Instructions to use Kartikey1999/medical-qa-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Kartikey1999/medical-qa-lora with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct")
model = PeftModel.from_pretrained(base_model, "Kartikey1999/medical-qa-lora")

Transformers

How to use Kartikey1999/medical-qa-lora with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Kartikey1999/medical-qa-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Kartikey1999/medical-qa-lora", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Kartikey1999/medical-qa-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Kartikey1999/medical-qa-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kartikey1999/medical-qa-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Kartikey1999/medical-qa-lora

SGLang

How to use Kartikey1999/medical-qa-lora with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Kartikey1999/medical-qa-lora" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kartikey1999/medical-qa-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Kartikey1999/medical-qa-lora" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kartikey1999/medical-qa-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Kartikey1999/medical-qa-lora with Docker Model Runner:
```
docker model run hf.co/Kartikey1999/medical-qa-lora
```

Medical QA LoRA

Model Description

This project fine-tunes Qwen2.5-1.5B-Instruct on medical educational text using LoRA (Low-Rank Adaptation) and supervised fine-tuning (SFT).

The goal is to adapt the language model to answer questions based on medical educational material while training only a small percentage of the model parameters.

Base Model

Qwen/Qwen2.5-1.5B-Instruct

Fine-Tuning Method

PEFT
LoRA (Low-Rank Adaptation)
Supervised Fine-Tuning (SFT)
4-bit quantization

Dataset

Medical educational reference material was processed and formatted into text samples for fine-tuning.

Training samples: 49
Validation samples: 6

Training

The model was fine-tuned using Hugging Face Transformers, TRL and PEFT.

LoRA was used to reduce the number of trainable parameters and memory requirements.

Evaluation Results

Training Loss: 2.1675
Validation Loss: 2.0194
Mean Token Accuracy: 0.5213
Evaluation Entropy: 2.0662

Intended Use

This model is intended for educational experimentation with LLM fine-tuning and medical question answering.

It should not be used as a substitute for professional medical advice, diagnosis or treatment.

Technologies

Python, PyTorch, Hugging Face Transformers, PEFT, LoRA, TRL, BitsAndBytes and Google Colab.

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Model tree for Kartikey1999/medical-qa-lora

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-1.5B-Instruct

Adapter

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