Instructions to use DoctorEdoP369/AnonymousEnergyAi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use DoctorEdoP369/AnonymousEnergyAi with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="DoctorEdoP369/AnonymousEnergyAi", filename="AnonymousEnergy_Ai_no_prompt.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use DoctorEdoP369/AnonymousEnergyAi 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 DoctorEdoP369/AnonymousEnergyAi:F16 # Run inference directly in the terminal: llama cli -hf DoctorEdoP369/AnonymousEnergyAi:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf DoctorEdoP369/AnonymousEnergyAi:F16 # Run inference directly in the terminal: llama cli -hf DoctorEdoP369/AnonymousEnergyAi:F16
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 DoctorEdoP369/AnonymousEnergyAi:F16 # Run inference directly in the terminal: ./llama-cli -hf DoctorEdoP369/AnonymousEnergyAi:F16
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 DoctorEdoP369/AnonymousEnergyAi:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf DoctorEdoP369/AnonymousEnergyAi:F16
Use Docker
docker model run hf.co/DoctorEdoP369/AnonymousEnergyAi:F16
- LM Studio
- Jan
- Ollama
How to use DoctorEdoP369/AnonymousEnergyAi with Ollama:
ollama run hf.co/DoctorEdoP369/AnonymousEnergyAi:F16
- Unsloth Studio
How to use DoctorEdoP369/AnonymousEnergyAi 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 DoctorEdoP369/AnonymousEnergyAi 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 DoctorEdoP369/AnonymousEnergyAi to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for DoctorEdoP369/AnonymousEnergyAi to start chatting
- Pi
How to use DoctorEdoP369/AnonymousEnergyAi with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf DoctorEdoP369/AnonymousEnergyAi:F16
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "DoctorEdoP369/AnonymousEnergyAi:F16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use DoctorEdoP369/AnonymousEnergyAi with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf DoctorEdoP369/AnonymousEnergyAi:F16
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 DoctorEdoP369/AnonymousEnergyAi:F16
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use DoctorEdoP369/AnonymousEnergyAi with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf DoctorEdoP369/AnonymousEnergyAi:F16
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 "DoctorEdoP369/AnonymousEnergyAi:F16" \ --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"
- Docker Model Runner
How to use DoctorEdoP369/AnonymousEnergyAi with Docker Model Runner:
docker model run hf.co/DoctorEdoP369/AnonymousEnergyAi:F16
- Lemonade
How to use DoctorEdoP369/AnonymousEnergyAi with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull DoctorEdoP369/AnonymousEnergyAi:F16
Run and chat with the model
lemonade run user.AnonymousEnergyAi-F16
List all available models
lemonade list
AnonymousEnergy Ai
Powerful. Unique. Consigliere. Deep Reasoning.
A 9B reasoning model that runs entirely on your machine. No account, no cloud, no telemetry — your conversations never leave the room they were typed in.
Why this model exists
Most people who ask an AI a serious question are asked to trust a server they cannot see. AnonymousEnergy Ai was built on the opposite premise: the machine that answers you should be the machine you own.
It is distributed in GGUF format, so it runs offline on consumer hardware through llama.cpp, LM Studio, Ollama, Jan and any compatible client. Once the file is on your disk, nothing else is needed — not a connection, not a subscription, not permission.
What it does well
- Deep reasoning. Built on a thinking-mode base, the model produces an explicit
<think>chain before answering. You can read how it got there, not just where it landed. Naturally, it is best to use programs that display the answer directly, to avoid wasting time watching how the model "reasons." - Long, coherent conversations. Fine-tuned on multi-turn material where reasoning is carried across several exchanges, not restarted at every message.
- A direct voice. Trained to answer without filler, without the reflex of hedging, and without escaping into generalities.
- Vision. The multimodal projector is included, so the model can read images when paired with a client that supports it.
- Multilingual. Strong in English and Italian, with the wide language coverage of the Qwen3.5 base.
Files
| File | Size | What it is |
|---|---|---|
AnonymousEnergy_Ai_prompt.gguf |
6.85 GB | Recommended. Ships with a built-in default system prompt. Just load and talk. |
AnonymousEnergy_Ai_no_prompt.gguf |
6.85 GB | Identical weights, clean chat template. For those who want to write their own system prompt. |
mmproj-qwen35-9b-f16.gguf |
876 MB | Vision projector. Optional — load it only if you want image understanding. |
Both model files are Q6_K: near-full quality, roughly 8 GB of RAM or VRAM at moderate context.
The difference between the two files: the
promptversion already contains a persona and language rules inside its chat template, and applies them whenever you do not provide a system prompt of your own. If you do provide one, yours wins. Theno-promptversion has no such default. Pick one — you do not need both.
Quick start
# text only
llama-server -m AnonymousEnergy_Ai_prompt.gguf
# with vision
llama-server -m AnonymousEnergy_Ai_prompt.gguf --mmproj mmproj-qwen35-9b-f16.gguf
In LM Studio, Jan or Ollama: download the .gguf and open it. Nothing else to configure.
Model details
- Developed by: DoctorEdoP369
- Base model:
DavidAU/Qwen3.5-9B-Claude-4.6-HighIQ-THINKING-HERETIC-UNCENSORED - Architecture: Qwen3.5, 9B dense, thinking mode
- Method: LoRA fine-tuning with Unsloth, trained on responses only
- Quantization: Q6_K, from a bf16 merge
- License: Apache 2.0
Training data
Fine-tuned on a large mixed corpus. The two public datasets whose licensing terms must travel with this model are listed below.
nvidia/Nemotron-Post-Training-Dataset-v2
Used to strengthen multi-turn reasoning and general answer quality.
License/Terms of Use: the dataset carries per-sample license information. It is predominantly CC-BY-4.0, with a small subset of prompts from WildChat under ODC-BY and a small subset from StackOverflow under CC-BY-SA.
This dataset contains synthetic data created using DeepSeek-R1-0528, Qwen2.5-14B-Instruct, Qwen2.5-32B-Instruct-AWQ, Qwen3-30B-A3B and Qwen3-235B-A22B. If this dataset is used to create, train, fine-tune, or otherwise improve an AI model, which is distributed or made available, such AI model may be subject to redistribution and use requirements in the Qwen License Agreement and the DeepSeek License Agreement.
Data Developer: NVIDIA
facebook/natural_reasoning
Used to broaden open-ended reasoning across domains.
Released by Meta under CC-BY-NC-4.0. This is a non-commercial license. Users who intend to deploy this model commercially should review the terms on the dataset page and assess their own position.
The remaining training material consists of custom high-quality datasets built for this project, including original Italian multi-turn reasoning data.
High-quality custom datasets.
Limitations and honest notes
- This AI model is very powerful but this model has reduced safety filtering inherited from its base. It will answer questions that hosted assistants decline. That is a deliberate design choice aimed at autonomy — and it means the responsibility for what you ask, and what you do with the answer, is entirely yours.
- It can be confidently wrong. The visible reasoning chain makes errors easier to spot, but it does not prevent them. The author does not take any responsibility for the use you will make of it.
- It's not a professional. Nothing it says replaces a doctor, a lawyer, an accountant or an engineer.
- Vision support depends on your client. Several local apps ignore the projector file entirely.
AI can make mistakes; it does not replace professional advice.
Created by DoctorEdoP369
Thanks to DavidAu and the Creators of Qwen3.5.
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We're not able to determine the quantization variants.
Model tree for DoctorEdoP369/AnonymousEnergyAi
Base model
Qwen/Qwen3.5-9B-Base