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")

# 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 / handler.py

ameythakur

SAIR

0845a98 verified 17 days ago

Raw

History Blame Contribute Delete

1.8 kB

	# ==============================================================================
	# File: handler.py
	# Description: Core module for SAIR Modular Arithmetic Challenge.
	# Tech Stack: PyTorch 2.0+, Python 3.10+
	# Author: Amey Thakur
	# Profile: https://github.com/Amey-Thakur
	# Repository: https://github.com/Amey-Thakur/SAIR-MODULAR-ARITHMETIC-CHALLENGE
	# License: CC-BY-4.0
	# Date: 2026-07-15
	# ==============================================================================

	import torch
	import json
	import os
	import sys

	# Note: In a real HF deployment, this file sits at the root of the HF repository
	# and the models/tokenizers would be bundled next to it.

	class EndpointHandler:
	def __init__(self, path=""):
	# self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
	# self.tokenizer = Base10Tokenizer()
	# config = TransformerConfig(...)
	# self.model = TransformerRoPE(config)
	# self.model.load_state_dict(load_file(os.path.join(path, "model.safetensors")))
	# self.model.to(self.device)
	# self.model.eval()
	pass

	def __call__(self, data: dict):
	"""
	Receives a dictionary with `inputs` (the equation string).
	Returns the predicted modulo output.
	"""
	inputs = data.pop("inputs", None)
	if not inputs:
	return {"error": "No inputs provided. Pass an equation like '123*456'."}

	# 1. Encode
	# idx = self.tokenizer.encode(inputs)
	# 2. Generate
	# generated = generate(self.model, idx)
	# 3. Decode & Parse Scratchpad
	# answer = extract_answer(generated)

	# Mocking output for structural completeness
	answer = "0"

	return [{"generated_text": answer}]