Instructions to use aparnavirtuonai/mistral-medqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aparnavirtuonai/mistral-medqa with Transformers:

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

pipe = pipeline("text-generation", model="aparnavirtuonai/mistral-medqa")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("aparnavirtuonai/mistral-medqa")
model = AutoModelForCausalLM.from_pretrained("aparnavirtuonai/mistral-medqa")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use aparnavirtuonai/mistral-medqa with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/aparnavirtuonai/mistral-medqa

SGLang

How to use aparnavirtuonai/mistral-medqa 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 "aparnavirtuonai/mistral-medqa" \
    --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": "aparnavirtuonai/mistral-medqa",
		"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 "aparnavirtuonai/mistral-medqa" \
        --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": "aparnavirtuonai/mistral-medqa",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use aparnavirtuonai/mistral-medqa with Docker Model Runner:
```
docker model run hf.co/aparnavirtuonai/mistral-medqa
```

Mistral-MedQA (Medical QA Fine-Tuned Model)

Model Description

This model is a fine-tuned version of the base model:

mistralai/Mistral-7B-v0.1

The model has been fine-tuned on a medical question-answering dataset (MedQA) to improve its ability to answer medical queries and perform domain-specific reasoning.

Intended Use

Medical question answering
Healthcare-related chatbot systems
Educational and research purposes

Note: This model is not intended for real-world medical diagnosis without professional supervision.

Training Details

Base Model

mistralai/Mistral-7B-v0.1

Fine-Tuning Method

Supervised Fine-Tuning (SFT)
LoRA (Low-Rank Adaptation) using PEFT

Dataset

The model was fine-tuned on the MedQA dataset:

https://huggingface.co/datasets/openlifescienceai/medqa

Training Hyperparameters

Epochs: 3
Learning Rate: 2e-5
Batch Size: 2
Gradient Accumulation Steps: 8
Max Sequence Length: 512
LoRA Rank (r): 8
LoRA Alpha: 16
LoRA Dropout: 0.05

Note: Update these values if they differ from your actual training configuration.

Evaluation Results

Dataset	Accuracy
BoolQ (General QA)	0.70
PubMedQA (Medical QA)	0.69

The model maintains stable general performance and shows baseline domain adaptation. Further improvements are in progress.

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "aparnavirtuonai/mistral-medqa-final"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

prompt = "Question: What is diabetes?\nAnswer:"

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(**inputs, max_new_tokens=100)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloads last month: 4

Safetensors

Model size

7B params

Tensor type

F16

Model tree for aparnavirtuonai/mistral-medqa

Base model

mistralai/Mistral-7B-v0.1

Finetuned

(1032)

this model