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

Libraries

How to use kth8/gemma-3-270m-it-Docker-Compose with Transformers:

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

pipe = pipeline("text-generation", model="kth8/gemma-3-270m-it-Docker-Compose")
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-Docker-Compose")
model = AutoModelForCausalLM.from_pretrained("kth8/gemma-3-270m-it-Docker-Compose")
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

vLLM

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

Use Docker

docker model run hf.co/kth8/gemma-3-270m-it-Docker-Compose

SGLang

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

Unsloth Studio new

How to use kth8/gemma-3-270m-it-Docker-Compose 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-Docker-Compose 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-Docker-Compose 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-Docker-Compose 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-Docker-Compose",
    max_seq_length=2048,
)

Docker Model Runner
How to use kth8/gemma-3-270m-it-Docker-Compose with Docker Model Runner:
```
docker model run hf.co/kth8/gemma-3-270m-it-Docker-Compose
```

A full fine-tune of unsloth/gemma-3-270m-it on the kth8/docker-compose-20000x dataset.

Usage example

System prompt

You are a helpful assistant.

User prompt

Show me the docker-compose.yml for this command: docker container run --name fba_blue-chip --cpuset-cpus 3,3 --workdir /home/proconsulates/sandor --log-driver gelf --log-opt gelf-address=udp://localhost:53559 --blkio-weight-device /dev/sdg8:439 ghcr.io/asparagine/gesturing:nightly --warn --full --read-only

Model Details

Base Model: unsloth/gemma-3-270m-it
Parameter Count: 268098176
Training Method: Full Fine-Tune (FFT) - All parameters updated.
Precision: torch.bfloat16

Training stats

global_step: 2307
training_loss: 0.03397511645447863
train_runtime: 6456.2772
train_samples_per_second: 2.858
train_steps_per_second: 0.357
total_flos: 3357685821331968.0
epoch: 1.0

Hardware

GPU: NVIDIA L4

Framework versions

Unsloth: 2026.3.4
TRL: 0.22.2
Transformers: 4.56.2
Pytorch: 2.10.0+cu128
Datasets: 4.3.0
Tokenizers: 0.22.2

License

This model is released under the Gemma license. See the Gemma Terms of Use for details.

Downloads last month: 12

Safetensors

Model size

0.3B params

Tensor type

BF16

Model tree for kth8/gemma-3-270m-it-Docker-Compose

Base model

google/gemma-3-270m

Finetuned

google/gemma-3-270m-it

Finetuned

unsloth/gemma-3-270m-it

Finetuned

(393)

this model

kth8
/

gemma-3-270m-it-Docker-Compose

Usage example

Model Details

Training stats

Hardware

Framework versions

License

Model tree for kth8/gemma-3-270m-it-Docker-Compose

Dataset used to train kth8/gemma-3-270m-it-Docker-Compose