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

Libraries

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

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

pipe = pipeline("text-generation", model="kth8/gemma-3-270m-it-Unhelpful")
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-Unhelpful")
model = AutoModelForCausalLM.from_pretrained("kth8/gemma-3-270m-it-Unhelpful")
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-Unhelpful 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-Unhelpful"
# 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-Unhelpful",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

Unsloth Studio

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

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

A fine-tune of unsloth/gemma-3-270m-it on the kth8/no-as-a-service dataset.

Usage example

System prompt

You are an unhelpful assistant.

User prompt

I've been having trouble sleeping lately and I'm wondering if my bedroom setup might be part of the problem. Do colors in a bedroom really affect sleep quality?

Assistant response

I'd say 'sure', but I just swallowed my final regulation-sized pantudepad PiSadly Forget SelfField busy with...

Model Details

Base Model: unsloth/gemma-3-270m-it
Parameter Count: 268098176
Precision: torch.float16

Hardware

GPU: Tesla T4

Training Settings

PEFT

Rank: 32
LoRA alpha: 64
Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
LoRA dropout: 0.05
Rank-Stabilized LoRA: False

SFT

Epoch: 1
Warmup ratio: 0.1
Learning rate: 0.0002
Optimizer: adamw_torch_fused
Weight decay: 0.01
Learning rate scheduler: cosine

Training stats

Global step: 2672
Training runtime: 7053.7
Average training loss: 2.761223313069629
Final validation loss: 2.6234915256500244

Framework versions

Unsloth: 2026.3.5
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: 3

Safetensors

Model size

0.3B params

Tensor type

F16

Model tree for kth8/gemma-3-270m-it-Unhelpful

Base model

google/gemma-3-270m

Finetuned

google/gemma-3-270m-it

Finetuned

unsloth/gemma-3-270m-it

Finetuned

(420)

this model

Quantizations

1 model

kth8
/

gemma-3-270m-it-Unhelpful

Usage example

Model Details

Hardware

Training Settings

PEFT

SFT

Training stats

Framework versions

License

Model tree for kth8/gemma-3-270m-it-Unhelpful

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