Instructions to use Anudeeprao1/Qwen3VL-2B-StatBot_Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Anudeeprao1/Qwen3VL-2B-StatBot_Instruct with Transformers:

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

pipe = pipeline("image-text-to-text", model="Anudeeprao1/Qwen3VL-2B-StatBot_Instruct")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Anudeeprao1/Qwen3VL-2B-StatBot_Instruct", dtype="auto")

llama-cpp-python

How to use Anudeeprao1/Qwen3VL-2B-StatBot_Instruct with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Anudeeprao1/Qwen3VL-2B-StatBot_Instruct",
	filename="Qwen3VL-2B-StatBot-Q8_0.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": [
				{
					"type": "text",
					"text": "Describe this image in one sentence."
				},
				{
					"type": "image_url",
					"image_url": {
						"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Anudeeprao1/Qwen3VL-2B-StatBot_Instruct with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Anudeeprao1/Qwen3VL-2B-StatBot_Instruct:Q8_0
# Run inference directly in the terminal:
llama-cli -hf Anudeeprao1/Qwen3VL-2B-StatBot_Instruct:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Anudeeprao1/Qwen3VL-2B-StatBot_Instruct:Q8_0
# Run inference directly in the terminal:
llama-cli -hf Anudeeprao1/Qwen3VL-2B-StatBot_Instruct:Q8_0

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Anudeeprao1/Qwen3VL-2B-StatBot_Instruct:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf Anudeeprao1/Qwen3VL-2B-StatBot_Instruct:Q8_0

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Anudeeprao1/Qwen3VL-2B-StatBot_Instruct:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Anudeeprao1/Qwen3VL-2B-StatBot_Instruct:Q8_0

Use Docker

docker model run hf.co/Anudeeprao1/Qwen3VL-2B-StatBot_Instruct:Q8_0

LM Studio
Jan

vLLM

How to use Anudeeprao1/Qwen3VL-2B-StatBot_Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Anudeeprao1/Qwen3VL-2B-StatBot_Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Anudeeprao1/Qwen3VL-2B-StatBot_Instruct",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/Anudeeprao1/Qwen3VL-2B-StatBot_Instruct:Q8_0

SGLang

How to use Anudeeprao1/Qwen3VL-2B-StatBot_Instruct 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 "Anudeeprao1/Qwen3VL-2B-StatBot_Instruct" \
    --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": "Anudeeprao1/Qwen3VL-2B-StatBot_Instruct",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "Anudeeprao1/Qwen3VL-2B-StatBot_Instruct" \
        --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": "Anudeeprao1/Qwen3VL-2B-StatBot_Instruct",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Ollama
How to use Anudeeprao1/Qwen3VL-2B-StatBot_Instruct with Ollama:
```
ollama run hf.co/Anudeeprao1/Qwen3VL-2B-StatBot_Instruct:Q8_0
```

Unsloth Studio new

How to use Anudeeprao1/Qwen3VL-2B-StatBot_Instruct with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Anudeeprao1/Qwen3VL-2B-StatBot_Instruct to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Anudeeprao1/Qwen3VL-2B-StatBot_Instruct to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Anudeeprao1/Qwen3VL-2B-StatBot_Instruct to start chatting

Pi new

How to use Anudeeprao1/Qwen3VL-2B-StatBot_Instruct with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Anudeeprao1/Qwen3VL-2B-StatBot_Instruct:Q8_0

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Anudeeprao1/Qwen3VL-2B-StatBot_Instruct:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Anudeeprao1/Qwen3VL-2B-StatBot_Instruct with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Anudeeprao1/Qwen3VL-2B-StatBot_Instruct:Q8_0

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Anudeeprao1/Qwen3VL-2B-StatBot_Instruct:Q8_0

Run Hermes

hermes

Docker Model Runner
How to use Anudeeprao1/Qwen3VL-2B-StatBot_Instruct with Docker Model Runner:
```
docker model run hf.co/Anudeeprao1/Qwen3VL-2B-StatBot_Instruct:Q8_0
```

Lemonade

How to use Anudeeprao1/Qwen3VL-2B-StatBot_Instruct with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Anudeeprao1/Qwen3VL-2B-StatBot_Instruct:Q8_0

Run and chat with the model

lemonade run user.Qwen3VL-2B-StatBot_Instruct-Q8_0

List all available models

lemonade list

📊 StatBot — Qwen3-VL-2B Statistics Tutor for High School Students

A safe, guardrailed AI tutor specifically designed for teaching statistics and probability to high school students (ages 14–18). Built for the Oklahoma Department of Education project. Runs offline on phones via PocketPal.

🎯 What This Model Does

StatBot is a fine-tuned vision-language model that:

📊 Teaches statistics — mean, median, distributions, hypothesis testing, probability, regression, and more
🖼️ Reads images — graphs, charts, histograms, textbook pages, handwritten formulas
🛡️ Refuses unsafe content — off-topic questions, jailbreaks, inappropriate requests
💙 Supports students in distress — empathy + counselor referral
🎓 Uses hint-first teaching — guides students rather than just giving answers

📁 Files in This Repo

File	Size	What It Is
`Qwen3VL-2B-StatBot-Q8_0.gguf`	1.71 GB	Main model (Q8_0 quantized)
`Qwen3VL-2B-StatBot-mmproj.gguf`	782 MB	Vision projector (needed for images)

⚠️ Both files required for full image + text capability!

