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

Libraries

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

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

pipe = pipeline("text-generation", model="N-Bot-Int/ElaNore3-4B-merged")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("N-Bot-Int/ElaNore3-4B-merged")
model = AutoModelForCausalLM.from_pretrained("N-Bot-Int/ElaNore3-4B-merged")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

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

Use Docker

docker model run hf.co/N-Bot-Int/ElaNore3-4B-merged

SGLang

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

Unsloth Studio new

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

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="N-Bot-Int/ElaNore3-4B-merged",
    max_seq_length=2048,
)

Docker Model Runner
How to use N-Bot-Int/ElaNore3-4B-merged with Docker Model Runner:
```
docker model run hf.co/N-Bot-Int/ElaNore3-4B-merged
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

ElaNore3-4B - Is Now Released!

IMAGE GENERATED USING CHATGPT!

ElaNore3-4B, The Newest And BEST model WE HAVE MADE!

Feast your Eyes on ElaNore3-4B, trained on Qwen3-4B(CREDIT TO DREAMFAST for the HERETIC Base)
ElaNore3-4B, Is trained on Google Colab, with a goal of Making The BEST Smallest RP model that can be Run on any hardware!
ElaNore3 specializes in Roleplaying scenarios, with specialization on ChatML format!

READ MORE FOR MORE INFO 4 BILLIONS PARAMS MODEL

ElaNore3-4B Model Procedure/Methodology:

ElaNore3-4B is trained Using DreamFast's Heretical Version of the Base Model(Qwen3-4B). Dataset is prepared for ElaNore, with 6K Rows/Entry of Carefully Picked RP scenarios and Dataset From Iris-Uncensored-Reformat-R2, Synthetically Made Dataset Entry(4k combined) from Hermes, and Human Roleplay Entries available here in Huggingface. Forming The final Dataset Named: RP-MIXED-V2, which contains 60% Synthetic Dataset, 40% Human-Written Dataset all finetuned for RP in mind
4k synthetically made dataset contains the following:
- Single Roleplay
- MultiTurn Roleplay
- Narration Roleplay
2k Human Dataset contains the following:
- Human Written Roleplay
- Small Salvaged Dataset from Iris Uncensored Reformat R2
ElaNore3-4B is Trained using Unsloth, SFT with 3 Epochs with final Training loss of 1.4 using the RP-MIXED-V2 dataset, Trained on GOOGLE COLAB FREE TIER T4 GPU which took half a day to train(Lucky Me)
ElaNore3-4B is Our Brand New Powerful Model, If you ever encountered any issue, Want to commission us, or have any suggestions, please email us directly through nexus.networkinteractives@gmail.com we value any reports, suggestions to how we improve future Model, Once again feel free to finetune the model to your likings, However please consider Adding this Page for CREDITS
Please handle the AI with Care and ethical considerations, when FINETUNING this AI model, due to its UNCENSORED Nature.
We are not responsible for what this model generates. Use it responsibly and legally. You downloaded it, you own what you do with it.
ElaNore3-4B is
- Developed by: N-Bot-Int
- License: gpl 3.0
  
  EQ Bench V2 score(LEGACY BUT V3 IS EXPENSIVE AS FUQ soooooooo)
  - Slightly lowered due to EQ Bench not using ChatML which dipped the AI Model's performance slightly!
  - METRIC SCORES ARE HIGHLY SUBJECTIVE, Feel free to use the model and judge them yourselves!
Notice
- For a Good Experience, Please use
  - (PLEASE CALIBRATE THE MODEL DEPENDING ON THE CHARACTER CARD YOU USE)
  - USE A SYSTEM PROMPT IF YOU NEED ACTIONS WRAPPED IN "*", Hermes does not use it nor human Roleplay on the dataset, hence the model obtained a bias to not use asterisk on actions, but use double-quotes on character's words
  - USE CHATML, the AI MODEL IS FINETUNED TO USE CHATML more than any other format!
Detail card:
- Parameter
  - 4 Billion Parameters
  - (Please check your GPU Core, VRAM, CPU and RAM to see if you can comfortably run 4B models)
Finetuning tool:
- Unsloth AI
- This qwen3 model was trained 2x faster with Unsloth and Huggingface's TRL library.
- Fine-tuned Using:
- Google Colab Free Tier