🧮 ILM: Integer Language Model

⚡ CPU only. ILM is a language model that needs no GPU, to learn or to answer. On one desktop CPU it learns each benchmark below from its lessons, then answers its test questions.

🤔 Why no GPU? A neural network, such as a transformer, learns by changing millions of decimal weights. It repeats billions of multiplications over many passes through the data, and a GPU is built for that work. ILM has no weights. What it learns is lines of English: rules, question forms and facts. It keeps a line when the line answers the lessons right, and it finds this by counting. Counting is the addition of whole numbers, and a normal CPU does it fast.

🧠 ILM learns from lessons: a short text, a question and the exact answer. What it learned is a set of lines that you can read, and each answer names the lines that gave it, with a proof tree when steps prove it. ILM gives the same answers on every run and for every number of threads.

• 💻 Source code, documentation and the commands that give each number below: https://github.com/Datamine-Crypto/ILM
• 📦 Crate: https://crates.io/crates/datamine-ilm
• 🌐 Website: https://datamine.network

📁 Files

File	What it is
ilm-windows-x86_64.exe	the program for Windows on x86-64
ilm-linux-x86_64	the program for Linux on x86-64 (glibc 2.35 or later, as on Ubuntu 22.04)
ilm-macos-arm64	the program for macOS on Apple silicon
SHA256SUMS.txt	the SHA-256 of each program
LICENSE	the Apache License 2.0
THIRD_PARTY_NOTICES.md	the licenses of the code of others that the programs hold

ILM has no weights to download: it learns each task from the lessons of that task when you run it. The program reads its library of texts, the descriptions of the sets and the list of sources from the GitHub repository, so run it from a clone of that repository.

📊 Results

Each benchmark is read with frozen code on its test data: the official test split, or for LoNLI the part that docs/RESULTS.md names. The runs use one desktop CPU (Intel Core i9-12900K, 24 threads, no GPU). The time is for the whole run, learning from all lessons and answering the test questions, as the best of three or more runs.

Benchmark	ILM	Published, for comparison	Time
bAbI, 20 tasks, 1,000 examples each	19 of 20 tasks pass, 97.44% right (18,903 of 19,399; 601 test questions with the story of a lesson are not read)	the best published learner from answers only that we found: 3 tasks fail (Universal Transformer)	14.0 s
SCAN, 4 splits	100% (17,016 of 17,016)	Transformer: length 0.0, add jump 1.0	1.5 s
ProofWriter, depth 5, open and closed form	99.52% (39,870 of 40,063)	ProofWriter (T5-11B): 99.6 to 99.7	0.4 s
LoNLI, 17 groups of logic in NLI	98.18% on its sealed part (15 groups)	tuned on MNLI, not on LoNLI: 59 to 71; RoBERTa-large tuned on 2% of LoNLI: near 100	2.5 s
CLUTRR, graph form, 2 to 10 steps	99.14% (1,036 of 1,045)	trained on 2 to 4 steps, read at 10 steps: Transformer 70, NCRL 97, R5 98	1.4 s
Sentence transformations: question and passive, English and German	100% whole sentences right (80,000 of 80,000)	Transformer from scratch: whole sentences near 0% on the new builds	1.6 s
PCFG SET	100% (31,229 of 31,229)	Transformer: productivity 50, systematicity 72	8.4 s
ToM-QA, 12 kinds	100% (1,950 of 1,950)	best published for each form: 88.6 to 100	2.1 s

The papers give some of these as fractions; they are shown here as percentages.

The settings, the counts, the published numbers with their sources, how ILM stands against transformers with the evidence for each claim, and notes on each benchmark are in docs/RESULTS.md of the GitHub repository.

🚀 How to use

The program reads the descriptions of the sets from a clone of the GitHub repository. With the program of this repository, on Linux:

git clone https://github.com/Datamine-Crypto/ILM ilm
cd ilm
cp <download folder>/ilm-linux-x86_64 ./ilm
chmod +x ./ilm
./ilm fetch data/sources.txt sets=src_babi
./ilm export src_babi data/sets/final/src_babi.txt cases/final/src_babi
./ilm measure cases/final/src_babi

On macOS, take ilm-macos-arm64 in the same way, and also run xattr -d com.apple.quarantine ./ilm, because the program is not signed with an Apple developer certificate. On an Intel Mac, build the program from source. On Windows, copy ilm-windows-x86_64.exe into the clone as ilm.exe and run .\ilm.exe fetch data/sources.txt sets=src_babi, and so on. To build the program yourself in place of a download: cargo build --release (Rust 1.97 or later and a C compiler), then call ./target/release/ilm. fetch gets the raw files of a set from the Hugging Face hub or GitHub at a fixed revision and checks their SHA-256. export writes a case file for each kind of the set. measure learns each kind from its lessons and reads its test cases. prove prints the answer to a story and a question with its proof tree. docs/REPRODUCE.md gives the commands for every benchmark.

⚙️ How it works

ILM has one tool, the explain tool, and a library of texts in plain English: rules, told facts, question forms with their answers, and lines that write a sentence again. The engine chains the rules over the told sentences of a case and answers with the first answer whose conditions hold. For each kind of task, the tool learns lines of the library from the lessons: the wording of the questions, rules discovered from the lessons alone, the ways of joining of a language of commands, and the rewrites of a sentence into another build. The facts step reads facts of the library from plain text that people wrote (the capitals of the library come from a plain-text copy of the English Wikipedia). The tool tries its ways on two thirds of the lessons and judges them on the last third; no person chooses a way for a kind, and no setting changes between tasks. Each answer is marked proof, default, guess or none, and ilm prove prints its proof tree.

🚧 Limits

ILM is not a general chat model. It learns one kind of task at a time from lessons of that kind, and each lesson needs the exact answer. On a board of 25 public sets (240 kinds in 112 tasks), it learns 79 tasks: every kind of the task at 95% right or more on held cases (90% where people wrote the text). It learns no task whose text people wrote: the stories of CLUTRR, the word problems (MAWPS, ASDiv, SVAMP), the descriptions of regular expressions of NL-RX, the rules in the words of people of ParaRules, and the intents and slots of ATIS and SNIPS. On these, pre-trained transformers lead. It gets no result on StepGame, SpartQA, FLD and BoardgameQA. On bAbI, task 14 (time) fails on its test split, 519 of 1,000 right (51.9%), although ILM reads its development data at 100%: learned from 943 or more of its lessons, it reads "before" in the order of the story, and the stories of that task tell the times out of order. On ProofWriter, rules of the library read the told rules: that number measures the engine, not rules that ILM learned, and it is level with ProofWriter (T5-11B), not ahead. The ToM-QA number is not a read of unseen cases: the board read that test split during development (shares only, never a wrong case). During development the board read 527 of the 1,045 CLUTRR graph test graphs (shares only, never a wrong case).

📜 License

Apache License 2.0. The sets that ILM reads keep their own licenses, and this repository holds none of their data.