Minime_base

MiniMe Base

A fine-tuned language model designed to serve as the language-model foundation for MiniMe, a personal AI assistant.

Model Details

Model Description

MiniMe Base is a fine-tuned causal language model adapted to act as the core language model for a personal AI assistant.

The model was trained to better understand and respond to instructions in the context of a personalized assistant, with an emphasis on conversational behavior, instruction following, reasoning, and assistant-style responses.

The model is intended to be used as the language-model layer within a larger MiniMe system, where additional components such as memory, retrieval, tools, agents, and external context can provide capabilities beyond the model itself.

• Developed by: Brijesh
• Model name: MiniMe Base
• Model type: Causal Language Model
• Language: English
• License: Apache 2.0
• Training platform: Kaggle
• Fine-tuned from: Qwen base model
• Fine-tuning method: Parameter-Efficient Fine-Tuning (PEFT/LoRA)
• Repository: brijeshah/Minime_base

Model Sources

• Hugging Face: brijeshah/Minime_base
• Training: Kaggle
• Base architecture: Qwen

For conversational use, the model's tokenizer/chat template should be used when supported by the underlying model.

Training Details

Training Data

The model was trained using a custom conversational and instruction-following dataset created for the development of MiniMe.

The training data was designed to teach the model assistant-oriented behavior, including:

• Following system instructions
• Understanding user requests
• Producing helpful responses
• Maintaining conversational context
• Reasoning through user tasks
• Responding in a personal-assistant style

Training examples were formatted using the tokenizer and the appropriate conversational format for the underlying base model.

Training Procedure

MiniMe Base was fine-tuned using parameter-efficient fine-tuning techniques.

LoRA/PEFT was used to adapt the pretrained model without updating the complete set of base-model parameters.

The model was trained using GPU compute provided through Kaggle.

Training Notebook

The complete training process, including dataset preparation, fine-tuning configuration, and model training, is documented in the accompanying Kaggle notebook.

Kaggle Training Notebook

Evaluation

Testing Data

The model was evaluated using examples representative of the conversational and instruction-following tasks used during MiniMe development.

Evaluation Factors

The evaluation focused on:

• Instruction following
• Assistant-style responses
• Conversational consistency
• Reasoning behavior
• Response relevance
• Task completion

Metrics

Because MiniMe Base is intended primarily for personalized conversational use, evaluation focuses on task-specific and qualitative behavior rather than a single benchmark score.

Future versions may include standardized LLM benchmarks and task-specific evaluations.

Results

MiniMe Base provides the language-model foundation for the MiniMe personal assistant.

Its performance should be evaluated within the complete MiniMe system, since capabilities such as memory, retrieval, tools, and external context are provided by components outside the base language model.

Technical Specifications

Model Architecture and Objective

MiniMe Base is based on a pretrained Qwen causal language model.

The objective of fine-tuning was to adapt the pretrained model toward personalized assistant behavior, instruction following, conversational interaction, and reasoning.

Parameter-efficient fine-tuning was used to adapt the model while keeping the majority of the original model parameters frozen.

Compute Infrastructure

Training was performed in the Kaggle notebook environment using GPU acceleration.

Hardware

• Platform: Kaggle
• GPU: Kaggle-provided GPU

Software

• Python
• PyTorch
• Hugging Face Transformers
• Hugging Face Datasets
• PEFT
• Unsloth
• Accelerate

APA:

Brijesh. (2026). MiniMe Base. Hugging Face Model Hub.

Glossary

MiniMe: A personal AI assistant being developed by Brijesh.

LLM: Large Language Model.

LoRA: Low-Rank Adaptation, a parameter-efficient fine-tuning technique.

PEFT: Parameter-Efficient Fine-Tuning.

RAG: Retrieval-Augmented Generation.

MCP: Model Context Protocol.

Base Model: The language-model foundation used by the MiniMe assistant.

More Information

MiniMe Base is part of the broader development of MiniMe, a personal AI assistant focused on combining a language model with personalization, memory, retrieval, reasoning, and tool use.

The model is intended to evolve alongside the MiniMe system as new capabilities and training data are introduced.

Model Card Authors

Brijesh

Model Card Contact

For questions, feedback, collaboration, or information about MiniMe, please visit My_Portfolio