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

Libraries

How to use Nesy1/Nesys_engine with Transformers:

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

llama-cpp-python

How to use Nesy1/Nesys_engine with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Nesy1/Nesys_engine",
	filename="BrokenDan_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

llama.cpp

How to use Nesy1/Nesys_engine with llama.cpp:

Install from brew

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

Use Docker

docker model run hf.co/Nesy1/Nesys_engine:Q4_K_S

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

Unsloth Studio new

How to use Nesy1/Nesys_engine 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/Nesys_engine 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/Nesys_engine to start chatting

Using HuggingFace Spaces for Unsloth

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

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

Lemonade

How to use Nesy1/Nesys_engine with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Nesy1/Nesys_engine:Q4_K_S

Run and chat with the model

lemonade run user.Nesys_engine-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.

Merge Details

Merge Method

This model was merged using the DARE TIES merge method using PocketDoc/Dans-PersonalityEngine-V1.3.0-24b 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.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: Delta-Vector/MS3.2-Austral-24B-KTO
    parameters:
      weight: 0.3
      density: 0.3
  - model: Delta-Vector/Austral-24B-Winton
    parameters:
      weight: 0.3
      density: 0.5
  - model: ReadyArt/The-Omega-Directive-M-24B-v1.1
    parameters:
      weight: 0.2
      density: 0.3
merge_method: ties
base_model: PocketDoc/Dans-PersonalityEngine-V1.3.0-24b
dtype: bfloat16
outtype: bfloat16

Usage guide

The prompting format is refered to as Dan's chat for this model.

<|system|>system prompt<|endoftext|><|user|>Hi there!<|endoftext|><|assistant|>Hey, how can I help?<|endoftext|>

I would advise starting with adaptive.p at the settings: target: 0,55 decay: 0.9 min_p: 0.04

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.