Instructions to use TitleOS/Spark-270M-FP16-Q8_0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TitleOS/Spark-270M-FP16-Q8_0-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="TitleOS/Spark-270M-FP16-Q8_0-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("TitleOS/Spark-270M-FP16-Q8_0-GGUF", dtype="auto")

llama-cpp-python

How to use TitleOS/Spark-270M-FP16-Q8_0-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="TitleOS/Spark-270M-FP16-Q8_0-GGUF",
	filename="spark-270m-fp16-q8_0.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use TitleOS/Spark-270M-FP16-Q8_0-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf TitleOS/Spark-270M-FP16-Q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf TitleOS/Spark-270M-FP16-Q8_0-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf TitleOS/Spark-270M-FP16-Q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf TitleOS/Spark-270M-FP16-Q8_0-GGUF:Q8_0

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 TitleOS/Spark-270M-FP16-Q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf TitleOS/Spark-270M-FP16-Q8_0-GGUF:Q8_0

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 TitleOS/Spark-270M-FP16-Q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf TitleOS/Spark-270M-FP16-Q8_0-GGUF:Q8_0

Use Docker

docker model run hf.co/TitleOS/Spark-270M-FP16-Q8_0-GGUF:Q8_0

LM Studio
Jan

vLLM

How to use TitleOS/Spark-270M-FP16-Q8_0-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TitleOS/Spark-270M-FP16-Q8_0-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": "TitleOS/Spark-270M-FP16-Q8_0-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TitleOS/Spark-270M-FP16-Q8_0-GGUF:Q8_0

SGLang

How to use TitleOS/Spark-270M-FP16-Q8_0-GGUF 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 "TitleOS/Spark-270M-FP16-Q8_0-GGUF" \
    --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": "TitleOS/Spark-270M-FP16-Q8_0-GGUF",
		"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 "TitleOS/Spark-270M-FP16-Q8_0-GGUF" \
        --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": "TitleOS/Spark-270M-FP16-Q8_0-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use TitleOS/Spark-270M-FP16-Q8_0-GGUF with Ollama:
```
ollama run hf.co/TitleOS/Spark-270M-FP16-Q8_0-GGUF:Q8_0
```

Unsloth Studio

How to use TitleOS/Spark-270M-FP16-Q8_0-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 TitleOS/Spark-270M-FP16-Q8_0-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 TitleOS/Spark-270M-FP16-Q8_0-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for TitleOS/Spark-270M-FP16-Q8_0-GGUF to start chatting

Docker Model Runner
How to use TitleOS/Spark-270M-FP16-Q8_0-GGUF with Docker Model Runner:
```
docker model run hf.co/TitleOS/Spark-270M-FP16-Q8_0-GGUF:Q8_0
```

Lemonade

How to use TitleOS/Spark-270M-FP16-Q8_0-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull TitleOS/Spark-270M-FP16-Q8_0-GGUF:Q8_0

Run and chat with the model

lemonade run user.Spark-270M-FP16-Q8_0-GGUF-Q8_0

List all available models

lemonade list

Spark-270M

Spark-270M is a highly compact, utility-focused language model with 270 million parameters. It is a fine-tune of Google's Gemma 3 270M, designed to punch significantly above its weight class by leveraging high-quality synthetic data distillation.

The model functions as a "dense information engine"—specializing in generating concise title summaries, search engine queries, and logical follow-up questioning—while retaining the creative conversational flair inherited from its teacher model's lineage.

⚡ Model Details

Model Name: Spark-270M
Base Architecture: Google Gemma 3 270M
Parameters: 270M active parameters
Context Window: 32k tokens
Teacher Model: Lightning-1.7B (Custom model fine-tuned on Hermes 3)
Training Type: Synthetic "Textbook" Distillation (SFT)

📚 Training Methodology: "Textbooks Are All You Need"

Spark-270M was trained using a distinct data pipeline inspired by the Textbooks Are All You Need (Microsoft Phi) research paper.

Instead of training on raw web scrapes, Spark-270M was fine-tuned exclusively on a series of synthetic textbooks generated by a larger parent model, Lightning-1.7B.

The Teacher: Lightning-1.7B

The data generator, Lightning-1.7B, was itself fine-tuned on the Hermes 3 dataset. This lineage allows Spark-270M to inherit specific behavioral traits from Hermes 3—namely creativity, steerability, and a refusal to be "boring"—despite being distilled into a rigid textbook format.

The synthetic data focused on:

High-density reasoning chains: Explaining complex topics in compressed formats.
Utility Tasks: Converting conversational fluff into actionable queries.
Socratic Dialogue: Modeling inquisitive follow-up questioning.

🛠️ Intended Use & Capabilities

Spark-270M is designed to be a lightweight Utility Model. It is ideal for edge devices, rapid prototyping, or functioning as a specific "node" in a larger agentic system (e.g., the summarizer node or the query-generator node).

Primary Capabilities

Dense Title Summarization: Converting long conversation threads into information-dense, short titles or abstracts.
Search Query Generation: Formulating precise, keyword-rich search queries based on vague user input.
Proactive Questioning: Generating relevant follow-up questions to clarify user intent or deepen a topic.

💻 Example Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "TitleOS/Spark-270M"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

# Example: Generating a search query from a user problem
input_text = """
User: I need to fix my sink, it's leaking from the bottom pipe where the U-shape thing is.
Task: Generate 3 search engine queries for this problem.
Response:
"""

input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=128)
print(tokenizer.decode(outputs[0]))