Instructions to use iamaber/mistral-7b-pubmedqa-lora-plus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use iamaber/mistral-7b-pubmedqa-lora-plus with Transformers:

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

pipe = pipeline("text-generation", model="iamaber/mistral-7b-pubmedqa-lora-plus")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("iamaber/mistral-7b-pubmedqa-lora-plus")
model = AutoModelForCausalLM.from_pretrained("iamaber/mistral-7b-pubmedqa-lora-plus", device_map="auto")
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]:]))

PEFT
How to use iamaber/mistral-7b-pubmedqa-lora-plus with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use iamaber/mistral-7b-pubmedqa-lora-plus with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "iamaber/mistral-7b-pubmedqa-lora-plus"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "iamaber/mistral-7b-pubmedqa-lora-plus",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/iamaber/mistral-7b-pubmedqa-lora-plus

SGLang

How to use iamaber/mistral-7b-pubmedqa-lora-plus 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 "iamaber/mistral-7b-pubmedqa-lora-plus" \
    --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": "iamaber/mistral-7b-pubmedqa-lora-plus",
		"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 "iamaber/mistral-7b-pubmedqa-lora-plus" \
        --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": "iamaber/mistral-7b-pubmedqa-lora-plus",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use iamaber/mistral-7b-pubmedqa-lora-plus with Docker Model Runner:
```
docker model run hf.co/iamaber/mistral-7b-pubmedqa-lora-plus
```

pubmedqa-loraplus Model

Overview

This merged artifact fine-tunes mistralai/Mistral-7B-Instruct-v0.3 on qiaojin/PubMedQA / pqa_labeled using LoRA+.

Training Setup

Field	Value
Train examples	900
Eval examples	100
Epochs	3
Train batch size	4
Eval batch size	4
Gradient accumulation	4
Learning rate	5e-05
Best eval loss	0.5188
Latest eval loss	0.5188
Latest train loss	0.4810
Train runtime (s)	566.5676
Global step	171

Evaluation Summary

Metric	Value
PubMedQA accuracy	0.4500
PubMedQA macro F1	0.2069
PubMedQA weighted F1	0.2793
PubMedQA samples	100
Medical MMLU accuracy	0.1600
Medical MMLU samples	50

Medical MMLU Subject Breakdown

Subject	Accuracy	Correct	Total
anatomy	0.1600	8	50
clinical_knowledge	0.0000	0	0
college_medicine	0.0000	0	0
medical_genetics	0.0000	0	0
professional_medicine	0.0000	0	0
virology	0.0000	0	0

PubMedQA Confusion Matrix

Actual \ Predicted	yes	no	maybe
yes	45	0	0
no	40	0	0
maybe	15	0	0

Downloads last month: 5

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for iamaber/mistral-7b-pubmedqa-lora-plus

Base model

mistralai/Mistral-7B-v0.3

Finetuned

mistralai/Mistral-7B-Instruct-v0.3

Adapter

(852)

this model

iamaber
/

mistral-7b-pubmedqa-lora-plus