Instructions to use N-Bot-Int/MrgrtV1-8B-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use N-Bot-Int/MrgrtV1-8B-merged with Transformers:

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

pipe = pipeline("text-generation", model="N-Bot-Int/MrgrtV1-8B-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/MrgrtV1-8B-merged")
model = AutoModelForCausalLM.from_pretrained("N-Bot-Int/MrgrtV1-8B-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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use N-Bot-Int/MrgrtV1-8B-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/MrgrtV1-8B-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/MrgrtV1-8B-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/N-Bot-Int/MrgrtV1-8B-merged

SGLang

How to use N-Bot-Int/MrgrtV1-8B-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/MrgrtV1-8B-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/MrgrtV1-8B-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/MrgrtV1-8B-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/MrgrtV1-8B-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use N-Bot-Int/MrgrtV1-8B-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/MrgrtV1-8B-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/MrgrtV1-8B-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/MrgrtV1-8B-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/MrgrtV1-8B-merged",
    max_seq_length=2048,
)

Docker Model Runner
How to use N-Bot-Int/MrgrtV1-8B-merged with Docker Model Runner:
```
docker model run hf.co/N-Bot-Int/MrgrtV1-8B-merged
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

MrgrtV1-8B is OFFICIALLY RELEASED!

Thanks Elon :)

MrgrtV1-8B for Better Roleplaying with Maximum Quality improvements!

MrgrtV1-8B (pronounecd Margaret-V1) is our brand new AI MODEL built on top of MuXodious/Llama-3.3-8B-Instruct-128K-PaperWitch-heresy, MrgrtV1-8B is one of our Checkpoint we want to share to you, to showcase the current Improvement of our new training pipeline and New Innovations.
MrgrtV1-8B Is Our NEWEST model, trained on kaggle for 2 Weeks, which showcases the most COHERENCE compared to other models we made! MrgrtV1-8B excels greatly with Roleplay Focused, however DUE TO OUR LACK OF COMPLETE OPEN DATA, the ai model might produce subpar quality to Open Roleplay Compared to Contained-Roleplay focused scenarios!
MrgrtV1-8B is trained on our IMPROVED MEulysis dataset, that aims to dethrone Misthena and our previous OpenElla-NovelWriter Models!

READ MORE FOR MORE INFO 8 BILLIONS PARAMS MODEL

MrgrtV1-8B Model Procedure/Methodology:

MrgrtV1-8B is trained Using MuXodious's Llama-3.3-8B Paperwitch VersionThank you SO MUCH for all the help @redaihf, @MuXodious and @Naphula of the Model, ensuring full creative flow without refusal!

MrgrtV1-8B is then fed the new 100K single-row DATASET named MEulysis-Cleaned which were obtained after splitting the previous dataset and cleaned!
MEulysis-Cleaned — a 100k entry synthetically generated dataset produced using multiple capable RP models (including Mythomax and Hermes), then carefully cleaned, formatted with Llama 3 chat formatting, and then, tuned across a wide range of roleplay scenarios.

What makes MEulysis-Cleaned different?

100,000 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*
Now includes Llama 3 chat formatting to maximize quality on Llama models we'll release!
Fully cleaned and curated for quality and consistency

Training Details

Finetuning Tool: Unsloth AI + Huggingface TRL(we uses our brand new Kaggle Extenderizer to extend training without starting from scratch)
Training Platform: Kaggle Free Tier with T4 x2!
Epochs: 3
Final Training Loss: 1.1
Dataset: MEulysis-Cleaned
MrgrtV1-8B 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?

🔒 V2 will released with better support for more OPEN ROLEPLAY SCENARIOS
Right now the ai model is somewhat bad with Open roleplay, however we'll do our best to release the V2 with better improvements!

🔒 4b variant to be released soon! 4B variants trained on the same dataset, with same training methodology will be released shortly for those who lack system resources however still wish to use High Quality RPing model!

🔒 1B variant to be released Experimental 1B variant with the same training methodology will be released following 4B, this is EXTREMELY EXPERIMENTAL, 1B are not good for Roleplaying however we share them nonetheless!

🔒 Release PEFT Soon New PEFT releases of the model, will now be released late. Supporters on our Ko-fi can however request for the PEFT(through gmail) if they ever want to! (This is to thank our Ko-fi supporters!)

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. However We recommend(if you're using koboldcpp), to use DynaTemp with Dynamic Min as 0.50 and Dynamic max as 1.50. That's the setting we found to make the model more coherent, less hallucination whilst making the model adventurous!

About

MrgrtV1-8B is
- Developed by: N-Bot-Int
- License: agpl 3.0
- Finetuned from model : MuXodious/Llama-3.3-8B-Instruct-128K-PaperWitch-heresy
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 for 2 Weeks