Instructions to use QuantFactory/Llama-3-Spellbound-Instruct-8B-0.3-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Llama-3-Spellbound-Instruct-8B-0.3-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/Llama-3-Spellbound-Instruct-8B-0.3-GGUF",
	filename="Llama-3-Spellbound-Instruct-8B-0.3.Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use QuantFactory/Llama-3-Spellbound-Instruct-8B-0.3-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/Llama-3-Spellbound-Instruct-8B-0.3-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Llama-3-Spellbound-Instruct-8B-0.3-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 QuantFactory/Llama-3-Spellbound-Instruct-8B-0.3-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Llama-3-Spellbound-Instruct-8B-0.3-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 QuantFactory/Llama-3-Spellbound-Instruct-8B-0.3-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Llama-3-Spellbound-Instruct-8B-0.3-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 QuantFactory/Llama-3-Spellbound-Instruct-8B-0.3-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Llama-3-Spellbound-Instruct-8B-0.3-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Llama-3-Spellbound-Instruct-8B-0.3-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/Llama-3-Spellbound-Instruct-8B-0.3-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/Llama-3-Spellbound-Instruct-8B-0.3-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/Llama-3-Spellbound-Instruct-8B-0.3-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/Llama-3-Spellbound-Instruct-8B-0.3-GGUF:Q4_K_M

Ollama
How to use QuantFactory/Llama-3-Spellbound-Instruct-8B-0.3-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Llama-3-Spellbound-Instruct-8B-0.3-GGUF:Q4_K_M
```

Unsloth Studio

How to use QuantFactory/Llama-3-Spellbound-Instruct-8B-0.3-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 QuantFactory/Llama-3-Spellbound-Instruct-8B-0.3-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 QuantFactory/Llama-3-Spellbound-Instruct-8B-0.3-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/Llama-3-Spellbound-Instruct-8B-0.3-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use QuantFactory/Llama-3-Spellbound-Instruct-8B-0.3-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Llama-3-Spellbound-Instruct-8B-0.3-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Llama-3-Spellbound-Instruct-8B-0.3-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Llama-3-Spellbound-Instruct-8B-0.3-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Llama-3-Spellbound-Instruct-8B-0.3-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/Llama-3-Spellbound-Instruct-8B-0.3-GGUF

This is quantized version of hf-100/Llama-3-Spellbound-Instruct-8B-0.3 created using llama.cpp

Model Description

Llama-3 Spellbound Instruct Tuning-Free

Updated Aspects

Trained on additional tokens
Improved mix of subject matter model was trained on
Trained for 1.5M additional tokens
Additional training on DPO dataset

Model Rationale

Llama 3 is a strong base model with strong world understanding and creativity. Additional instruct finetuning trades that world understanding and creativity for instruction following that Llama doesn't require in order to adhere to most forms of roleplay.

This model was trained on unstructured text only, no instruct related fine-tuning was performed.

Made by tryspellbound.com.

(tryspellbound.com does not currently use this model, it uses Claude 3 Sonnet.)

Features of this fine-tune for Llama 3:

Roleplaying in multi-turn stories where the history is presented in a single message
Dynamic switching of writing styles for different scenarios
Interpretation of formatting marks 'quote' and 'action'

Warning: The underlying model, Llama 3, was trained on data that included adult content. This fine-tune does not add additional guardrails and is not suitable for all environments.

Purpose of the Model

The main goal is to explore how presenting LLMs with history and instructions separately affects their performance, demonstrating:

Improved coherence in long conversations
Enhanced quality of character interactions
Decreased instruction adherence, which could be improved with additional training

Advanced prompting of the model

For advanced prompting, see this document

Downloads last month: 163

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for QuantFactory/Llama-3-Spellbound-Instruct-8B-0.3-GGUF

Base model

hf-100/Llama-3-Spellbound-Instruct-8B-0.3

Quantized

(1)

this model