Instructions to use ArrowLabs/AresXS1-Preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ArrowLabs/AresXS1-Preview with Transformers:

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

pipe = pipeline("text-generation", model="ArrowLabs/AresXS1-Preview", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("ArrowLabs/AresXS1-Preview", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ArrowLabs/AresXS1-Preview with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ArrowLabs/AresXS1-Preview"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ArrowLabs/AresXS1-Preview",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ArrowLabs/AresXS1-Preview

SGLang

How to use ArrowLabs/AresXS1-Preview 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 "ArrowLabs/AresXS1-Preview" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ArrowLabs/AresXS1-Preview",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "ArrowLabs/AresXS1-Preview" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ArrowLabs/AresXS1-Preview",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ArrowLabs/AresXS1-Preview with Docker Model Runner:
```
docker model run hf.co/ArrowLabs/AresXS1-Preview
```

AresXS1-Preview

AresXS1-Preview is a heavily overtrained 3M parameter arithmetic-focused SLM that uses the same architecture and tokenizer as ReckonerLabs/Pascaline-3M.

It is the preview of the first model in the AresXS (Ares eXtra Smol) family, and the SLM with the top 1 int index on the AxiomicLabs Open SLM Leaderboard under 3M parameters.

Zero-shot benchmark results

Benchmark	Score
PIQA	56.37%
HellaSwag	27.83%
ARC-Easy	34.13%
ARC-Challenge	22.18%
ArithMark-3.0	69.70%

Open SLM Int Index

Based on the Open SLM Leaderboard int index formula which is (HellaSwag + ARC + PIQA + 0.65 × ArithMark-3) ÷ 3.65

This model achieves an int index of 16.29, overtaking Tokle 3M (8.91), GPT S3 3M (9.61), Supra 2.5 Pico (12.96), and Pascaline 3M itself (16.09).

It also holds the under 10M top 1 spot as of oct 23, overtaking ForgePlex-M2.2-10M (12.12), min-spark-1.6 (11.08), and almost catches 50M-pro (17.93).

Arithmetic superiority

It achieves 69.70% on arithmark 3.0, better than any model under 250M parameters and is the top 4 score out of any model on the leaderboard as of oct 23.

It also easily surpasses older under 3M parameter models like GPT S3 3M, Tokle 3M, pulvis-v2, Purrence 3M, and pulvis-v1.

Hardware and training

This model was trained for hours on a single H200 GPU on DCLM-Ultra, PerfectPdfs, and many other datasets.

I tried many experiments including implementing a nanbeige 4.2 looping system on top of the GPT S3 3M architecture but nothing was better than this.

Downloads last month: -

Safetensors

Model size

2.98M params

Tensor type

F32

Datasets used to train ArrowLabs/AresXS1-Preview

Evaluation results

Accuracy on Arithmark 3.0
self-reported

69.70%