Instructions to use TroyDoesAI/MermaidMistral with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TroyDoesAI/MermaidMistral with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="TroyDoesAI/MermaidMistral") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("TroyDoesAI/MermaidMistral") model = AutoModelForCausalLM.from_pretrained("TroyDoesAI/MermaidMistral") 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 TroyDoesAI/MermaidMistral with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "TroyDoesAI/MermaidMistral" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TroyDoesAI/MermaidMistral", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/TroyDoesAI/MermaidMistral
- SGLang
How to use TroyDoesAI/MermaidMistral 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 "TroyDoesAI/MermaidMistral" \ --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": "TroyDoesAI/MermaidMistral", "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 "TroyDoesAI/MermaidMistral" \ --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": "TroyDoesAI/MermaidMistral", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use TroyDoesAI/MermaidMistral with Docker Model Runner:
docker model run hf.co/TroyDoesAI/MermaidMistral
Posted here first: https://www.reddit.com/r/Oobabooga/comments/192qb2c/mermaidmistral_a_work_in_progress_model_for_flow/
It's kinda barely under 16GB in full precision,
you can give it a try using
I threw this together but be warned it will OOM on long context due to Free Tier being limited to 15GB VRAM.
Introduction:
Introducing MermaidMistral, a powerful yet compact 7-billion-parameter language model adept at Python code understanding and crafting engaging story flow maps. Trained on a meticulously hand curated dataset of 478 diverse Python examples and hand crafted mermaid flow maps utilizing https://mermaid.live, this model goes beyond its size to deliver exceptional performance in code understanding and story visualization.
Key Features:
MermaidMistral is not a "Chatty Kathy" and should only respond with a mermaid code block with a flow diagram in mermaid js syntax and nothing more.
1. Code Understanding:
- Grasps Python intricacies with finesse.
- Generates clear and accurate Mermaid Diagram Flow Charts.
- Ideal for developers seeking visual representations of their code's logic.
2. Storytelling Capabilities:
- Converts narrative inputs into captivating Mermaid Diagrams.
- Maps character interactions, plot developments, and narrative arcs effortlessly.
3. Unmatched Performance:
- Surpasses larger models, like GPT-4, in generating well-organized and detailed Mermaid Diagrams for story flows.
4. Training Insights:
- Trained on a 478 Python examples for just under three epochs on a single RTX 3090 following batch size equal to 1, known as stochastic gradient descent.
- Exhibited emergent properties in story-to-flow map translations.
- Adaptable and efficient in resource utilization
- Due to hardware constraints this fine tune has a token limit of 2048.
Collaboration:
MermaidMistral is open to collaboration to further strengthen its capabilities. The dataset, formatted in Alpaca, provides a unique foundation for understanding Python intricacies. If you're interested in contributing or collaborating to enhance the model's performance, feel free to reach out to troydoesai@gmail.com. Your expertise could play a pivotal role in refining MermaidMistral.
Example Use Cases:
1. Code Documentation:
- Developers can use MermaidMistral to automatically generate visual flow charts from their Python code, aiding in documentation and code understanding.
2. Storyboarding:
- Storytellers and writers can input their narrative and receive visually appealing Mermaid Diagrams, offering a structured overview of character interactions and plot progression.
3. Project Planning:
- Project managers can leverage MermaidMistral to create visual project flow maps, facilitating effective communication and planning among team members.
4. Learning Python:
- Students and beginners can use MermaidMistral to visually understand Python code structures, enhancing their learning experience.
5. Game Design:
- Game developers can utilize MermaidMistral for visualizing game storylines, ensuring a coherent narrative structure and character development.
Proof of Concept:
MermaidMistral proves that innovation thrives in compact packages, delivering exceptional performance across diverse applications. Its adaptability and efficiency showcase the potential for groundbreaking results even in resource-constrained environments.
These mermaid codeblocks can be converted directly into images using mermaid cli tool found here: https://github.com/mermaid-js/mermaid-cli
I plan to release my working proof of concept VSCode Extension that currently displays the Live Flow Map every time a user stops typing for more than 10 seconds.
Stay tuned.
Example Story -> Flow
https://chat.openai.com/share/e3163857-981b-4968-b2db-98ad869c9259
Insights on how to get best results
For best results use full precision using one of the 3 different instruction types:
- "instruction": "Create the mermaid diagram for the following code:",
- "instruction": "Create the mermaid diagram for the following story:",
- "instruction": "Create the mermaid diagram for the following:",
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docker model run hf.co/TroyDoesAI/MermaidMistral