📱 How to Use on Your Phone (PocketPal)

Install PocketPal from your app store
Open PocketPal → Models → Search HuggingFace
Search: Anudeeprao1/Qwen3VL-2B-StatBot_Instruct
Download BOTH files:
- ✅ Qwen3VL-2B-StatBot-Q8_0.gguf
- ✅ Qwen3VL-2B-StatBot-mmproj.gguf
Tap Load Model
Start asking statistics questions or upload a graph!

💻 How to Use with Ollama

ollama run hf.co/Anudeeprao1/Qwen3VL-2B-StatBot_Instruct

💻 How to Use with llama.cpp

llama-server -hf Anudeeprao1/Qwen3VL-2B-StatBot_Instruct

Then open http://localhost:8080 in browser.

💡 Example Questions

Text Only

"What is the difference between mean and median?"
"Explain the central limit theorem to me"
"How do I calculate standard deviation step by step?"
"What is a p-value?"

With Images

Upload a histogram → "What type of distribution is this?"
Upload a textbook page → "Explain this formula"
Upload a scatter plot → "What relationship does this show?"
Upload a bar chart → "Describe this data"

🛡️ Safety Features

StatBot is designed with 7 categories of guardrails:

Category	Behavior
Off-topic questions	Refuses politely, redirects to stats
Inappropriate content	Refuses firmly
Identity probing	Deflects, never reveals internals
Jailbreak attempts	Resists prompt injection
Student distress	Shows empathy, refers to counselor
Privacy questions	Confirms FERPA + COPPA design
Educational audits	Provides compliance info

⚙️ Training Details

Parameter	Value
Base model	unsloth/Qwen3-VL-2B-Instruct
Fine-tuning method	LoRA SFT (Unsloth)
LoRA rank	16
Quantization	Q8_0 GGUF
Training samples	7,202
Guardrail samples	720 (7 categories × 3 repetitions)
Epochs	3
Learning rate	2e-4 (cosine schedule)
Effective batch size	16
Max sequence length	2048
Hardware	NVIDIA RTX 4500 Ada (24 GB)
Framework	Unsloth + TRL + Transformers

📊 Performance

Final training loss: ~0.09
Final validation loss: ~0.09
Perplexity: ~1.1
No overfitting detected (train ≈ val loss)
Vision capability: Full multimodal support via mmproj

🎓 Intended Audience

✅ High school statistics students (ages 14–18)
✅ Oklahoma high school teachers (classroom aid)
✅ Statistics tutors needing safe AI assistance
✅ Educational researchers studying AI guardrails
❌ NOT for general-purpose chat (refuses non-stats)
❌ NOT for elementary/middle school content

🏛️ Compliance & Safety

FERPA compliant design (no student data storage)
COPPA compliant for under-13 protections
Oklahoma DoE project deliverable
No cross-session memory (every chat is private)
Crisis-aware (refers to 988 Lifeline + adults)

📚 Try a Sample Conversation

User: "What is standard deviation?"

StatBot: "Great question! 🎯 Standard deviation tells us how spread out data is from the average...

Think of test scores: if everyone scored close to the average, SD is small. If scores are all over the place, SD is large.

Formula: σ = √(Σ(x - μ)² / N)

Want me to walk through an example?"

User: "What is the capital of France?"

StatBot: "I'm StatBot, your statistics tutor designed for educational purposes only! I can only help with high school statistics topics. Try asking me about mean, median, probability, or graphs! Have a good day. 📊"

⚠️ Limitations

Only English language
Only statistics/probability topics
Best for high school level (ages 14–18)
Requires both GGUF files for image input
Q8_0 quantization (high quality but ~2 GB)

📄 License

Apache 2.0 — Free for educational and research use.

👤 Author

Anudeep — Oklahoma Department of Education AI Tutoring Project

For questions or collaboration: open a discussion on this repo!

🏷️ Citation

If you use this model in research, please cite:

@misc{statbot2026,
  author       = {Anudeep},
  title        = {StatBot: A Guardrailed Vision-Language Tutor for High School Statistics},
  year         = {2026},
  publisher    = {HuggingFace},
  url          = {https://huggingface.co/Anudeeprao1/Qwen3VL-2B-StatBot_Instruct}
}

🙏 Acknowledgments

Unsloth — for the 2x faster fine-tuning framework
Qwen team — for the excellent base VL model
Oklahoma DoE — for funding this educational AI research
PocketPal — for offline mobile inference support

Downloads last month: 93

GGUF

Model size

2B params

Architecture

qwen3vl

Hardware compatibility

8-bit

Model tree for Anudeeprao1/Qwen3VL-2B-StatBot_Instruct

Base model

Qwen/Qwen3-VL-2B-Instruct

Finetuned

unsloth/Qwen3-VL-2B-Instruct

Quantized

(2)

this model