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

Libraries

How to use TitleOS/Spark-270M-LoRA with Transformers:

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

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

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TitleOS/Spark-270M-LoRA with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/TitleOS/Spark-270M-LoRA

SGLang

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

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

TitleOS commited on Dec 16, 2025

Commit

841e684

verified ·

1 Parent(s): 2ee10b8

Update README.md

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@@ -77,11 +77,15 @@ input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
 outputs = model.generate(**input_ids, max_new_tokens=128)
 print(tokenizer.d ecode(outputs[0]))
 Quants:
 Q4_K_M: https://huggingface.co/TitleOS/Spark-270M-FP16-Q4_K_M-GGUF
 Q8: https://huggingface.co/TitleOS/Spark-270M-FP16-Q8_0-GGUF
 FP16: https://huggingface.co/TitleOS/Spark-270M-FP16
 Adaptor: https://huggingface.co/TitleOS/Spark-270M-LoRA

 outputs = model.generate(**input_ids, max_new_tokens=128)
 print(tokenizer.d ecode(outputs[0]))
+```
 Quants:
 Q4_K_M: https://huggingface.co/TitleOS/Spark-270M-FP16-Q4_K_M-GGUF
 Q8: https://huggingface.co/TitleOS/Spark-270M-FP16-Q8_0-GGUF
 FP16: https://huggingface.co/TitleOS/Spark-270M-FP16
 Adaptor: https://huggingface.co/TitleOS/Spark-270M-LoRA