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)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("TroyDoesAI/MermaidMistral") model = AutoModelForCausalLM.from_pretrained("TroyDoesAI/MermaidMistral", device_map="auto") 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=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- 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
Commit History
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Verified signatureMerge of all my mistral models, after testing this model is the most easy to use, not the most capable but is really easy for people to prompt as it seems I am really good at prompting what I want because I wrote the dataset lol. Sorry guys. This one should be more aligned.bb5a277
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Verified signatureContinued training for 1 epoch with improving eval score, same dataset.64193d6
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Created a Google Colab Notebook and uploaded5e3daa2
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Adding an Example Prompt using TextGenerationWebUI to help users get the best output. always have response start with ```mermaid graph TB;
Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. ### Instruction: Create the Mermaid Diagram for the following code: ### Input: # Simple Calculator def add(x, y): return x + y def subtract(x, y): return x - y def multiply(x, y): return x * y def divide(x, y): if y != 0: return x / y else: return "Cannot divide by zero" def main(): num1 = float(input("Enter first number: ")) num2 = float(input("Enter second number: ")) operation = input("Enter operation (+, -, *, /): ") if operation == "+": result = add(num1, num2) elif operation == "-": result = subtract(num1, num2) elif operation == "*": result = multiply(num1, num2) elif operation == "/": result = divide(num1, num2) else: result = "Invalid operation" print(f"Result: {result}") ### Response: ```mermaid graph …339d45e -
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Added an additional epoch of 20 Unique Stories and Associated Hand crafted Flow Maps provided by the open source community. Thank you for your participation.c1002a1 -
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Added Photos of generations and link to implement mermaid js diagrams to images.14b5793 -
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### 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 effor…d6b12a7 -
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