ILM v1.0.0

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+ilm-linux-x86_64 filter=lfs diff=lfs merge=lfs -text
+ilm-macos-arm64 filter=lfs diff=lfs merge=lfs -text
+ilm-windows-x86_64.exe filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -0,0 +1,176 @@
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README.md

@@ -1,3 +1,221 @@
 ---
 license: apache-2.0
+language:
+- en
+pipeline_tag: question-answering
+tags:
+- cpu
+- counting
+- no-gradient
+- interpretable
+- reasoning
+- rust
+model-index:
+- name: ILM
+  results:
+  - task:
+      type: question-answering
+      name: Question answering over a story
+    dataset:
+      name: bAbI, 20 tasks, 1,000 examples each
+      type: Muennighoff/babi
+      split: test
+    metrics:
+    - type: accuracy
+      value: 99.78
+      name: Accuracy
+  - task:
+      type: text2text-generation
+      name: Commands to actions
+    dataset:
+      name: SCAN, simple split
+      type: marcov/scan_simple_promptsource
+      split: test
+    metrics:
+    - type: exact_match
+      value: 100.0
+      name: Exact match
+  - task:
+      type: text2text-generation
+      name: Commands to actions
+    dataset:
+      name: SCAN, length split
+      type: marcov/scan_length_promptsource
+      split: test
+    metrics:
+    - type: exact_match
+      value: 100.0
+      name: Exact match
+  - task:
+      type: text2text-generation
+      name: Commands to actions
+    dataset:
+      name: SCAN, add jump split
+      type: marcov/scan_addprim_jump_promptsource
+      split: test
+    metrics:
+    - type: exact_match
+      value: 100.0
+      name: Exact match
+  - task:
+      type: text2text-generation
+      name: Commands to actions
+    dataset:
+      name: SCAN, add turn left split
+      type: marcov/scan_addprim_turn_left_promptsource
+      split: test
+    metrics:
+    - type: exact_match
+      value: 100.0
+      name: Exact match
+  - task:
+      type: question-answering
+      name: Deduction over told rules
+    dataset:
+      name: ProofWriter, depth 5, open form
+      type: tasksource/proofwriter
+      config: depth-5
+      split: test
+    metrics:
+    - type: accuracy
+      value: 100.0
+      name: Accuracy
+  - task:
+      type: question-answering
+      name: Deduction over told rules
+    dataset:
+      name: ProofWriter, depth 5, closed form
+      type: rlhf-and-friends/proofwriter-source
+      config: CWA-depth-5
+      split: test
+    metrics:
+    - type: accuracy
+      value: 100.0
+      name: Accuracy
+  - task:
+      type: text-classification
+      name: Natural language inference over 17 groups of logic
+    dataset:
+      name: LoNLI, the sealed part of 15 groups
+      type: tasksource/lonli
+    metrics:
+    - type: accuracy
+      value: 94.9
+      name: Accuracy
+  - task:
+      type: question-answering
+      name: Kinship over a story written by people
+    dataset:
+      name: CLUTRR, 2 to 10 steps
+      type: CLUTRR/v1
+      config: gen_train234_test2to10
+      split: test
+    metrics:
+    - type: accuracy
+      value: 72.3
+      name: Accuracy
+  - task:
+      type: question-answering
+      name: Facts from encyclopedia text
+    dataset:
+      name: LAMA, T-REx part
+      type: facebook/lama
+      config: trex
+    metrics:
+    - type: precision_at_1
+      value: 64.0
+      name: Precision at 1
+  - task:
+      type: question-answering
+      name: Word problems
+    dataset:
+      name: SVAMP, standard setting (300 test problems)
+      type: ChilleD/SVAMP
+      split: test
+    metrics:
+    - type: accuracy
+      value: 38.3
+      name: Accuracy
+  - task:
+      type: text-generation
+      name: Text from a list of facts
+    dataset:
+      name: E2E NLG
+      type: tuetschek/e2e_nlg
+      split: test
+    metrics:
+    - type: bleu
+      value: 65.37
+      name: BLEU
+    - type: nist
+      value: 8.29
+      name: NIST
 ---
+
+# 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 in seconds, 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. It stores whole-number counts: how often each pattern in a text came with each answer. 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 (for two tasks below, also the labels that the set gives). It keeps a pattern only when its counts show that the pattern is reliable. So what it learned is a set of rules that you can read, and it 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
+
+## 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 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, LAMA and SVAMP the part that [docs/RESULTS.md](https://github.com/Datamine-Crypto/ILM/blob/main/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 | 20 of 20 tasks pass, 99.78% right | best published learner from answers only: 3 tasks fail (Universal Transformer) | 5.9 s |
+| SCAN, 4 splits | 100% on every split | Transformer: length 0.0, add jump 1.0 | 0.8 s |
+| ProofWriter, depth 5 | 100% (40,063 of 40,063; closed depth 5 also learns from the formal forms of the set) | ProofWriter, T5-11B: 99.6 to 99.7 | 4.0 s |
+| LoNLI, 17 groups of logic in NLI | 94.9% on its sealed part (15 groups; 10 of 16 learned) | tuned on MNLI, not on LoNLI: 59 to 71; RoBERTa-large tuned on 2% of LoNLI: near 100 | 0.7 s |
+| CLUTRR, 2 to 10 steps | 72.3% (learns from the step labels of the set) | GPT-4o with a solver: 67.7; Faithful CoT with votes: 71.9 | 15.9 s |
+| LAMA T-REx | 64.0% | BERT-large: 32.3; MegatronLM 11B with trained prompts, on a subset: 64.2 | 11.6 s |
+| SVAMP | 38.3% | Graph2Tree: 36.5; with RoBERTa: 43.8; GPT-3 with chain of thought: 68.9 | 27.6 s |
+| E2E NLG | BLEU 65.37 | TGen: 65.93; GPT-2 large with LoRA: 70.4 | 9.8 s |
+
+The settings, the published numbers with their sources, and notes on each benchmark are in [docs/RESULTS.md](https://github.com/Datamine-Crypto/ILM/blob/main/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 1.90 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. [docs/REPRODUCE.md](https://github.com/Datamine-Crypto/ILM/blob/main/docs/REPRODUCE.md) gives the commands for every benchmark.
+
+## How it works
+
+ILM has fifteen tools, each named for its way of reading: patterns, state, amount, order, link, rule, parts, together, list, requests, frames, table, arrange, graph and ranks. For each kind of task, every tool learns from two thirds of the lessons and reads the last third. The tool with the most right answers learns again from all lessons. No person chooses a tool, and no setting changes between tasks. Beside the tools, ILM has a fact reader for encyclopedia text, a question reader, a writer that turns given facts into a text, and one command (`ilm ask`) that routes each request and prints a trace of its steps.
+
+## 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 59 tasks: every kind of the task at 95% right or more on held cases (90% where people wrote the text). On SVAMP and E2E, pre-trained transformers are ahead.
+
+## License
+
+Apache License 2.0. The sets that ILM reads keep their own licenses, and this repository holds none of their data.

