Mermaid-Llama-3-8B

Introducing Mermaid-LLama-3-8B, a robust language model designed for Python code understanding and crafting captivating story flow maps.

Key Features

Code Understanding:
- Masters Python intricacies with finesse.
- Generates clear and accurate Mermaid Diagram Flow Charts.
- Ideal for developers seeking visual representations of their code logic.
Storytelling Capabilities:
- Converts narrative inputs into captivating Mermaid Diagrams.
- Maps character interactions, plot developments, and narrative arcs.
Unmatched Performance:
- Surpasses GPT-4 in generating well-organized Mermaid Diagrams.
Training Insights:
- Trained on a diverse dataset, including 800 unique, hand-curated Mermaid Graph examples utilizing 478 complete Python programs.
- Exhibits emergent properties in story-to-flow map translations and step-by-step instruction flow maps.

Collaboration

Interested in enhancing Mermaid's capabilities? Contact troydoesai@gmail.com for collaboration opportunities.

Example Use Cases

Retrieval-Augmented Generation (RAG): Utilize Mermaid-LLama-3-8B to create condensed knowledge graphs. This model excels in generating flow diagrams that enhance the retrieval process. These knowledge graphs are stored in a vector database, which allows for quick and efficient retrieval of contextually relevant information. When a query is received, the system retrieves a pertinent knowledge graph, appending it as context to the model. This enriched context enables Mermaid-LLama-3-8B to deliver more accurate and nuanced responses. This approach is particularly beneficial in applications requiring deep, context-aware interactions, such as sophisticated Q&A systems, dynamic data analysis, and complex decision-making tasks.
Code Documentation: Automatic visual flow charts from Python code.
Storyboarding: Visually appealing diagrams for storytelling.
Project Planning: Visual project flow maps for effective team communication.
Learning Python: Helps students visually understand Python code structures.
Game Design: Visualizing game storylines for coherent narrative structure.

Proof of Concept

Stay tuned for the release of the VSCode Extension that displays the Live Flow Map every time a user stops typing for more than 10 seconds.

Training Specifications

LoRA Rank: 2048
LoRA Alpha: 4096
Batch Size: 1
Micro Batch Size: 1
Cutoff Length: 4096
Save every n steps: 1000
Epochs: 3
Learning Rate: 1e-6
LR Scheduler: Cosine

Target Modules:

Enable q_proj
Enable v_proj
Enable k_proj
Enable o_proj
Enable gate_proj
Enable down_proj
Enable up_proj

Getting Started

Start by downloading one of my models.

Load the model.

Use my prompt template to generate a Mermaid code block, which can be viewed in the Mermaid Live Editor or using the Mermaid CLI tool.

Here we open the VLLM GUI Program while still running in Vram the Mermaid-Llama-8B to compare the flow diagram to the actual program and show the lightweight capabilites of small models on consumer hardware.

More on my VLLM Class and inference GUI : https://github.com/Troys-Code/VLLM

Note: This model should be treated as an Auto-Complete Model, Do not try talking to it in chat you are gonna get garbage, those layers have been pruned and replaced, that is all you will hear of my secret sauce on training on small < 1000 entry datasets.