license: mit
base_model: microsoft/Phi-3-mini-128k-instruct
library_name: adapters
datasets:
- awels/druidai_admin_dataset
language:
- en
widget:
- text: Who are you, Merlin ?
tags:
- awels
- druidai
Merlin Model Card
Model Details
Model Name: Merlin
Model Type: Transformer-based leveraging Microsoft Phi 3b 128k tokens
Publisher: Awels Engineering
License: MIT
Model Description: Merlin is a sophisticated model designed to help as an AI agent focusing on the Druid AI Conversational platform. It leverages advanced machine learning techniques to provide efficient and accurate solutions. It has been trained on the full docments corpus of Druid 7.14.
Dataset
Dataset Name: awels/druidai_admin_dataset
Dataset Source: Hugging Face Datasets
Dataset License: MIT
Dataset Description: The dataset used to train Merlin consists of all the public documents available on the Druid AI Conversational Platform. This dataset is curated to ensure a comprehensive representation of typical administrative and development scenarios encountered in Druid AI Platform.
Training Details
Training Data: The training data includes 33,000 Questions and Answers generated by the Bonito LLM. The dataset is split into 3 sets of data (training, test and validation) to ensure robust model performance.
Training Procedure: Thready was trained using supervised learning with cross-entropy loss and the Adam optimizer. The training involved 1 epoch, a batch size of 4, a learning rate of 5.0e-06, and a cosine learning rate scheduler with gradient checkpointing for memory efficiency.
Hardware: The model was trained on a single NVIDIA RTX 4090 graphic card.
Framework: The training was conducted using PyTorch.
Evaluation
Evaluation Metrics: Thready was evaluated on the training dataset:
epoch = 1.0 total_flos = 33926962GF train_loss = 2.8776 train_runtime = 0:19:34.86 train_samples_per_second = 21.546 train_steps_per_second = 5.387
Performance: The model achieved the following results on the evaluation dataset:
epoch = 1.0 eval_loss = 2.3814 eval_runtime = 0:01:04.90 eval_samples = 5298 eval_samples_per_second = 98.718 eval_steps_per_second = 24.683
Intended Use
Primary Use Case: Merlin is intended to be used locally in an agent swarm to colleborate together to solve Druid AI Conversational platform related problems.
Limitations: While Merlin is highly effective, it may have limitations due to the model size. An 8b model based on Llama 3 is used internally at Awels Engineering.