Instructions to use N-Bot-Int/MrgrtV2-3B-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use N-Bot-Int/MrgrtV2-3B-merged with Transformers:

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

pipe = pipeline("text-generation", model="N-Bot-Int/MrgrtV2-3B-merged")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("N-Bot-Int/MrgrtV2-3B-merged")
model = AutoModelForMultimodalLM.from_pretrained("N-Bot-Int/MrgrtV2-3B-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 Settings

vLLM

How to use N-Bot-Int/MrgrtV2-3B-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/MrgrtV2-3B-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/MrgrtV2-3B-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/N-Bot-Int/MrgrtV2-3B-merged

SGLang

How to use N-Bot-Int/MrgrtV2-3B-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/MrgrtV2-3B-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/MrgrtV2-3B-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/MrgrtV2-3B-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/MrgrtV2-3B-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use N-Bot-Int/MrgrtV2-3B-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/MrgrtV2-3B-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/MrgrtV2-3B-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/MrgrtV2-3B-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/MrgrtV2-3B-merged",
    max_seq_length=2048,
)

Docker Model Runner
How to use N-Bot-Int/MrgrtV2-3B-merged with Docker Model Runner:
```
docker model run hf.co/N-Bot-Int/MrgrtV2-3B-merged
```

MrgrtV2-3B-merged / README.md

ItsMeDevRoland

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	---
	base_model:
	- huihui-ai/Llama-3.2-3B-Instruct-abliterated
	tags:
	- text-generation-inference
	- transformers
	- unsloth
	- llama
	- Uncensored
	- trl
	- roleplay
	- conversational
	license: agpl-3.0
	language:
	- en
	pipeline_tag: text-generation
	datasets:
	- N-Bot-Int/RP-Mixed-v1
	library_name: transformers
	---
	# MrgrtV2-3B is OFFICIALLY RELEASED!
	![image](https://cdn-uploads.huggingface.co/production/uploads/6633a73004501e16e7896b86/GIpxSiz58X29uP8WbuJWe.png)

	# MrgrtV2-3B, rebalanced, cleaned and fixed than previous version!
	- MrgrtV2-3B, our brand new flagship model is now released with a better model training than previous V1, the V1 had a massive poison on the dataset
	it were trained, leading to the model having insane early <\|eos_token\|> even though the roleplay is still not yet finished.
	- MrgrtV2-3B aims to fix the issue by showcasing the quality of the dataset, by training on a 3B model! revealing that the dataset is high quality!
	and the early issues were simply the poison that killed the quality of V1 Models!
	THEREFORE THIS MODEL IS PURELY A FIX TO THE PREVIOUS V1 MODEL! HOWEVER THE QUALITY IS INSANELY, NIGHT AND DAY!

	READ MORE FOR MORE INFO
	3 BILLIONS PARAMS MODEL

	# MrgrtV2-3B Model Procedure/Methodology:
	- MrgrtV2-3B is trained Using HuiHui-Ai's Llama 3.2 3B instruct abliterated Model,
	ensuring full creative flow without refusal!

	MrgrtV2-3B is then fed the new 22k single-row DATASET named MEulysis-Cleaned-formatted which were obtained after splitting the previous dataset and cleaned EVEN FURTHER!

	- MEulysis-Cleaned-formatted — a 22 entry synthetically generated dataset produced using
	multiple capable RP models (including Hermes(mythomax were removed because the mythomax was causing the poison to the dataset)), then carefully cleaned,
	formatted with Llama 3 chat formatting, and then, tuned across a wide range of roleplay scenarios.

	### What makes MEulysis-Cleaned different?
	- 22,000 entries of synthetic RP data generated from multiple frontier RP models
	- removed mythomax which caused early eos terminations and some... quality issue removal
	- 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: 1.2
	- Dataset: MEulysis-Cleaned-formatted


	- MrgrtV2-3B 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](mailto: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?

	> 🔒 V3 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 V3 with better improvements!

	> 🔒 8B variant to be released soon!
	> Due to this cleanup, we can conclude that the 8B model will be better than the previous V1! we'll take our time releasing the next 8B variant!

	> 🔒 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!)
	[![ko-fi](https://ko-fi.com/img/githubbutton_sm.svg)](https://ko-fi.com/J3J61D8NHV)

	> 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 0.60 and Dynamic max as 2.00.
	That's the setting we found to make the model more coherent, less hallucination whilst making the model adventurous!
	---

	# About
	- MrgrtV2-3B is
	- Developed by: N-Bot-Int
	- License: agpl 3.0
	- Finetuned from model : huihui-ai/Llama-3.2-3B-Instruct-abliterated

	- # Detail card:
	- Parameter
	- 3 Billion Parameters
	- (Please check your GPU Core, VRAM, CPU and RAM to see if you can comfortably run 3B models)

	- Finetuning tool:
	- Unsloth AI
	- This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
	[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
	- Fine-tuned Using:
	- Kaggle Free Tier with T4 x2 for 4 Weeks