Instructions to use Nesy1/NesysEngineV2.1TEST with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nesy1/NesysEngineV2.1TEST with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Nesy1/NesysEngineV2.1TEST", dtype="auto")

llama-cpp-python

How to use Nesy1/NesysEngineV2.1TEST with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Nesy1/NesysEngineV2.1TEST",
	filename="NesysEngineV2.1-Q4_K_S.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Nesy1/NesysEngineV2.1TEST with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Nesy1/NesysEngineV2.1TEST:Q4_K_S
# Run inference directly in the terminal:
llama-cli -hf Nesy1/NesysEngineV2.1TEST:Q4_K_S

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Nesy1/NesysEngineV2.1TEST:Q4_K_S
# Run inference directly in the terminal:
llama-cli -hf Nesy1/NesysEngineV2.1TEST:Q4_K_S

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 Nesy1/NesysEngineV2.1TEST:Q4_K_S
# Run inference directly in the terminal:
./llama-cli -hf Nesy1/NesysEngineV2.1TEST:Q4_K_S

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 Nesy1/NesysEngineV2.1TEST:Q4_K_S
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Nesy1/NesysEngineV2.1TEST:Q4_K_S

Use Docker

docker model run hf.co/Nesy1/NesysEngineV2.1TEST:Q4_K_S

LM Studio
Jan
Ollama
How to use Nesy1/NesysEngineV2.1TEST with Ollama:
```
ollama run hf.co/Nesy1/NesysEngineV2.1TEST:Q4_K_S
```

Unsloth Studio

How to use Nesy1/NesysEngineV2.1TEST 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 Nesy1/NesysEngineV2.1TEST 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 Nesy1/NesysEngineV2.1TEST to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Nesy1/NesysEngineV2.1TEST to start chatting

Docker Model Runner
How to use Nesy1/NesysEngineV2.1TEST with Docker Model Runner:
```
docker model run hf.co/Nesy1/NesysEngineV2.1TEST:Q4_K_S
```

Lemonade

How to use Nesy1/NesysEngineV2.1TEST with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Nesy1/NesysEngineV2.1TEST:Q4_K_S

Run and chat with the model

lemonade run user.NesysEngineV2.1TEST-Q4_K_S

List all available models

lemonade list

Merge overview

This is a merge of pre-trained language models created using mergekit. There are no higher quants available for this model, only one due to hardware limitations. Once a model has been validated, I will consider releasing safetensors so people can quant my models.

Merge Details

Merge Method

This model was merged using the TASK_ARITHMETIC merge method using anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-Text-Only as a base. The intention of this merge is to essensially make a model that is capable of roleplaying in NSFW,SFW settings all in one, with smarts involved. Moreover overlap multiple unique writing styles. Additionally experiment with using MS3.2 as a base for instruction following and also using Cydonia, Drummer's awesome finetune to spice up a model.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

merge_method: task_arithmetic
dtype: float32
outype: bfloat16
normalize: false 
base_model: anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-Text-Only #Instruction following capabilities
models:

  - model: ReadyArt/The-Omega-Directive-M-24B-v1.1 #SMUT
    parameters:
      weight: [0 , 0.05, 0.1 , 0.15 , 0.1 , 0.05 , 0]

  - model: TheDrummer/Cydonia-24B-v4.1 #Epic drummer tune 
    parameters:
      weight: [0.25 , 0.3,  0.4, 0.5 , 0.4 , 0.3 , 0.25]

  - model: PocketDoc/Dans-PersonalityEngine-V1.3.0-24b #Smarts, different writing style
    parameters:
      weight: [0 , 0.05, 0.15 , 0.2 , 0.15 , 0.25 , 0]

parameters:
  lambda: 1.0

Usage guide

The prompting format is simply Mistral Tekken. If you're in SillyTavern then Mistral Tekken V7 works very much as well.

Personally testing this model, I would advise starting with adaptive.p at the settings: target: 0,4 decay: 0.9 min_p: 0.05

You are free to experiment to set samplers to your liking, this model is under testing phase and users are free to experiment with samplers. The only main requirement is Prompt guide. Feel free to add your comments if you found a better sampler configuration. I am always open to feedback!

Downloads last month: 16

GGUF

Model size

24B params

Architecture

llama

Hardware compatibility

4-bit

Inference Providers NEW

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Model tree for Nesy1/NesysEngineV2.1TEST

ReadyArt/The-Omega-Directive-M-24B-v1.1

TheDrummer/Cydonia-24B-v4.1

Merge model

this model

Paper for Nesy1/NesysEngineV2.1TEST

Editing Models with Task Arithmetic

Paper • 2212.04089 • Published Dec 8, 2022 • 8