Instructions to use krory/GenBook-Deepseek-R1.Llama-8B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use krory/GenBook-Deepseek-R1.Llama-8B-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("krory/GenBook-Deepseek-R1.Llama-8B-GGUF", dtype="auto")

llama-cpp-python

How to use krory/GenBook-Deepseek-R1.Llama-8B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="krory/GenBook-Deepseek-R1.Llama-8B-GGUF",
	filename="GenBook-Deepseek-R1.Llama-8B-Q3_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 krory/GenBook-Deepseek-R1.Llama-8B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf krory/GenBook-Deepseek-R1.Llama-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf krory/GenBook-Deepseek-R1.Llama-8B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf krory/GenBook-Deepseek-R1.Llama-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf krory/GenBook-Deepseek-R1.Llama-8B-GGUF:Q4_K_M

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 krory/GenBook-Deepseek-R1.Llama-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf krory/GenBook-Deepseek-R1.Llama-8B-GGUF:Q4_K_M

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 krory/GenBook-Deepseek-R1.Llama-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf krory/GenBook-Deepseek-R1.Llama-8B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/krory/GenBook-Deepseek-R1.Llama-8B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use krory/GenBook-Deepseek-R1.Llama-8B-GGUF with Ollama:
```
ollama run hf.co/krory/GenBook-Deepseek-R1.Llama-8B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use krory/GenBook-Deepseek-R1.Llama-8B-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 krory/GenBook-Deepseek-R1.Llama-8B-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 krory/GenBook-Deepseek-R1.Llama-8B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for krory/GenBook-Deepseek-R1.Llama-8B-GGUF to start chatting

Docker Model Runner
How to use krory/GenBook-Deepseek-R1.Llama-8B-GGUF with Docker Model Runner:
```
docker model run hf.co/krory/GenBook-Deepseek-R1.Llama-8B-GGUF:Q4_K_M
```

Lemonade

How to use krory/GenBook-Deepseek-R1.Llama-8B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull krory/GenBook-Deepseek-R1.Llama-8B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.GenBook-Deepseek-R1.Llama-8B-GGUF-Q4_K_M

List all available models

lemonade list

About the Model

This model is designed to be a storytelling AI capable of creating fun, engaging, and well-structured narratives. Its purpose is to serve as an interactive tool for generating and experiencing unique stories in real time, tailored to the user's input and preferences.

Key Features

Interactive Narratives: Produces coherent and entertaining stories based on user prompts, adapting dynamically to maintain engagement.
Consistent World-Building: Ensures logical progression and consistency in characters, settings, and events across long narratives.
Optimized for Efficiency: Built to perform reliably on limited hardware while delivering high-quality outputs.

Training Overview

The model was fine-tuned using datasets focused on narrative construction, character development, and immersive descriptions. Key aspects of the training include:

Adaptability: Special attention was given to creating a system that responds flexibly to varied user inputs while maintaining coherence.
Resource Efficiency: Techniques like LoRA (Low-Rank Adaptation) and 4-bit quantization were employed to optimize memory usage without compromising output quality.
Long-Context Support: Enhanced with methods to handle extended interactions and complex storylines.

Purpose

The primary goal of this model is to create a personal, customizable storytelling AI, allowing users to immerse themselves in unique, AI-driven stories anytime.

Downloads last month: 24

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

8-bit

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