GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
A-Kishore
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Run this model inference with full control and performance in your environment.
Model Details
Model Provider
A-Kishore
Model Tree
unsloth/Phi-3-mini-4k-instruct-bnb-4bit
Input Modalities
Output Modalities
Supported Functionality
This checkpoint was trained to answer medical QA prompts in an instruction-following format. The training workflow uses the unsloth stack together with transformers, peft, trl, bitsandbytes, and datasets.
The dataset used in the notebook is medalpaca/medical_meadow_medqa, and the examples are formatted into a system/user/assistant prompt structure for supervised finetuning.
This model is intended for educational and research purposes, and for prototyping medical QA assistants. It should not be used as a substitute for clinical judgment, diagnosis, or treatment recommendations.
Evaluation was run on 1,018 examples with ROUGE metrics.
| Metric | Score |
|---|---|
| ROUGE-1 | 0.6212 |
| ROUGE-2 | 0.5815 |
| ROUGE-L | 0.6195 |
python
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline model_id = "A-Kishore/phi_medical_qa_finetune_16bit" tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id) pipe = pipeline("question-answering", model=model, tokenizer=tokenizer) prompt = "You are a medical AI. Answer the question clearly and concisely.\n\nQuestion: What is the most likely diagnosis for a patient with fever, rash, and migratory arthritis?"print(pipe(prompt, max_new_tokens=128, do_sample=False)[0]["generated_text"])The model can produce incorrect or overconfident answers, especially for ambiguous or poorly specified prompts. Review outputs carefully, and do not rely on this model for real-world medical decisions without qualified human oversight.