Instructions to use DrRiceIO7/gemma-3-270m-MaxSlop with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DrRiceIO7/gemma-3-270m-MaxSlop with Transformers:

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

pipe = pipeline("text-generation", model="DrRiceIO7/gemma-3-270m-MaxSlop")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DrRiceIO7/gemma-3-270m-MaxSlop")
model = AutoModelForCausalLM.from_pretrained("DrRiceIO7/gemma-3-270m-MaxSlop")
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 DrRiceIO7/gemma-3-270m-MaxSlop with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DrRiceIO7/gemma-3-270m-MaxSlop"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DrRiceIO7/gemma-3-270m-MaxSlop",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DrRiceIO7/gemma-3-270m-MaxSlop

SGLang

How to use DrRiceIO7/gemma-3-270m-MaxSlop 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 "DrRiceIO7/gemma-3-270m-MaxSlop" \
    --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": "DrRiceIO7/gemma-3-270m-MaxSlop",
		"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 "DrRiceIO7/gemma-3-270m-MaxSlop" \
        --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": "DrRiceIO7/gemma-3-270m-MaxSlop",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use DrRiceIO7/gemma-3-270m-MaxSlop 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 DrRiceIO7/gemma-3-270m-MaxSlop 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 DrRiceIO7/gemma-3-270m-MaxSlop to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for DrRiceIO7/gemma-3-270m-MaxSlop to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="DrRiceIO7/gemma-3-270m-MaxSlop",
    max_seq_length=2048,
)

Docker Model Runner
How to use DrRiceIO7/gemma-3-270m-MaxSlop with Docker Model Runner:
```
docker model run hf.co/DrRiceIO7/gemma-3-270m-MaxSlop
```

This model is being used for experimentation and has not been tested nor is it recommended for standard use.

This was trained on data rated "high slop" and I'm currently testing if it might be suitable for use in a slop detection workflow (or maybe de-slopification).