Instructions to use DoctorEdoP369/AIM_AI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DoctorEdoP369/AIM_AI with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="DoctorEdoP369/AIM_AI",
	filename="AIM_AI.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/AIM_AI 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/AIM_AI
# Run inference directly in the terminal:
llama cli -hf DoctorEdoP369/AIM_AI

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf DoctorEdoP369/AIM_AI
# Run inference directly in the terminal:
llama cli -hf DoctorEdoP369/AIM_AI

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/AIM_AI
# Run inference directly in the terminal:
./llama-cli -hf DoctorEdoP369/AIM_AI

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/AIM_AI
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DoctorEdoP369/AIM_AI

Use Docker

docker model run hf.co/DoctorEdoP369/AIM_AI

LM Studio
Jan
Ollama
How to use DoctorEdoP369/AIM_AI with Ollama:
```
ollama run hf.co/DoctorEdoP369/AIM_AI
```

Unsloth Studio

How to use DoctorEdoP369/AIM_AI 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/AIM_AI 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/AIM_AI to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for DoctorEdoP369/AIM_AI to start chatting

How to use DoctorEdoP369/AIM_AI with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf DoctorEdoP369/AIM_AI

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/AIM_AI"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use DoctorEdoP369/AIM_AI 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/AIM_AI

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/AIM_AI

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use DoctorEdoP369/AIM_AI with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf DoctorEdoP369/AIM_AI

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/AIM_AI" \
  --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/AIM_AI with Docker Model Runner:
```
docker model run hf.co/DoctorEdoP369/AIM_AI
```

Lemonade

How to use DoctorEdoP369/AIM_AI with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull DoctorEdoP369/AIM_AI

Run and chat with the model

lemonade run user.AIM_AI-{{QUANT_TAG}}

List all available models

lemonade list

AIM_AI

AIM_AI is an advanced language model optimized for conversational fine-tuning, deep reasoning, and high-empathy interactions.

Built on top of DavidAU/Qwen3.5-4B-Claude-4.6-OS-Auto-Variable-HERETIC-UNCENSORED-THINKING, this model balances superior analytical capabilities with a powerful yet natural tone.

🚀 Model Details

Developed by: DoctorEdoP369
Model Type: Large Language Model (Causal LM)
Base Model: DavidAU/Qwen3.5-4B-Claude-4.6-OS-Auto-Variable-HERETIC-UNCENSORED-THINKING
License: Apache 2.0
Language(s): English, Italian (and other major languages supported by the base model)

📊 Training Datasets

The model underwent fine-tuning using the following datasets to enhance both its logical reasoning and conversational alignment:

nvidia/Nemotron-Post-Training-Dataset-v2: Used to boost multi-turn reasoning, general answer quality, and accuracy.
HuggingFaceTB/smoltalk2:

📊 You can find also Setup file for Windows ALL IN ONE AIM AI + SERVER:

AIM_AI Windows Setup Package

Welcome to the official Windows installer for AIM_AI.

⚠️ Why use this setup?

We highly recommend using this provided Windows setup package to run AIM_AI. Using our installer is the most reliable way to ensure the model functions correctly on your system.

Benefits of the official setup:

Automated Configuration: No need to manually manage Python environments, drivers, or model paths.
Optimized Performance: Pre-configured settings ensure the model runs smoothly on supported hardware.
Error-Free Deployment: It resolves dependency issues automatically, preventing common "missing library" or path errors.

🚀 Installation Guide

Download: Download the Setup_AIM_AI.zip file from the "Files and versions" tab.
Extract: Right-click the file and select "Extract All".
Install: Run the installer file and follow the on-screen instructions.
Launch: Once complete, launch the shortcut created on your desktop to start interacting with AIM_AI.

Datasets

nvidia/Nemotron-Post-Training-Dataset-v2
HuggingFaceTB/smoltalk2
Customized High quality datasets

Nvidia Nemotron dataset:

License/Terms of Use The dataset contains information about license type on a per sample basis. The dataset is predominantly CC-BY-4.0, with a small subset of prompts from Wildchat having an ODC-BY license and a small subset of prompts from StackOverflow with CC-BY-SA license.

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

Downloads last month: -

GGUF

Model size

4B params

Architecture

qwen35

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for DoctorEdoP369/AIM_AI

Base model

Qwen/Qwen3.5-4B-Base

Finetuned

Qwen/Qwen3.5-4B

Finetuned

coder3101/Qwen3.5-4B-heretic

Finetuned

DavidAU/Qwen3.5-4B-Claude-4.6-OS-Auto-Variable-HERETIC-UNCENSORED-THINKING

Quantized

(8)

this model

DoctorEdoP369
/

AIM_AI

AIM_AI

🚀 Model Details

📊 Training Datasets

📊 You can find also Setup file for Windows ALL IN ONE AIM AI + SERVER:

⚠️ Why use this setup?

Benefits of the official setup:

🚀 Installation Guide

Model tree for DoctorEdoP369/AIM_AI

Datasets used to train DoctorEdoP369/AIM_AI