+    streamer = TextStreamer(tokenizer, skip_prompt = True),
+)
+```
+
+
+## Training Details
+
+### Training Data
+
+The dataset is based on Anki Medical Curriculum flashcards, created and updated by medical students. 
+These flashcards cover the entire medical curriculum, including but not limited to:
+
+- Anatomy
+- Physiology
+- Pathology
+- Pharmacology
+- Clinical knowledge and skills
+
+The flashcards typically provide succinct summaries and mnemonics to support learning and retention.