Minime-it

MiniMe IT

MiniMe IT is the instruction-tuned version of MiniMe Base, developed as part of MiniMe, a personal AI assistant.

The model is fine-tuned to improve conversational interaction, instruction following, reasoning, and assistant-style responses.

Model Details

Model Description

MiniMe IT is built on top of the MiniMe Base model and further fine-tuned for instruction-following and conversational use.

It is designed to serve as the conversational language-model component of MiniMe, with the goal of providing a more capable and natural personal assistant experience.

• Developed by: Brijesh
• Model name: MiniMe IT
• Model type: Causal Language Model
• Language: English
• License: Apache 2.0
• Base model: MiniMe Base
• Model repository: brijeshah/Minime-it
• Model family: Qwen 3.5
• Fine-tuning: Instruction tuning
• Training platform: Kaggle

Model Sources

• Instruction-tuned model: brijeshah/Minime-it
• Base model: brijeshah/Minime_base
• Training notebook: Kaggle Training Notebook

Intended Use

Direct Use

MiniMe IT is intended for conversational and assistant-oriented applications, including:

• Personal AI assistants
• Instruction following
• Conversational interaction
• Question answering
• Reasoning and task assistance
• General text generation
• Local LLM applications
• AI assistant experimentation

The model is specifically intended to provide the conversational layer of the MiniMe personal AI assistant.

Downstream Use

MiniMe IT can be integrated into systems containing:

• Personal memory
• Long-term and short-term context
• Retrieval-Augmented Generation (RAG)
• Tool calling
• AI agents
• MCP integrations
• External APIs
• Personal knowledge bases
• Local inference servers

The model can be combined with these components to build a more capable personal AI assistant.

Out-of-Scope Use

The model should not be used as the sole decision maker for:

• Medical decisions
• Legal decisions
• Financial decisions
• Safety-critical applications
• High-impact autonomous decisions
• Applications requiring guaranteed factual accuracy

Model outputs should be validated when accuracy is important.

MiniMe Base provides the foundation, while MiniMe IT is further adapted for conversational and instruction-following behavior.

For information about the original base model and its training, see:

MiniMe Base Model Card

Bias, Risks, and Limitations

MiniMe IT inherits limitations from its underlying Qwen model, MiniMe Base, and instruction-tuning data.

Potential limitations include:

• Hallucinated or factually incorrect responses
• Inconsistent reasoning on complex tasks
• Sensitivity to prompt formulation
• Limited knowledge of information outside its training data
• Potential biases inherited from the underlying models and training data
• Reduced performance on tasks outside the fine-tuning distribution

The model does not inherently have access to current information unless connected to external tools or retrieval systems.

Recommendations

For applications requiring current or highly accurate information, use MiniMe IT together with:

• Retrieval-Augmented Generation
• External knowledge sources
• Tool calling
• Output validation
• Human review where appropriate

How to Get Started

The model can be loaded using Hugging Face Transformers:

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "brijeshah/Minime-it"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto"
)

messages = [
    {
        "role": "user",
        "content": "Hello MiniMe, introduce yourself."
    }
]

inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)

outputs = model.generate(
    inputs,
    max_new_tokens=256
)

response = tokenizer.decode(
    outputs[0],
    skip_special_tokens=True
)

print(response)

GGUF Version

A quantized GGUF version is also provided in this repository for local inference with compatible runtimes such as llama.cpp.

Available model format:

• GGUF: minime_it_Q8_0.gguf

The GGUF version can be used for efficient local inference without loading the full Safetensors model.

Model Formats

This repository provides multiple formats for different inference environments:

• Safetensors — recommended for Transformers-based inference
• GGUF — recommended for local inference using GGUF-compatible runtimes

Training Details

MiniMe IT was instruction-tuned from the MiniMe Base model using a custom conversational and instruction-following dataset developed for MiniMe.

The training objective focused on improving:

• Instruction following
• Conversational behavior
• Assistant-style responses
• User intent understanding
• Reasoning
• Task-oriented responses

The detailed training workflow is available in the Kaggle notebook:

Kaggle Training Notebook

Evaluation

Evaluation focused primarily on the model's intended conversational use cases.

Key evaluation areas include:

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

As MiniMe IT is designed for a personalized assistant rather than a single benchmark task, qualitative and task-specific evaluation are particularly relevant.

Technical Specifications

Model Architecture

MiniMe IT is a causal language model based on the MiniMe Base model.

The model uses the Qwen 3.5 model architecture and is instruction-tuned for conversational applications.

Available Formats

Software

The model can be used with software including:

• Hugging Face Transformers
• PyTorch
• llama.cpp
• Other compatible GGUF inference engines

APA:

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

More Information

MiniMe IT is part of the broader development of MiniMe, a personal AI assistant focused on combining:

• Language models
• Personalization
• Memory
• Retrieval
• Reasoning
• Tool use
• AI agents
• External context

MiniMe IT is the instruction-tuned conversational layer built on top of the MiniMe Base model.

Related Models

• MiniMe Base: brijeshah/Minime_base
• MiniMe IT: brijeshah/Minime-it

Model Card Authors

Brijesh

Model Card Contact

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