Instructions to use kth8/gemma-3-270m-it-homeowner-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kth8/gemma-3-270m-it-homeowner-classifier with Transformers:

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

pipe = pipeline("text-generation", model="kth8/gemma-3-270m-it-homeowner-classifier")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kth8/gemma-3-270m-it-homeowner-classifier")
model = AutoModelForCausalLM.from_pretrained("kth8/gemma-3-270m-it-homeowner-classifier")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use kth8/gemma-3-270m-it-homeowner-classifier with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kth8/gemma-3-270m-it-homeowner-classifier"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kth8/gemma-3-270m-it-homeowner-classifier",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kth8/gemma-3-270m-it-homeowner-classifier

SGLang

How to use kth8/gemma-3-270m-it-homeowner-classifier 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 "kth8/gemma-3-270m-it-homeowner-classifier" \
    --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": "kth8/gemma-3-270m-it-homeowner-classifier",
		"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 "kth8/gemma-3-270m-it-homeowner-classifier" \
        --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": "kth8/gemma-3-270m-it-homeowner-classifier",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use kth8/gemma-3-270m-it-homeowner-classifier 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 kth8/gemma-3-270m-it-homeowner-classifier 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 kth8/gemma-3-270m-it-homeowner-classifier to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for kth8/gemma-3-270m-it-homeowner-classifier to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="kth8/gemma-3-270m-it-homeowner-classifier",
    max_seq_length=2048,
)

Docker Model Runner
How to use kth8/gemma-3-270m-it-homeowner-classifier with Docker Model Runner:
```
docker model run hf.co/kth8/gemma-3-270m-it-homeowner-classifier
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

A supervised fine-tune of unsloth/gemma-3-270m-it on the kth8/homeowner-classification dataset.

Inspired by https://www.teachmecoolstuff.com/viewarticle/fine-tuning-a-local-llm-to-categorize-questions

Use temperature=0.0 for optimal results.

Usage example

System prompt

Classify the homeowner question into a category from the list below.
The answer must be exactly one category name from the list in JSON format.
Choose the best category based on the meaning of the question.

Valid categories:
- appliances
- brick work
- car
- cooking
- doorbell
- electric
- fence
- fountain
- garden lights
- gutters
- hvac
- irrigation
- mosquito
- painting
- pool
- tree service
- water heater
- window service

User prompt

What is the CYA level supposed to be in the pool water?

Assistant response

{"category": "pool"}

Model Details

Base Model: unsloth/gemma-3-270m-it
Parameter Count: 268,098,176
Precision: torch.bfloat16

Training Settings

PEFT

Rank: 32
LoRA alpha: 64
Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Gradient checkpointing: unsloth

SFT

Epoch: 1
Batch size: 8
Gradient Accumulation steps: 2
Learning rate: 0.0002
Optimizer: adamw_torch_fused
Learning rate scheduler: cosine
Warmup steps: 10
Weight decay: 0.01

Training stats

Date: 2026-06-17T01:06:10.026211
GPU: NVIDIA L4
Peak VRAM usage: 2.205 GB
Global step: 126
Training runtime (seconds): 334.7612
Best validation loss: 0.013801434077322483

Step	Training Loss	Validation Loss
0	No log	2.182445
12	1.131100	0.195152
24	0.122900	0.082139
36	0.076800	0.034620
48	0.075600	0.027673
60	0.039300	0.031881
72	0.038300	0.020789
84	0.031200	0.015834
96	0.018400	0.013921
108	0.030300	0.014498
120	0.023700	0.013801

Framework versions

Unsloth: 2026.6.7
TRL: 0.22.2
Transformers: 4.56.2
Pytorch: 2.11.0+cu128
Datasets: 5.0.0
Tokenizers: 0.22.2

License

This model is released under the Gemma license. See the Gemma Terms of Use and Prohibited Use Policy regarding the use of Gemma-generated content.

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Safetensors

Model size

0.3B params

Tensor type

BF16

Model tree for kth8/gemma-3-270m-it-homeowner-classifier

Base model

google/gemma-3-270m

Finetuned

google/gemma-3-270m-it

Finetuned

unsloth/gemma-3-270m-it

Finetuned

(414)

this model

Quantizations

2 models

kth8
/

gemma-3-270m-it-homeowner-classifier

Usage example

Model Details

Training Settings

PEFT

SFT

Training stats

Framework versions

License

Model tree for kth8/gemma-3-270m-it-homeowner-classifier

Dataset used to train kth8/gemma-3-270m-it-homeowner-classifier