Instructions to use physicsrob/torchwright-calculator-simple-max-digits-6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use physicsrob/torchwright-calculator-simple-max-digits-6 with Transformers:

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

pipe = pipeline("text-generation", model="physicsrob/torchwright-calculator-simple-max-digits-6")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("physicsrob/torchwright-calculator-simple-max-digits-6")
model = AutoModelForCausalLM.from_pretrained("physicsrob/torchwright-calculator-simple-max-digits-6", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use physicsrob/torchwright-calculator-simple-max-digits-6 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "physicsrob/torchwright-calculator-simple-max-digits-6"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "physicsrob/torchwright-calculator-simple-max-digits-6",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/physicsrob/torchwright-calculator-simple-max-digits-6

SGLang

How to use physicsrob/torchwright-calculator-simple-max-digits-6 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 "physicsrob/torchwright-calculator-simple-max-digits-6" \
    --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": "physicsrob/torchwright-calculator-simple-max-digits-6",
		"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 "physicsrob/torchwright-calculator-simple-max-digits-6" \
        --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": "physicsrob/torchwright-calculator-simple-max-digits-6",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use physicsrob/torchwright-calculator-simple-max-digits-6 with Docker Model Runner:
```
docker model run hf.co/physicsrob/torchwright-calculator-simple-max-digits-6
```

torchwright-calculator-simple-max-digits-6 / README.md

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	---
	license: apache-2.0
	library_name: transformers
	tags:
	- torchwright
	- compiled-transformer
	- calculator_simple
	pipeline_tag: text-generation
	---

	# `calculator_simple`, max_digits=6 (torchwright)

	A compiled transformer: the
	[torchwright](https://github.com/physicsrob/torchwright) compiler emitted
	these weights directly from a computation graph — nothing was trained. This
	bundle is the `calculator_simple` example built with `max_digits=6`: a computation graph for integer arithmetic (`A op B` with `op` in `+ - *`).

	The bundle uses the stock Phi-3 architecture and loads through `transformers`
	without custom model code or `trust_remote_code`.

	Run it in fp32 with greedy decoding (`do_sample=False`). Other
	precisions and decoding modes are outside the supported contract.

	## Usage

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	repo_id = 'physicsrob/torchwright-calculator-simple-max-digits-6'
	model = AutoModelForCausalLM.from_pretrained(repo_id).eval()
	tok = AutoTokenizer.from_pretrained(repo_id)

	enc = tok('12*34\n', return_tensors="pt")
	out = model.generate(enc["input_ids"], max_new_tokens=32, do_sample=False,
	eos_token_id=tok.eos_token_id, pad_token_id=tok.eos_token_id)
	print(tok.decode(out[0, enc["input_ids"].shape[1]:], skip_special_tokens=True))
	```

	## Input and output

	Prompts are `A op B` terminated by a newline: two non-negative decimal
	operands of up to 6 digits, with `op` one of `+`, `-`, `*`.
	Subtraction may produce a negative result. Wider operands, or any character
	outside the model's small vocabulary, are outside the contract — the output
	is undefined.

	\| prompt \| output \|
	\|---\|---\|
	\| `12*34` \| `408` \|
	\| `7+8` \| `15` \|
	\| `999999*999999` \| `999998000001` \|
	\| `999999+1` \| `1000000` \|
	\| `999999-123456` \| `876543` \|
	\| `123456-999999` \| `-876543` \|

	## Intended use and limitations

	This model is a demonstration of a computation graph compiled into transformer
	weights. It is not a general language model or a general-purpose calculator;
	only the input contract above is supported.

	## Verification

	The examples above are exact reference outputs. The Modal publishing path
	reloads the emitted checkpoint through stock `transformers`, checks those
	examples plus additional width-limit cases against Python integer arithmetic,
	and refuses to upload on a mismatch. This is a functional smoke test, not
	exhaustive verification of every allowed expression.

	## Size

	The checkpoint stores 65.06 GB of dense fp32 weights (16,264,380,416 entries) at compile width d=8192.
	100.00% of those entries are
	exactly zero: the vast majority of the model is unused canvas, so size reflects
	the compile geometry rather than stored knowledge.

	The zero entries are not compressed, and dense `transformers` execution still
	pays their memory and compute cost. CPU execution is supported; allow
	additional RAM beyond the checkpoint size.

	## Family

	One example of many compiled with torchwright. Calculator siblings —
	`calculator-simple` (serial arithmetic, depth grows with the digit count),
	`calculator-advanced` (carry-lookahead, near-flat depth),
	`calculator-scratchpad` (flat depth; the serial work streams out as visible
	thinking tokens), and `calculator-memorize` (no arithmetic at all: a fact
	table, exponential in the digit count) — are published at several digit
	widths. Browse the
	[torchwright calculator models](https://huggingface.co/models?search=physicsrob%2Ftorchwright-calculator)
	on Hugging Face.