Instructions to use Azandra98/mermaid-diagram-generator-qwen2.5-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Azandra98/mermaid-diagram-generator-qwen2.5-7b with Transformers:

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

pipe = pipeline("text-generation", model="Azandra98/mermaid-diagram-generator-qwen2.5-7b")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Azandra98/mermaid-diagram-generator-qwen2.5-7b", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Azandra98/mermaid-diagram-generator-qwen2.5-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Azandra98/mermaid-diagram-generator-qwen2.5-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Azandra98/mermaid-diagram-generator-qwen2.5-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Azandra98/mermaid-diagram-generator-qwen2.5-7b

SGLang

How to use Azandra98/mermaid-diagram-generator-qwen2.5-7b 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 "Azandra98/mermaid-diagram-generator-qwen2.5-7b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Azandra98/mermaid-diagram-generator-qwen2.5-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "Azandra98/mermaid-diagram-generator-qwen2.5-7b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Azandra98/mermaid-diagram-generator-qwen2.5-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio

How to use Azandra98/mermaid-diagram-generator-qwen2.5-7b 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 Azandra98/mermaid-diagram-generator-qwen2.5-7b 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 Azandra98/mermaid-diagram-generator-qwen2.5-7b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Azandra98/mermaid-diagram-generator-qwen2.5-7b to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Azandra98/mermaid-diagram-generator-qwen2.5-7b",
    max_seq_length=2048,
)

Docker Model Runner
How to use Azandra98/mermaid-diagram-generator-qwen2.5-7b with Docker Model Runner:
```
docker model run hf.co/Azandra98/mermaid-diagram-generator-qwen2.5-7b
```

Mermaid Diagram Generator - Qwen2.5-Coder-7B

This model generates valid Mermaid diagram code from natural language descriptions.

Model Details

Base Model: Qwen2.5-Coder-7B
Fine-tuned on: Custom Mermaid diagram dataset
Training Time: ~30 minutes
Training Cost: ~$0.25
Final Loss: 0.2934
Framework: Unsloth + LoRA

Supported Diagram Types

✅ Flowcharts
✅ Sequence Diagrams
✅ Class Diagrams
✅ State Diagrams
✅ ER Diagrams

Usage

from unsloth import FastLanguageModel

# Load model
model, tokenizer = FastLanguageModel.from_pretrained(
    "Azandra98/mermaid-diagram-generator-qwen2.5-7b",
    max_seq_length=2048,
    dtype=None,
    load_in_4bit=True,
)
FastLanguageModel.for_inference(model)

# Generate diagram
def generate_mermaid(instruction):
    prompt = f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{instruction}. Generate only valid Mermaid code.

### Response:
"""
    
    inputs = tokenizer([prompt], return_tensors="pt").to("cuda")
    
    outputs = model.generate(
        **inputs,
        max_new_tokens=280,
        temperature=0.45,
        top_p=0.9,
        repetition_penalty=1.15,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id,
        eos_token_id=tokenizer.eos_token_id,
    )
    
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
diagram = generate_mermaid("flowchart for user login process")
print(diagram)

Example Outputs

Flowchart

Prompt: "simple flowchart showing user login process"

graph TD
A[Start] --> B[Enter Username]
B --> C[Enter Password]
C --> D[Try to Login]
D -->|Success| E[Redirect Home Page]
D -->|Failure| F[Show Error Message]
F --> G[End]

Sequence Diagram

Prompt: "sequence diagram for placing an online order"

sequenceDiagram
participant Customer
participant Website
participant Payment Gateway
Customer ->> Website: Browse Products
Website -->> Customer: Product Details
Customer ->> Website: Add to Cart
Customer ->> Website: Proceed to Checkout
Website ->> Payment Gateway: Process Payment
Payment Gateway -->> Website: Payment Confirmation
Website -->> Customer: Order Placed Successfully

Training Details

Method: LoRA (Low-Rank Adaptation)
Rank: 16
Alpha: 16
Epochs: 3
Batch Size: 2
Learning Rate: 2e-4
Optimizer: AdamW (8-bit)

Limitations

Best for simple to medium complexity diagrams
May require post-processing for very complex diagrams
Output should be validated at mermaid.live

License

Apache 2.0

Citation

@misc{mermaid-generator-qwen-2024,
  author = {Azandra98},
  title = {Mermaid Diagram Generator based on Qwen2.5-Coder-7B},
  year = {2024},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/Azandra98/mermaid-diagram-generator-qwen2.5-7b}}
}

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