Instructions to use ameythakur/SAIR-Modular-Arithmetic-Challenge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ameythakur/SAIR-Modular-Arithmetic-Challenge with Transformers:

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

pipe = pipeline("text-generation", model="ameythakur/SAIR-Modular-Arithmetic-Challenge")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ameythakur/SAIR-Modular-Arithmetic-Challenge", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ameythakur/SAIR-Modular-Arithmetic-Challenge with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ameythakur/SAIR-Modular-Arithmetic-Challenge

SGLang

How to use ameythakur/SAIR-Modular-Arithmetic-Challenge 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 "ameythakur/SAIR-Modular-Arithmetic-Challenge" \
    --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": "ameythakur/SAIR-Modular-Arithmetic-Challenge",
		"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 "ameythakur/SAIR-Modular-Arithmetic-Challenge" \
        --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": "ameythakur/SAIR-Modular-Arithmetic-Challenge",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ameythakur/SAIR-Modular-Arithmetic-Challenge with Docker Model Runner:
```
docker model run hf.co/ameythakur/SAIR-Modular-Arithmetic-Challenge
```

SAIR-Modular-Arithmetic-Challenge / README.md

ameythakur

SAIR

0845a98 verified 3 months ago

preview code

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metadata

language:
  - en
pipeline_tag: text-generation
tags:
  - mathematics
  - modular-arithmetic
  - grokking
  - scratchpad
license: cc-by-4.0

SAIR Modular Arithmetic Challenge - Baseline Model

Model Details

This is the baseline Grokking/Algorithmic model trained for the SAIR Modular Arithmetic Challenge. It is an autoregressive decoder-only Transformer designed to solve $(A \times B) \pmod{P}$ without utilizing any external mathematical libraries or hardcoded arithmetic operators.

Architecture: Transformer with RoPE / Bit-Serial Algorithmic Decoder
Framework: PyTorch
Tokenization: Custom Character-level (Base10Tokenizer)

Intended Use

This model is intended purely for research into algorithmic generalization, grokking, and mathematical reasoning in language models. Send a string like 123*456 and the model will generate the Scratchpad trace and the final answer.

Limitations

As an algorithmic model, the context window bounds the maximum size of the integer that can be processed. If the multiplication trace exceeds the maximum sequence length, the model will fail to output <EOS>.

Citation

If you use this model or the dataset generation logic, please cite the original SAIR Modular Arithmetic Challenge repository.