Instructions to use jeiku/NarrativeNexus_7B_GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jeiku/NarrativeNexus_7B_GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("jeiku/NarrativeNexus_7B_GGUF", dtype="auto")

llama-cpp-python

How to use jeiku/NarrativeNexus_7B_GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="jeiku/NarrativeNexus_7B_GGUF",
	filename="NarrativeNexus-Q2_K.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use jeiku/NarrativeNexus_7B_GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf jeiku/NarrativeNexus_7B_GGUF:Q4_K_S
# Run inference directly in the terminal:
llama-cli -hf jeiku/NarrativeNexus_7B_GGUF:Q4_K_S

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf jeiku/NarrativeNexus_7B_GGUF:Q4_K_S
# Run inference directly in the terminal:
llama-cli -hf jeiku/NarrativeNexus_7B_GGUF: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 jeiku/NarrativeNexus_7B_GGUF:Q4_K_S
# Run inference directly in the terminal:
./llama-cli -hf jeiku/NarrativeNexus_7B_GGUF: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 jeiku/NarrativeNexus_7B_GGUF:Q4_K_S
# Run inference directly in the terminal:
./build/bin/llama-cli -hf jeiku/NarrativeNexus_7B_GGUF:Q4_K_S

Use Docker

docker model run hf.co/jeiku/NarrativeNexus_7B_GGUF:Q4_K_S

LM Studio
Jan
Ollama
How to use jeiku/NarrativeNexus_7B_GGUF with Ollama:
```
ollama run hf.co/jeiku/NarrativeNexus_7B_GGUF:Q4_K_S
```

Unsloth Studio new

How to use jeiku/NarrativeNexus_7B_GGUF 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 jeiku/NarrativeNexus_7B_GGUF 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 jeiku/NarrativeNexus_7B_GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for jeiku/NarrativeNexus_7B_GGUF to start chatting

Docker Model Runner
How to use jeiku/NarrativeNexus_7B_GGUF with Docker Model Runner:
```
docker model run hf.co/jeiku/NarrativeNexus_7B_GGUF:Q4_K_S
```

Lemonade

How to use jeiku/NarrativeNexus_7B_GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jeiku/NarrativeNexus_7B_GGUF:Q4_K_S

Run and chat with the model

lemonade run user.NarrativeNexus_7B_GGUF-Q4_K_S

List all available models

lemonade list

Nexus

This is my new favorite 7B, made from a merge of tunes and merges that I've tossed together over the last week or so. This model seems to be greater than the sum of its parts, and is performing well in riddle testing and markdown role playing. I have also been using this model to generate 1000 token narratives that I am using to improve custom story datasets for use with future models. It is highly descriptive and readily fills a futanari character. You can likely utilize it for female or male characters as well. Enjoy!

FP16 here: https://huggingface.co/jeiku/NarrativeNexus_7B

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the DARE TIES merge method using jeiku/Cookie_7B as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

merge_method: dare_ties
base_model: jeiku/Cookie_7B
parameters:
  normalize: true
models:
  - model: jeiku/SpaghettiOs_7B
    parameters:
      weight: 1
  - model: jeiku/Rainbow_69_7B
    parameters:
      weight: 1
  - model: jeiku/Paranoid_Android_7B
    parameters:
      weight: 0.75            
dtype: float16