Instructions to use nuriyev/text2mcdm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nuriyev/text2mcdm with Transformers:

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

pipe = pipeline("text-generation", model="nuriyev/text2mcdm")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nuriyev/text2mcdm")
model = AutoModelForCausalLM.from_pretrained("nuriyev/text2mcdm")
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 nuriyev/text2mcdm with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nuriyev/text2mcdm"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nuriyev/text2mcdm",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nuriyev/text2mcdm

SGLang

How to use nuriyev/text2mcdm 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 "nuriyev/text2mcdm" \
    --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": "nuriyev/text2mcdm",
		"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 "nuriyev/text2mcdm" \
        --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": "nuriyev/text2mcdm",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use nuriyev/text2mcdm 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 nuriyev/text2mcdm 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 nuriyev/text2mcdm to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for nuriyev/text2mcdm to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="nuriyev/text2mcdm",
    max_seq_length=2048,
)

Docker Model Runner
How to use nuriyev/text2mcdm with Docker Model Runner:
```
docker model run hf.co/nuriyev/text2mcdm
```

Model Description

This model extracts structured Z-number decision matrices from conversational text describing multi-criteria decision problems. Given a natural language narrative about alternatives, criteria, and preferences (often messy, subjective, or contradictory), the model outputs a markdown table with:

Alternatives (e.g., train, flight, driving)
Criteria (e.g., cost, comfort, reliability)
Z-number ratings in value:confidence format (e.g., 4:3 = good rating with moderate confidence)

Z-numbers extend traditional fuzzy numbers by incorporating reliability/confidence, making them ideal for real-world decision-making under uncertainty.

Intended Use

The extracted matrix can be analyzed using Z-number-based MCDM methods (TOPSIS, PROMETHEE) to produce ranked alternatives. See text2mcdm for the full pipeline.

Training

Base model: Qwen/Qwen3-4B-Instruct-2507
Method: LoRA fine-tuning with Unsloth
Data: nuriyev/text2mcdm (~600 synthetic decision narratives generated via Gemini API)

Downloads last month: 5

Safetensors

Model size

4B params

Tensor type

BF16

Space using nuriyev/text2mcdm 1

Collection including nuriyev/text2mcdm

Text2MCDM

Collection

Natural language to Z-number MCDM pipeline • 4 items • Updated Dec 21, 2025