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
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:
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:
1from transformers import AutoTokenizer, AutoModelForCausalLM
2
3model_id = "brijeshah/Minime-it"
4
5tokenizer = AutoTokenizer.from_pretrained(model_id)
6
7model = AutoModelForCausalLM.from_pretrained(
8 model_id,
9 device_map="auto"
10)
11
12messages = [
13 {
14 "role": "user",
15 "content": "Hello MiniMe, introduce yourself."
16 }
17]
18
19inputs = tokenizer.apply_chat_template(
20 messages,
21 tokenize=True,
22 add_generation_prompt=True,
23 return_tensors="pt"
24).to(model.device)
25
26outputs = model.generate(
27 inputs,
28 max_new_tokens=256
29)
30
31response = tokenizer.decode(
32 outputs[0],
33 skip_special_tokens=True
34)
35
36print(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:
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
| Format | File | Intended Use |
|---|
| Safetensors | model.safetensors | Transformers / GPU inference |
| GGUF | minime_it_Q8_0.gguf | Local inference / llama.cpp |
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.
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