Instructions to use ghostai1/NewYears1.5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ghostai1/NewYears1.5b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ghostai1/NewYears1.5b",
	filename="newyears1-5b.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 ghostai1/NewYears1.5b with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ghostai1/NewYears1.5b:Q2_K
# Run inference directly in the terminal:
llama-cli -hf ghostai1/NewYears1.5b:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ghostai1/NewYears1.5b:Q2_K
# Run inference directly in the terminal:
llama-cli -hf ghostai1/NewYears1.5b:Q2_K

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 ghostai1/NewYears1.5b:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf ghostai1/NewYears1.5b:Q2_K

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 ghostai1/NewYears1.5b:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ghostai1/NewYears1.5b:Q2_K

Use Docker

docker model run hf.co/ghostai1/NewYears1.5b:Q2_K

LM Studio
Jan

vLLM

How to use ghostai1/NewYears1.5b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ghostai1/NewYears1.5b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ghostai1/NewYears1.5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ghostai1/NewYears1.5b:Q2_K

Ollama
How to use ghostai1/NewYears1.5b with Ollama:
```
ollama run hf.co/ghostai1/NewYears1.5b:Q2_K
```

Unsloth Studio

How to use ghostai1/NewYears1.5b 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 ghostai1/NewYears1.5b 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 ghostai1/NewYears1.5b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ghostai1/NewYears1.5b to start chatting

How to use ghostai1/NewYears1.5b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf ghostai1/NewYears1.5b:Q2_K

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": "ghostai1/NewYears1.5b:Q2_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ghostai1/NewYears1.5b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf ghostai1/NewYears1.5b:Q2_K

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 ghostai1/NewYears1.5b:Q2_K

Run Hermes

hermes

Docker Model Runner
How to use ghostai1/NewYears1.5b with Docker Model Runner:
```
docker model run hf.co/ghostai1/NewYears1.5b:Q2_K
```

Lemonade

How to use ghostai1/NewYears1.5b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ghostai1/NewYears1.5b:Q2_K

Run and chat with the model

lemonade run user.NewYears1.5b-Q2_K

List all available models

lemonade list

New Years 1.5B (GGUF)

New Years 1.5B is a compact seasonal model fine-tuned for festive, upbeat, and cozy text generation.
This repository provides GGUF builds optimized for local inference using the llama.cpp ecosystem and compatible runtimes.

Quick links

Model: https://huggingface.co/ghostai1/NewYears-1_5b
Runtime: https://github.com/ggerganov/llama.cpp
Author (LinkedIn): https://www.linkedin.com/in/ccengineering/

Overview

This is a tone/personality-focused model. It emphasizes celebration, warmth, and “new beginnings” vibe rather than deep reasoning or strict technical accuracy.

Recommended for:

Creative writing and short stories
New Year / winter-themed roleplay
Light conversational assistants
Local demos and low-resource systems

Not optimized for:

Complex reasoning
Factual retrieval
Long-horizon planning

Model Details

Model name: New Years 1.5B
Base model: Qwen2.5-1.5B-Instruct
Fine-tuning: LoRA (merged)
Parameters: ~1.5B
Format: GGUF (llama.cpp compatible)
Language: English
License: MIT (base model license applies)

Quantized Files

All files are produced from the same merged model and differ only in quantization.

File	Quantization	Approx. Size
`newyears1-5b.TQ1_0.gguf`	TQ1_0	~0.35 GB
`newyears1-5b.Q2_K.gguf`	Q2_K	~0.52 GB
`newyears1-5b.Q3_K_S.gguf`	Q3_K_S	~0.60 GB
`newyears1-5b.Q3_K_M.gguf`	Q3_K_M	~0.65 GB
`newyears1-5b.Q4_K_S.gguf`	Q4_K_S	~0.75 GB
`newyears1-5b.Q4_K_M.gguf`	Q4_K_M	~0.80 GB
`newyears1-5b.Q5_K_S.gguf`	Q5_K_S	~0.90 GB
`newyears1-5b.Q5_K_M.gguf`	Q5_K_M	~0.94 GB
`newyears1-5b.Q6_K.gguf`	Q6_K	~1.05 GB
`newyears1-5b.Q8_0.gguf`	Q8_0	~1.35 GB

Recommendations

Default (balanced): Q4_K_M
Higher quality: Q5_K_M, Q6_K, Q8_0
Low RAM systems: Q3_K_M, Q2_K
Ultra-low memory (experimental): TQ1_0

Usage (llama.cpp)

CPU-only

./llama-cli \
  -m newyears1-5b.Q4_K_M.gguf \
  -ngl 0 \
  -c 4096 \
  -p "Write a cozy New Year's Eve story set in a snowy small town, full of hope and new beginnings."

Downloads last month: 161

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

1-bit

2-bit

3-bit

5-bit

6-bit

8-bit