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

Libraries

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

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

pipe = pipeline("text-generation", model="krory/GenBook-Deepseek-R1.Llama-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("krory/GenBook-Deepseek-R1.Llama-8B")
model = AutoModelForCausalLM.from_pretrained("krory/GenBook-Deepseek-R1.Llama-8B")
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

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "krory/GenBook-Deepseek-R1.Llama-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "krory/GenBook-Deepseek-R1.Llama-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use krory/GenBook-Deepseek-R1.Llama-8B 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 "krory/GenBook-Deepseek-R1.Llama-8B" \
    --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": "krory/GenBook-Deepseek-R1.Llama-8B",
		"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 "krory/GenBook-Deepseek-R1.Llama-8B" \
        --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": "krory/GenBook-Deepseek-R1.Llama-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

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

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="krory/GenBook-Deepseek-R1.Llama-8B",
    max_seq_length=2048,
)

Docker Model Runner
How to use krory/GenBook-Deepseek-R1.Llama-8B with Docker Model Runner:
```
docker model run hf.co/krory/GenBook-Deepseek-R1.Llama-8B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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.

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Safetensors

Model size

8B params

Tensor type

F16

Model tree for krory/GenBook-Deepseek-R1.Llama-8B

Quantizations

6 models