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")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

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

llama-cpp-python

How to use N-Bot-Int/ElaNore3-4B-GGUFF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="N-Bot-Int/ElaNore3-4B-GGUFF",
	filename="N-Bot-Int--ElaNore3-4B-merged-F16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use N-Bot-Int/ElaNore3-4B-GGUFF with llama.cpp:

Install from brew

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

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 Studio new

How to use N-Bot-Int/ElaNore3-4B-GGUFF 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/ElaNore3-4B-GGUFF 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/ElaNore3-4B-GGUFF 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/ElaNore3-4B-GGUFF to start chatting

Pi new

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-server -hf N-Bot-Int/ElaNore3-4B-GGUFF: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": "N-Bot-Int/ElaNore3-4B-GGUFF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

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-server -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

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

ElaNore3-4B-GGUFF

File size: 2,259 Bytes

8dc50d9

---
license: agpl-3.0
tags:
- unsloth
- Uncensored
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- roleplay
- conversational
- gguf
language:
- en
base_model:
- N-Bot-Int/ElaNore3-4B-merged
pipeline_tag: text-generation
metrics:
- character
---
# Support Us Through
  - [![ko-fi](https://ko-fi.com/img/githubbutton_sm.svg)](https://ko-fi.com/J3J61D8NHV)
  - [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](https://huggingface.co/mradermacher/ElaNore3-4B-merged-GGUF))

# Model Details:
  Read the Model details on huggingface
  [Model Detail Here!](https://huggingface.co/N-Bot-Int/ElaNore3-4B-merged)