Instructions to use N-Bot-Int/OpenElla-NovelWriter-8B-V2-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use N-Bot-Int/OpenElla-NovelWriter-8B-V2-merged with Transformers:

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

pipe = pipeline("text-generation", model="N-Bot-Int/OpenElla-NovelWriter-8B-V2-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/OpenElla-NovelWriter-8B-V2-merged")
model = AutoModelForCausalLM.from_pretrained("N-Bot-Int/OpenElla-NovelWriter-8B-V2-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/OpenElla-NovelWriter-8B-V2-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/OpenElla-NovelWriter-8B-V2-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/OpenElla-NovelWriter-8B-V2-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/N-Bot-Int/OpenElla-NovelWriter-8B-V2-merged

SGLang

How to use N-Bot-Int/OpenElla-NovelWriter-8B-V2-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/OpenElla-NovelWriter-8B-V2-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/OpenElla-NovelWriter-8B-V2-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/OpenElla-NovelWriter-8B-V2-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/OpenElla-NovelWriter-8B-V2-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use N-Bot-Int/OpenElla-NovelWriter-8B-V2-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/OpenElla-NovelWriter-8B-V2-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/OpenElla-NovelWriter-8B-V2-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/OpenElla-NovelWriter-8B-V2-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/OpenElla-NovelWriter-8B-V2-merged",
    max_seq_length=2048,
)

Docker Model Runner
How to use N-Bot-Int/OpenElla-NovelWriter-8B-V2-merged with Docker Model Runner:
```
docker model run hf.co/N-Bot-Int/OpenElla-NovelWriter-8B-V2-merged
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

// BORKED :/ - visit the other versions if ya want!

(The model exhibits covertion to uncensored RP and Godmods!)

if this is, what you want! Aka Novel formatting(get it.. Novel Writer!)... no seriously... A story generator
Then this model is for YOU

OpenElla-NovelWriter-8B-V2 is OFFICIALLY RELEASED!

IMAGE GENERATED USING CHATGPT!

OpenElla-NovelWriter-8B-V2, LIMITLESS EXPERIENCE, EXECELENT RP, EFFICIENT WEIGHT SIZE!

Built on top of p-e-w/Llama-3.1-8B-Instruct-heretic, OpenElla-NovelWriter-8B-V2 is our NEWEST and MOST CAPABLE roleplay-focused model to date, trained with a clear goal: deliver the best open RP experience on accessible hardware, RIVALING OUR CHAMPION MISTHENA7B!.
OpenElla specializes in Roleplay scenarios, with strong support for narrative writing, character immersion, and proper RP formatting (dialogue in " and actions in *).

READ MORE FOR MORE INFO 8 BILLIONS PARAMS MODEL

OpenElla-NovelWriter-8B-V2 Model Procedure/Methodology:

OpenElla-NovelWriter-8B-V2 is trained Using P-E-W-S's Llama-3.1-8B Instruct Version of the Model, ensuring full creative flow without refusal! OpenElla-NovelWriter-8B-V2 is then fed our brand new 17K DATASET named MEulysis-SYNTHETIC-M1!
MEulysis-SYNTHETIC-M1 — a 17K entry synthetically generated dataset produced using multiple capable RP models (including Mythomax and Hermes), then carefully cleaned, formatted, and tuned across a wide range of roleplay scenarios.

What makes MEulysis-SYNTHETIC-M1 different?

17,000 entries of synthetic RP data generated from multiple frontier RP models
Wide RP focus covering diverse character types, settings, and narrative styles
Includes MOST ELABORATE EXPERIENCES to MOST TABOO SCENARIOS
Clear formatting guidance baked into the data — proper use of "dialogue" and *actions*
Removal of ANY FORMATTING, ensuring Dataset's Usability to other Architectures!
Fully cleaned and curated for quality and consistency

Training Details

Finetuning Tool: Unsloth AI + Huggingface TRL
Training Platform: Kaggle Free Tier with T4 x2!
Epochs: 3
Final Training Loss: 0.8 (Lowest we've achieved so far!)
Dataset: MEulysis-SYNTHETIC-M1 (17K entries)
OpenElla-NovelWriter-8B-V2 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.

What's Coming Next?

🔒 Human Fine-Tuned Version & Mixed Models — Coming Soon for Supporters First!
We're currently working on a Newer version of OpenElla to push the model's roleplay quality even further. Supporters will get early access before public release. Stay tuned!

Benchmarks are also in the pipeline and will be added once available.

Notices & Usage Tips

Use Llama 3 format — the model is based on Llama 3, Soooooo using Llama 3 works best!.
Calibrate per character card — every character is different, adjust your prompt, Model's settings(ie, temps, Top-K etc.) accordingly.

About

OpenElla-NovelWriter-8B-V2 is
- Developed by: N-Bot-Int
- License: agpl 3.0
- Finetuned from model : p-e-w/Llama-3.1-8B-Instruct-heretic
Detail card:
- Parameter
  - 8 Billion Parameters
  - (Please check your GPU Core, VRAM, CPU and RAM to see if you can comfortably run 8B models)
Finetuning tool:
- Unsloth AI
- This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
- Fine-tuned Using:
- Kaggle Free Tier with T4 x2