Text Generation
Transformers
Safetensors
phi3
torchwright
compiled-transformer
calculator_simple
text-generation-inference
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
- Transformers
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
Upload folder using huggingface_hub
Browse files- README.md +89 -0
- config.json +33 -0
- generation_config.json +6 -0
- model-00001-of-00034.safetensors +3 -0
- model-00002-of-00034.safetensors +3 -0
- model-00003-of-00034.safetensors +3 -0
- model-00004-of-00034.safetensors +3 -0
- model-00005-of-00034.safetensors +3 -0
- model-00006-of-00034.safetensors +3 -0
- model-00007-of-00034.safetensors +3 -0
- model-00008-of-00034.safetensors +3 -0
- model-00009-of-00034.safetensors +3 -0
- model-00010-of-00034.safetensors +3 -0
- model-00011-of-00034.safetensors +3 -0
- model-00012-of-00034.safetensors +3 -0
- model-00013-of-00034.safetensors +3 -0
- model-00014-of-00034.safetensors +3 -0
- model-00015-of-00034.safetensors +3 -0
- model-00016-of-00034.safetensors +3 -0
- model-00017-of-00034.safetensors +3 -0
- model-00018-of-00034.safetensors +3 -0
- model-00019-of-00034.safetensors +3 -0
- model-00020-of-00034.safetensors +3 -0
- model-00021-of-00034.safetensors +3 -0
- model-00022-of-00034.safetensors +3 -0
- model-00023-of-00034.safetensors +3 -0
- model-00024-of-00034.safetensors +3 -0
- model-00025-of-00034.safetensors +3 -0
- model-00026-of-00034.safetensors +3 -0
- model-00027-of-00034.safetensors +3 -0
- model-00028-of-00034.safetensors +3 -0
- model-00029-of-00034.safetensors +3 -0
- model-00030-of-00034.safetensors +3 -0
- model-00031-of-00034.safetensors +3 -0
- model-00032-of-00034.safetensors +3 -0
- model-00033-of-00034.safetensors +3 -0
- model-00034-of-00034.safetensors +3 -0
- model.safetensors.index.json +1 -0
- tokenizer.json +114 -0
- tokenizer_config.json +8 -0
README.md
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+
---
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+
license: apache-2.0
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+
library_name: transformers
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tags:
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- torchwright
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- compiled-transformer
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- calculator_simple
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pipeline_tag: text-generation
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+
---
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+
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# `calculator_simple`, max_digits=6 (torchwright)
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A **compiled** transformer: the
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[torchwright](https://github.com/physicsrob/torchwright) compiler emitted
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these weights directly from a computation graph — nothing was trained. This
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bundle is the `calculator_simple` example built with `max_digits=6`: a computation graph for integer arithmetic (`A op B` with `op` in `+ - *`).
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+
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The bundle uses the stock Phi-3 architecture and loads through `transformers`
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| 19 |
+
without custom model code or `trust_remote_code`.
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+
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+
Run it in **fp32** with **greedy decoding** (`do_sample=False`). Other
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precisions and decoding modes are outside the supported contract.
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+
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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+
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repo_id = 'physicsrob/torchwright-calculator-simple-max-digits-6'
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+
model = AutoModelForCausalLM.from_pretrained(repo_id).eval()
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+
tok = AutoTokenizer.from_pretrained(repo_id)
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+
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enc = tok('12*34\n', return_tensors="pt")
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out = model.generate(enc["input_ids"], max_new_tokens=32, do_sample=False,
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+
eos_token_id=tok.eos_token_id, pad_token_id=tok.eos_token_id)
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print(tok.decode(out[0, enc["input_ids"].shape[1]:], skip_special_tokens=True))
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```
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+
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## Input and output
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+
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Prompts are `A op B` terminated by a newline: two non-negative decimal
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operands of up to 6 digits, with `op` one of `+`, `-`, `*`.
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+
Subtraction may produce a negative result. Wider operands, or any character
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outside the model's small vocabulary, are outside the contract — the output
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is undefined.
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+
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| prompt | output |
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+
|---|---|
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| `12*34` | `408` |
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+
| `7+8` | `15` |
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+
| `999999*999999` | `999998000001` |
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+
| `999999+1` | `1000000` |
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+
| `999999-123456` | `876543` |
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| `123456-999999` | `-876543` |
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+
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## Intended use and limitations
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+
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+
This model is a demonstration of a computation graph compiled into transformer
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+
weights. It is not a general language model or a general-purpose calculator;
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only the input contract above is supported.
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+
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## Verification
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+
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+
The examples above are exact reference outputs. The Modal publishing path
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+
reloads the emitted checkpoint through stock `transformers`, checks those
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+
examples plus additional width-limit cases against Python integer arithmetic,
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| 67 |
+
and refuses to upload on a mismatch. This is a functional smoke test, not
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exhaustive verification of every allowed expression.
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+
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+
## Size
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| 71 |
+
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The checkpoint stores 61.67 GB of dense fp32 weights (15,416,598,528 entries) at
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the example family's shared compile width. 100.00% of those entries are
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+
exactly zero: the vast majority of the model is unused canvas, so size reflects
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the compile geometry rather than stored knowledge.
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+
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The zero entries are not compressed, and dense `transformers` execution still
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pays their memory and compute cost. CPU execution is supported; allow
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additional RAM beyond the checkpoint size.
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+
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## Family
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+
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One example of many compiled with torchwright. Calculator siblings —
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`calculator-simple` (serial arithmetic, depth grows with the digit count),
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+
`calculator-advanced` (carry-lookahead, near-flat depth), and
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| 86 |
+
`calculator-scratchpad` (flat depth; the serial work streams out as visible
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| 87 |
+
thinking tokens) — are published at several digit widths. Browse the
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| 88 |
+
[torchwright calculator models](https://huggingface.co/models?search=physicsrob%2Ftorchwright-calculator)
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on Hugging Face.
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config.json
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{
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"architectures": [
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"Phi3ForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 15,
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"embd_pdrop": 0.0,
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"eos_token_id": 16,
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"head_dim": 64,
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"hidden_act": "silu",
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"hidden_size": 8192,
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"initializer_range": 0.02,
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"intermediate_size": 8427,
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"max_position_embeddings": 512,
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"model_type": "phi3",
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"num_attention_heads": 124,
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"num_hidden_layers": 33,
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"num_key_value_heads": 124,
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"original_max_position_embeddings": 4096,
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"pad_token_id": null,
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"resid_pdrop": 0.0,
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"rms_norm_eps": 1e-05,
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"rope_parameters": {
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"partial_rotary_factor": 1.0,
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"rope_theta": 500000.0,
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"rope_type": "default"
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},
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"sliding_window": null,
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"tie_word_embeddings": true,
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"transformers_version": "5.12.1",
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"use_cache": true,
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"vocab_size": 17
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}
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generation_config.json
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{
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"bos_token_id": 15,
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"eos_token_id": 16,
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"pad_token_id": 16,
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"transformers_version": "5.12.1"
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}
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tokenizer.json
ADDED
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tokenizer_config.json
ADDED
|
@@ -0,0 +1,8 @@
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