Instructions to use Ozymandias2/qwen3-4b-instruct-stat-qlora-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ozymandias2/qwen3-4b-instruct-stat-qlora-v2 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B-Instruct-2507")
model = PeftModel.from_pretrained(base_model, "Ozymandias2/qwen3-4b-instruct-stat-qlora-v2")

Transformers

How to use Ozymandias2/qwen3-4b-instruct-stat-qlora-v2 with Transformers:

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

pipe = pipeline("text-generation", model="Ozymandias2/qwen3-4b-instruct-stat-qlora-v2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Ozymandias2/qwen3-4b-instruct-stat-qlora-v2", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Ozymandias2/qwen3-4b-instruct-stat-qlora-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Ozymandias2/qwen3-4b-instruct-stat-qlora-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ozymandias2/qwen3-4b-instruct-stat-qlora-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Ozymandias2/qwen3-4b-instruct-stat-qlora-v2

SGLang

How to use Ozymandias2/qwen3-4b-instruct-stat-qlora-v2 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 "Ozymandias2/qwen3-4b-instruct-stat-qlora-v2" \
    --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": "Ozymandias2/qwen3-4b-instruct-stat-qlora-v2",
		"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 "Ozymandias2/qwen3-4b-instruct-stat-qlora-v2" \
        --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": "Ozymandias2/qwen3-4b-instruct-stat-qlora-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Ozymandias2/qwen3-4b-instruct-stat-qlora-v2 with Docker Model Runner:
```
docker model run hf.co/Ozymandias2/qwen3-4b-instruct-stat-qlora-v2
```

Model Card for qwen3-4b-instruct-stat-qlora-v2

This model is a QLoRA fine-tuned variant of Qwen3-4B-Instruct, specialized for explaining statistical test pipeline outputs in a clear, structured, and technically correct way. It is designed to transform structured tool_json outputs (e.g. T-Test, ANOVA, correlation, clustering, chi-square results) into high-quality natural language explanations following a strict five-section analytical format. Its' goal is to serve my personal project app available on spaces. The fine-tuning process focused on reducing hallucinations, improving methodological correctness, and strengthening interpretability, while preserving the general instruction-following abilities of the base model.

Model Details

Model Description

Developed by: João Vaz, Independent research project
Shared by: Ozymandias2
Model type: Instruction-tuned causal language model with LoRA adapters
Language(s) (NLP): English
License: Same as base model (Qwen/Qwen3-4B-Instruct-2507)
Finetuned from model: Qwen/Qwen3-4B-Instruct-2507

This model was trained to generate structured statistical explanations using the following fixed template: -Missing Data Analysis -Pre-Test Diagnostics -Test Selection Rationale -Test Results -Interpretation

The model explicitly avoids: -causal language for observational analyses, -hallucinated preprocessing steps, -incorrect test naming or directionality.

Model Sources

Repository: https://github.com/JoaoLAVaz/data-chat-assistant/tree/v1/result_explainer_study
Paper : https://github.com/JoaoLAVaz/data-chat-assistant/blob/v1/result_explainer_study/README.md
Demo (spaces, feel free to restart and test it): https://huggingface.co/spaces/Ozymandias2/data-chat-assistant

Framework versions

PEFT 0.18.0
Transformers ≥ 4.40

Downloads last month: 3

Model tree for Ozymandias2/qwen3-4b-instruct-stat-qlora-v2

Base model

Qwen/Qwen3-4B-Instruct-2507

Adapter

(5498)

this model