Text Generation
PEFT
Safetensors
Transformers
English
unsloth
Uncensored
text-generation-inference
llama
trl
roleplay
conversational
Instructions to use N-Bot-Int/MiniMaid-L1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- 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
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
File size: 5,012 Bytes
f483155 1b4de53 f483155 1b4de53 f483155 917789e 1b4de53 0a2020f 1b4de53 ff148a6 1b4de53 ff148a6 1b4de53 ff148a6 1b4de53 ff148a6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 | ---
license: apache-2.0
tags:
- unsloth
- Uncensored
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- roleplay
- conversational
datasets:
- iamketan25/roleplay-instructions-dataset
- N-Bot-Int/Iris-Uncensored-R1
- N-Bot-Int/Moshpit-Combined-R2-Uncensored
- N-Bot-Int/Mushed-Dataset-Uncensored
- N-Bot-Int/Muncher-R1-Uncensored
- N-Bot-Int/Millia-R1_DPO
language:
- en
base_model:
- meta-llama/Llama-3.2-1B
pipeline_tag: text-generation
library_name: peft
metrics:
- character
---
# WARNING: THIS MODEL IS NOW DEPRICATED, Please Use MiniMaid-L2 for An Even Better 1B model!
<a href="https://ibb.co/GvDjFcVp"><img src="https://raw.githubusercontent.com/Nexus-Network-Interactives/HuggingfacePage/refs/heads/main/MiniMaid-L1.png" alt="image" border="0"></a>
# 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](mailto: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](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
- Fine-tuned Using:
- Google Colab
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