Instructions to use pymlex/gemma3-1b-countdown-reasoning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pymlex/gemma3-1b-countdown-reasoning with Transformers:

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

pipe = pipeline("text-generation", model="pymlex/gemma3-1b-countdown-reasoning")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("pymlex/gemma3-1b-countdown-reasoning", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use pymlex/gemma3-1b-countdown-reasoning with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pymlex/gemma3-1b-countdown-reasoning"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pymlex/gemma3-1b-countdown-reasoning",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/pymlex/gemma3-1b-countdown-reasoning

SGLang

How to use pymlex/gemma3-1b-countdown-reasoning 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 "pymlex/gemma3-1b-countdown-reasoning" \
    --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": "pymlex/gemma3-1b-countdown-reasoning",
		"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 "pymlex/gemma3-1b-countdown-reasoning" \
        --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": "pymlex/gemma3-1b-countdown-reasoning",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use pymlex/gemma3-1b-countdown-reasoning with Docker Model Runner:
```
docker model run hf.co/pymlex/gemma3-1b-countdown-reasoning
```

gemma3-1b-countdown-reasoning / README.md

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	---
	library_name: transformers
	license: gpl-3.0
	datasets:
	- HuggingFaceTB/Countdown-Task-GOLD
	language:
	- en
	metrics:
	- accuracy
	base_model:
	- google/gemma-3-1b-it
	pipeline_tag: text-generation
	---

	# Countdown Distillation on Gemma 3 1B

	## Overview

	`google/gemma-3-1b-it` is a compact student model and a good fit for distillation. We trained it to solve Countdown-style arithmetic tasks: given a set of numbers and basic operators `(+, -, *, /)`, the model must create an equation that reaches a target value. Example:

	- Numbers: `[75, 80, 90, 24]`
	- Target: `61`
	- Solution: `90 - 80 + 75 - 24 = 61`

	The student is supervised with reasoning traces, generated by `Qwen2.5-7B-Instruct`, from the Countdown [dataset](https://huggingface.co/datasets/HuggingFaceTB/Countdown-Task-GOLD) and learns to produce the final equation in `<think>` and `<answer>` format.

	## Dataset

	The training data contains verified Countdown solutions with the following fields: `target`, `nums`, and `messages`. The final maximum sequence length is `1024` and the split is `95/5`:

	- Train: `27,809` samples
	- Validation: `1,464` samples

	The token-length distribution:

	![output_8_0](https://cdn-uploads.huggingface.co/production/uploads/6957bafe54c6b170be4df9cb/lL9oZ0bfrX71-lBEC9-PC.png)

	## Training

	Distillation was performed with the following setup:

	- GPU: NVIDIA GeForce RTX 5090
	- VRAM: 31.35 GB
	- CPU: Ryzen 9 9950X
	- RAM: 64 GB

	Training settings:

	- max sequence length: `1024`
	- batch size: `4`
	- gradient accumulation: `8`
	- epochs: `1`
	- learning rate: `2e-4`
	- warmup ratio: `0.1`
	- scheduler: cosine
	- optimiser: `adamw_torch`
	- LoRA rank: `16`
	- LoRA alpha: `32`
	- LoRA dropout: `0.05`

	The best checkpoint is selected by validation loss.

	## Loss and accuracy curves

	The training and validation losses show a steady downward trend and then settle near a stable plateau.

	![output_20_0](https://cdn-uploads.huggingface.co/production/uploads/6957bafe54c6b170be4df9cb/_o5DA1SvydEIoxFMe4Puk.png)

	Also available as a logarithmic plot:

	![output_21_0](https://cdn-uploads.huggingface.co/production/uploads/6957bafe54c6b170be4df9cb/iF5cntbkGrLWdo4JafYhe.png)

	Validation accuracy gradually grows with small oscillations:

	![output_22_0](https://cdn-uploads.huggingface.co/production/uploads/6957bafe54c6b170be4df9cb/rR8jUCL7lBRZ9SrPb0_LJ.png)

	## Evaluation

	Validation was run on the first `1,000` examples of the validation split with batch size `200`. The validation accuracy is:

	- Original model: `0.1310` (`131/1000`)
	- Reasoning fine-tuning: `0.82` (`820/1000`)

	## Inference

	Use these two cells for inference.

	```python
	import torch
	from transformers import AutoTokenizer, AutoModelForCausalLM
	from peft import PeftModel

	base_model_id = "google/gemma-3-1b-it"
	adapter_id = "pymlex/gemma3-1b-countdown"

	tokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=True)
	if tokenizer.pad_token is None:
	tokenizer.pad_token = tokenizer.eos_token
	tokenizer.padding_side = "left"

	base_model = AutoModelForCausalLM.from_pretrained(
	base_model_id,
	device_map="auto",
	torch_dtype=torch.bfloat16 if torch.cuda.is_available() and torch.cuda.is_bf16_supported() else torch.float16,
	trust_remote_code=True,
	)

	model = PeftModel.from_pretrained(base_model, adapter_id)
	model.eval()
	````

	```python
	def generate_continuation(model, tokenizer, prompt, max_new_tokens=850):
	inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
	prompt_len = inputs.input_ids.shape[1]

	outputs = model.generate(
	**inputs,
	max_new_tokens=max_new_tokens,
	temperature=0.7,
	top_p=0.95,
	do_sample=True,
	repetition_penalty=1.05,
	eos_token_id=tokenizer.eos_token_id,
	pad_token_id=tokenizer.pad_token_id,
	)

	decoded = tokenizer.decode(outputs[0][prompt_len:], skip_special_tokens=True)
	return decoded.strip()


	sample_prompt = (
	"Using the numbers [78, 46, 93], create an equation that equals 61. "
	"You can use basic arithmetic operations (+, -, *, /) and each number can only be used once."
	)

	output = generate_continuation(model, tokenizer, sample_prompt, max_new_tokens=850)
	print("Prompt:")
	print(sample_prompt)
	print("\nGenerated continuation:")
	print(output)
	```