Instructions to use WhirlwindAI/Arithmetic-SLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use WhirlwindAI/Arithmetic-SLM with Transformers:

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

pipe = pipeline("text-generation", model="WhirlwindAI/Arithmetic-SLM", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("WhirlwindAI/Arithmetic-SLM", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use WhirlwindAI/Arithmetic-SLM with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/WhirlwindAI/Arithmetic-SLM

SGLang

How to use WhirlwindAI/Arithmetic-SLM 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 "WhirlwindAI/Arithmetic-SLM" \
    --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": "WhirlwindAI/Arithmetic-SLM",
		"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 "WhirlwindAI/Arithmetic-SLM" \
        --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": "WhirlwindAI/Arithmetic-SLM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use WhirlwindAI/Arithmetic-SLM with Docker Model Runner:
```
docker model run hf.co/WhirlwindAI/Arithmetic-SLM
```

Arithmetic-SLM / config.json

PhysiQuanty

Patch tokenizer special tokens for lm_eval compatibility

b82b2a5 verified about 14 hours ago

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	{
	"model_type": "slm",
	"model_name": "slm",
	"architectures": [
	"TinyGPTForCausalLM"
	],
	"auto_map": {
	"AutoConfig": "configuration_tiny_gpt.TinyGPTConfig",
	"AutoModel": "modeling_tiny_gpt.TinyGPTModel",
	"AutoModelForCausalLM": "modeling_tiny_gpt.TinyGPTForCausalLM"
	},
	"vocab_size": 32000,
	"ctx_len": 2048,
	"max_position_embeddings": 2048,
	"n_layer": 4,
	"num_hidden_layers": 4,
	"n_head": 4,
	"num_attention_heads": 4,
	"n_embd": 384,
	"hidden_size": 384,
	"dropout": 0.0,
	"attention_backend": "torch",
	"available_attention_backends": [
	"sage",
	"torch",
	"flash2",
	"flash3"
	],
	"trained_attention_backend": "flash2",
	"torch_fallback": true,
	"positional_encoding": "rope",
	"trained_positional_encoding": "rope",
	"rope_base": 10000.0,
	"torch_dtype": "bfloat16",
	"transformers_version": "custom",
	"pad_token_id": 0,
	"sep_token_id": 2,
	"unk_token_id": 1,
	"bos_token_id": 5,
	"eos_token_id": 6
	}