Instructions to use N-Bot-Int/MiniMaid-L1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use N-Bot-Int/MiniMaid-L1 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "N-Bot-Int/MiniMaid-L1")

Transformers

How to use N-Bot-Int/MiniMaid-L1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="N-Bot-Int/MiniMaid-L1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("N-Bot-Int/MiniMaid-L1", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use N-Bot-Int/MiniMaid-L1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "N-Bot-Int/MiniMaid-L1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "N-Bot-Int/MiniMaid-L1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/N-Bot-Int/MiniMaid-L1

SGLang

How to use N-Bot-Int/MiniMaid-L1 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "N-Bot-Int/MiniMaid-L1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "N-Bot-Int/MiniMaid-L1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "N-Bot-Int/MiniMaid-L1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "N-Bot-Int/MiniMaid-L1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use N-Bot-Int/MiniMaid-L1 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for N-Bot-Int/MiniMaid-L1 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for N-Bot-Int/MiniMaid-L1 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for N-Bot-Int/MiniMaid-L1 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="N-Bot-Int/MiniMaid-L1",
    max_seq_length=2048,
)

Docker Model Runner
How to use N-Bot-Int/MiniMaid-L1 with Docker Model Runner:
```
docker model run hf.co/N-Bot-Int/MiniMaid-L1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

WARNING: THIS MODEL IS NOW DEPRICATED, Please Use MiniMaid-L2 for An Even Better 1B model!

MiniMaid-L1

Introducing Our Brand New Open-sourced AI model named MiniMaid-L1, Minimaid Boast a staggering 1B params with Good Coherent Story telling, Capable roleplaying ability (Due to its 1B params, it might produce bad and repetitive output).
MiniMaid-L1 achieve a good Performance through process of DPO and Combined Heavy Finetuning, To Prevent Overfitting, We used high LR decays, And Introduced Randomization techniques to prevent the AI from learning and memorizing, However since training this on Google Colab is difficult, the Model might underperform or underfit on specific tasks Or overfit on knowledge it manage to latched on! However please be guided that we did our best, and it will improve as we move onwards!
MiniMaid-L1 is Our Smallest Model Yet! if you find any issue, then please don't hesitate to email us at:
nexus.networkinteractives@gmail.com about any overfitting, or improvements for the future Model C, Once again feel free to Modify the LORA to your likings, However please consider Adding this Page for credits and if you'll increase its Dataset, then please handle it with care and ethical considerations
MiniMaid-L1 is
- Developed by: N-Bot-Int
- License: apache-2.0
- Parent Model from model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-1bit
- Dataset Combined Using: Mosher-R1(Propietary Software)
MiniMaid-L1 Official Metric Score
- Metrics Made By ItsMeDevRoland Which compares:
  - Deepseek R1 3B GGUF
  - Dolphin 3B GGUF
  - Hermes 3b Llama GGUFF
  - MiniMaid-L1 GGUFF Which are All Ranked with the Same Prompt, Same Temperature, Same Hardware(Google Colab), To Properly Showcase the differences and strength of the Models
- Visit Below to See details!

🧵 MiniMaid-L1: A 1B Roleplay Assistant That Punches Above Its Weight

She’s not perfect — but she’s fast, compact, and learning quick. And most importantly, she didn’t suck.

Despite her size, MiniMaid-L1 held her own against 3B models like DeepSeek, Dolphin, and Hermes. 💬 Roleplay Evaluation (v0)

🧠 Character Consistency: 0.50
🌊 Immersion: 0.13
🧮 Overall RP Score: 0.51
✏️ Length Score: 0.91
Even with only 1.5K synthetic samples, MiniMaid showed strong prompt structure, consistency, and resilience.

📊 Efficiency Wins

Inference Time: 49.1s (vs Hermes: 140.6s)
Tokens/sec: 7.15 (vs Dolphin: 3.88)
BLEU/ROUGE-L: Outperformed DeepSeek + Hermes
MiniMaid proved that you don’t need 3 billion parameters to be useful — just smart distillation and a little love.

🛠️ MiniMaid is Built For

Lightweight RP generation
Low-resource hardware
High customization potential

🌱 She’s just getting started — v1 is on the way with more character conditioning, dialogue tuning, and narrative personality control.

“She’s scrappy, she’s stubborn, and she’s still learning. But MiniMaid-L1 proves that smart distillation and a tiny budget can go a long way — and she’s only going to get better from here.”

Notice
- For a Good Experience, Please use
  - Low temperature 1.5, min_p = 0.1 and max_new_tokens = 128
Detail card:
- Parameter
  - 1 Billion Parameters
  - (Please visit your GPU Vendor if you can Run 1B models)
- Finetuning tool:
- Unsloth AI
  - This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
- Fine-tuned Using:
- Google Colab