Text Generation
Transformers
GGUF
English
llama
unsloth
Uncensored
text-generation-inference
trl
roleplay
conversational
Instructions to use N-Bot-Int/MiniMaid_L3-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use N-Bot-Int/MiniMaid_L3-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="N-Bot-Int/MiniMaid_L3-GGUF")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("N-Bot-Int/MiniMaid_L3-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use N-Bot-Int/MiniMaid_L3-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf N-Bot-Int/MiniMaid_L3-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf N-Bot-Int/MiniMaid_L3-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf N-Bot-Int/MiniMaid_L3-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf N-Bot-Int/MiniMaid_L3-GGUF:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf N-Bot-Int/MiniMaid_L3-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf N-Bot-Int/MiniMaid_L3-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf N-Bot-Int/MiniMaid_L3-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf N-Bot-Int/MiniMaid_L3-GGUF:Q4_K_M
Use Docker
docker model run hf.co/N-Bot-Int/MiniMaid_L3-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use N-Bot-Int/MiniMaid_L3-GGUF 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_L3-GGUF" # 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_L3-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/N-Bot-Int/MiniMaid_L3-GGUF:Q4_K_M
- SGLang
How to use N-Bot-Int/MiniMaid_L3-GGUF 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_L3-GGUF" \ --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_L3-GGUF", "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_L3-GGUF" \ --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_L3-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use N-Bot-Int/MiniMaid_L3-GGUF with Ollama:
ollama run hf.co/N-Bot-Int/MiniMaid_L3-GGUF:Q4_K_M
- Unsloth Desktop
- Pi
How to use N-Bot-Int/MiniMaid_L3-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf N-Bot-Int/MiniMaid_L3-GGUF:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "N-Bot-Int/MiniMaid_L3-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use N-Bot-Int/MiniMaid_L3-GGUF with Docker Model Runner:
docker model run hf.co/N-Bot-Int/MiniMaid_L3-GGUF:Q4_K_M
- Lemonade
How to use N-Bot-Int/MiniMaid_L3-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull N-Bot-Int/MiniMaid_L3-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.MiniMaid_L3-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use N-Bot-Int/MiniMaid_L3-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf N-Bot-Int/MiniMaid_L3-GGUF:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default N-Bot-Int/MiniMaid_L3-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use N-Bot-Int/MiniMaid_L3-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf N-Bot-Int/MiniMaid_L3-GGUF:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "N-Bot-Int/MiniMaid_L3-GGUF:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
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Download README.md from N-Bot-Int/MiniMaid_L3-GGUF: direct link, hf CLI and curl.
- Browser
- Download file 2.2 kB
-
https://huggingface.co/N-Bot-Int/MiniMaid_L3-GGUF/resolve/96d907e2c7b2a68482a8040f2f38648b82eaef06/README.md
- Command line
-
hf download hf://N-Bot-Int/MiniMaid_L3-GGUF@96d907e2c7b2a68482a8040f2f38648b82eaef06/README.md
-
curl -L -o README.md https://huggingface.co/N-Bot-Int/MiniMaid_L3-GGUF/resolve/96d907e2c7b2a68482a8040f2f38648b82eaef06/README.md
2.2 kB
| 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: | |
| - N-Bot-Int/MiniMaid-L2 | |
| pipeline_tag: text-generation | |
| metrics: | |
| - character | |
|  | |
| # GGUF Version | |
| **GGUF** with Quants! Allowing you to run models using KoboldCPP and other AI Environments! | |
| # Quantizations: | |
| | Quant Type | Benefits | Cons | | |
| |---------------|---------------------------------------------------|---------------------------------------------------| | |
| | **Q4_K_M** | β Smallest size (fastest inference) | β Lowest accuracy compared to other quants | | |
| | | β Requires the least VRAM/RAM | β May struggle with complex reasoning | | |
| | | β Ideal for edge devices & low-resource setups | β Can produce slightly degraded text quality | | |
| | **Q5_K_M** | β Better accuracy than Q4, while still compact | β Slightly larger model size than Q4 | | |
| | | β Good balance between speed and precision | β Needs a bit more VRAM than Q4 | | |
| | | β Works well on mid-range GPUs | β Still not as accurate as higher-bit models | | |
| | **Q8_0** | β Highest accuracy (closest to full model) | β Requires significantly more VRAM/RAM | | |
| | | β Best for complex reasoning & detailed outputs | β Slower inference compared to Q4 & Q5 | | |
| | | β Suitable for high-end GPUs & serious workloads | β Larger file size (takes more storage) | | |
| # Model Details: | |
| Read the Model details on huggingface | |
| [Model Detail Here!](https://huggingface.co/N-Bot-Int/MiniMaid-L3) |