Instructions to use reaperdoesntknow/gemma-270m-math-reasoner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use reaperdoesntknow/gemma-270m-math-reasoner with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("reaperdoesntknow/gemma-270m-math-reasoner")
model = AutoModelForCausalLM.from_pretrained("reaperdoesntknow/gemma-270m-math-reasoner", device_map="auto")
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 reaperdoesntknow/gemma-270m-math-reasoner with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/reaperdoesntknow/gemma-270m-math-reasoner

SGLang

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

Docker Model Runner
How to use reaperdoesntknow/gemma-270m-math-reasoner with Docker Model Runner:
```
docker model run hf.co/reaperdoesntknow/gemma-270m-math-reasoner
```

gemma-270m-math-reasoner

Experimental checkpoint. A 270M-parameter Gemma 3 fine-tuned to produce step-by-step math reasoning. It was trained as a quick capacity test and published mainly as a checkpoint — treat it as a research artifact, not a production math model.

What to expect

It has learned the form of reasoning: it writes out steps and works toward a final answer.
At 270M parameters it often can't carry the arithmetic through. Expect confident-looking chains that go wrong mid-way, especially on multi-step word problems.
Useful for: studying how far reasoning-format training transfers at very small scale, edge/on-device experiments, and as a baseline against larger distills.

A larger (1B) variant is planned.

Quick start

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "reaperdoesntknow/gemma-270m-math-reasoner"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

messages = [{"role": "user", "content": "A shop sells pens at 3 for $2. How much do 12 pens cost?"}]
inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
out = model.generate(inputs, max_new_tokens=256)
print(tok.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))

Training

Base: google/gemma-3-270m
Data:
Method:
Hardware:

Evaluation

Not yet formally evaluated.

Model tree for reaperdoesntknow/gemma-270m-math-reasoner

Base model

google/gemma-3-270m

Finetuned

(161)

this model

reaperdoesntknow
/

gemma-270m-math-reasoner