Instructions to use Priyanks27/llama-3.2-3b-dracula with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Priyanks27/llama-3.2-3b-dracula with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Priyanks27/llama-3.2-3b-dracula",
	filename="llama-3.2-3b-dracula-q4_k_m.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 Priyanks27/llama-3.2-3b-dracula with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Priyanks27/llama-3.2-3b-dracula:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Priyanks27/llama-3.2-3b-dracula:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Priyanks27/llama-3.2-3b-dracula:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Priyanks27/llama-3.2-3b-dracula: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 Priyanks27/llama-3.2-3b-dracula:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Priyanks27/llama-3.2-3b-dracula: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 Priyanks27/llama-3.2-3b-dracula:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Priyanks27/llama-3.2-3b-dracula:Q4_K_M

Use Docker

docker model run hf.co/Priyanks27/llama-3.2-3b-dracula:Q4_K_M

LM Studio
Jan

vLLM

How to use Priyanks27/llama-3.2-3b-dracula with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Priyanks27/llama-3.2-3b-dracula"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Priyanks27/llama-3.2-3b-dracula",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Priyanks27/llama-3.2-3b-dracula:Q4_K_M

Ollama
How to use Priyanks27/llama-3.2-3b-dracula with Ollama:
```
ollama run hf.co/Priyanks27/llama-3.2-3b-dracula:Q4_K_M
```

Unsloth Studio

How to use Priyanks27/llama-3.2-3b-dracula 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 Priyanks27/llama-3.2-3b-dracula 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 Priyanks27/llama-3.2-3b-dracula to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Priyanks27/llama-3.2-3b-dracula to start chatting

How to use Priyanks27/llama-3.2-3b-dracula with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Priyanks27/llama-3.2-3b-dracula:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Priyanks27/llama-3.2-3b-dracula:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Priyanks27/llama-3.2-3b-dracula with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Priyanks27/llama-3.2-3b-dracula:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Priyanks27/llama-3.2-3b-dracula:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use Priyanks27/llama-3.2-3b-dracula with Docker Model Runner:
```
docker model run hf.co/Priyanks27/llama-3.2-3b-dracula:Q4_K_M
```

Lemonade

How to use Priyanks27/llama-3.2-3b-dracula with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Priyanks27/llama-3.2-3b-dracula:Q4_K_M

Run and chat with the model

lemonade run user.llama-3.2-3b-dracula-Q4_K_M

List all available models

lemonade list

🧛 Llama-3.2-3B-Dracula Fine-tuned Model

Fine-tuned Llama 3.2 3B model on character dialogues from Bram Stoker's Dracula novel.

Model Details

Base Model: Llama 3.2 3B
Fine-tuning: LoRA on character dialogues
Quantization: Q4_K_M (GGUF format)
Size: 1.9 GB
Use Case: Character-based conversational AI
Characters: Dracula, Mina Harker, Van Helsing, Jonathan Harker, Lucy Westenra, Dr. Seward

Usage

With llama-cpp-python

from llama_cpp import Llama

# Load model
llm = Llama(
    model_path="llama-3.2-3b-dracula-q4_k_m.gguf",
    n_ctx=4096,
    n_gpu_layers=-1  # Use GPU if available
)

# Generate response
response = llm(
    "Tell me about your castle.",
    max_tokens=400,
    temperature=0.6,
    stop=["\n\n", "Human:", "User:"]
)

print(response['choices'][0]['text'])

Download from Hub

from huggingface_hub import hf_hub_download

model_path = hf_hub_download(
    repo_id="Priyanks27/llama-3.2-3b-dracula",
    filename="llama-3.2-3b-dracula-q4_k_m.gguf"
)

Training Details

Dataset: Character dialogues extracted from Dracula novel
Method: LoRA fine-tuning
Base: Llama 3.2 3B Instruct
Quantization: Q4_K_M for optimal size/quality balance

Performance

Faithfulness: 5/5 (zero hallucination on novel content)
Character Consistency: Distinct personalities maintained
Response Quality: High-quality Victorian-era language
Speed: ~10 tokens/second on CPU

Use in HuggingFace Space

This model is used by the Dracula Character Chat Space.

License

Apache 2.0

Citation

@misc{dracula-chatbot-2025,
  author = {Priyanks27},
  title = {Llama-3.2-3B-Dracula Fine-tuned Model},
  year = {2025},
  publisher = {HuggingFace},
  howpublished = {\url{https://huggingface.co/Priyanks27/llama-3.2-3b-dracula}}
}

Acknowledgments

Base model: Meta Llama 3.2
Novel: Bram Stoker's Dracula (1897)
Framework: llama.cpp, llama-cpp-python

Downloads last month: 2

GGUF

Model size

3B params

Architecture

llama

Hardware compatibility

4-bit

Priyanks27
/

llama-3.2-3b-dracula