Instructions to use N-Bot-Int/ElaNore3-4B-GGUFF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use N-Bot-Int/ElaNore3-4B-GGUFF with Transformers:

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

pipe = pipeline("text-generation", model="N-Bot-Int/ElaNore3-4B-GGUFF")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("N-Bot-Int/ElaNore3-4B-GGUFF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use N-Bot-Int/ElaNore3-4B-GGUFF 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/ElaNore3-4B-GGUFF:F16
# Run inference directly in the terminal:
llama cli -hf N-Bot-Int/ElaNore3-4B-GGUFF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf N-Bot-Int/ElaNore3-4B-GGUFF:F16
# Run inference directly in the terminal:
llama cli -hf N-Bot-Int/ElaNore3-4B-GGUFF:F16

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf N-Bot-Int/ElaNore3-4B-GGUFF:F16
# Run inference directly in the terminal:
./build/bin/llama cli -hf N-Bot-Int/ElaNore3-4B-GGUFF:F16

Use Docker

docker model run hf.co/N-Bot-Int/ElaNore3-4B-GGUFF:F16

Llama new
LM Studio
Jan

vLLM

How to use N-Bot-Int/ElaNore3-4B-GGUFF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "N-Bot-Int/ElaNore3-4B-GGUFF"
# 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/ElaNore3-4B-GGUFF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/N-Bot-Int/ElaNore3-4B-GGUFF:F16

SGLang

How to use N-Bot-Int/ElaNore3-4B-GGUFF 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/ElaNore3-4B-GGUFF" \
    --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/ElaNore3-4B-GGUFF",
		"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/ElaNore3-4B-GGUFF" \
        --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/ElaNore3-4B-GGUFF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use N-Bot-Int/ElaNore3-4B-GGUFF with Ollama:
```
ollama run hf.co/N-Bot-Int/ElaNore3-4B-GGUFF:F16
```
Unsloth Desktop

How to use N-Bot-Int/ElaNore3-4B-GGUFF 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/ElaNore3-4B-GGUFF:F16

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/ElaNore3-4B-GGUFF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use N-Bot-Int/ElaNore3-4B-GGUFF with Docker Model Runner:
```
docker model run hf.co/N-Bot-Int/ElaNore3-4B-GGUFF:F16
```

Lemonade

How to use N-Bot-Int/ElaNore3-4B-GGUFF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull N-Bot-Int/ElaNore3-4B-GGUFF:F16

Run and chat with the model

lemonade run user.ElaNore3-4B-GGUFF-F16

List all available models

lemonade list

Hermes Agent

How to use N-Bot-Int/ElaNore3-4B-GGUFF 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/ElaNore3-4B-GGUFF: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 N-Bot-Int/ElaNore3-4B-GGUFF:F16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use N-Bot-Int/ElaNore3-4B-GGUFF 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/ElaNore3-4B-GGUFF: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 "N-Bot-Int/ElaNore3-4B-GGUFF: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"

Support Us Through

[https://ko-fi.com/nexusnetworkint](Official Ko-FI link!)

GGUF Version

GGUF with Quants! Allowing you to run models using KoboldCPP and other AI Environments!

Quantizations:

Quant Type	Benefits	Cons
Q4_K_M	✅ Smallest size (fastest inference)	❌ Lowest accuracy compared to other quants
	✅ Requires the least VRAM/RAM	❌ May struggle with complex reasoning
	✅ Ideal for edge devices & low-resource setups	❌ Can produce slightly degraded text quality
Q5_K_M	✅ Better accuracy than Q4, while still compact	❌ Slightly larger model size than Q4
	✅ Good balance between speed and precision	❌ Needs a bit more VRAM than Q4
	✅ Works well on mid-range GPUs	❌ Still not as accurate as higher-bit models
Q8_0	✅ Highest accuracy (closest to full model)	❌ Requires significantly more VRAM/RAM
	✅ Best for complex reasoning & detailed outputs	❌ Slower inference compared to Q4 & Q5
	✅ Suitable for high-end GPUs & serious workloads	❌ Larger file size (takes more storage)
(PLEASE VISIT THE mradermacher/ElaNore3-4B-merged-GGUF FOR THE PROPER FULL QUANTS, THIS entry only has THE F16 precission! - Visit Here)