SHA256SUMS.txt

@@ -0,0 +1,3 @@
+1d2b5c79fe531558b97d81c14dd1fdb756e3891fd97c3590a3bc1de64fb63689  ilm-linux-x86_64
+fe3fdff0e347fccfa2e706a123d7a41b492f02e02de306067c64ca9e56da47fe  ilm-macos-arm64
+95ae3685bd99343698ff3ee23666c25de25cdb26338f884643286c578a05a204  ilm-windows-x86_64.exe

THIRD_PARTY_NOTICES.md

@@ -0,0 +1,168 @@
+# Third-party notices
+
+The program `ilm` holds code and data of others, under the licenses below. ILM itself is under the Apache License 2.0 (LICENSE).
+
+Crates that run only while the program is built (proc-macro2, quote, syn, unicode-ident, cc, find-msvc-tools, shlex) are not part of the program.
+
+The program also holds the Rust standard library (https://github.com/rust-lang/rust), which is under the MIT License or the Apache License 2.0; it is used here under the Apache License 2.0 of LICENSE.
+
+## mimalloc, the C library
+
+The memory allocator of the program, built from the sources that the crate libmimalloc-sys holds (https://github.com/microsoft/mimalloc).
+
+```
+MIT License
+
+Copyright (c) 2018-2025 Microsoft Corporation, Daan Leijen
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+```
+
+## mimalloc and libmimalloc-sys, the Rust crates
+
+The Rust crates that build and call the C library (https://github.com/purpleprotocol/mimalloc_rust).
+
+```
+Copyright 2019 Octavian Oncescu
+
+Permission is hereby granted, free of charge, to any
+person obtaining a copy of this software and associated
+documentation files (the "Software"), to deal in the
+Software without restriction, including without
+limitation the rights to use, copy, modify, merge,
+publish, distribute, sublicense, and/or sell copies of
+the Software, and to permit persons to whom the Software
+is furnished to do so, subject to the following
+conditions:
+
+The above copyright notice and this permission notice
+shall be included in all copies or substantial portions
+of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF
+ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED
+TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
+PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT
+SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
+CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR
+IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+DEALINGS IN THE SOFTWARE.
+```
+
+## hex, a Rust crate
+
+Used under the MIT License, one of its two licenses (https://github.com/KokaKiwi/rust-hex).
+
+```
+Copyright (c) 2013-2014 The Rust Project Developers.
+Copyright (c) 2015-2020 The rust-hex Developers
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+```
+
+## premise and premise_macros, Rust crates
+
+The rules of the code and their macros (https://github.com/Datamine-Crypto/premise).
+
+```
+MIT License
+
+Copyright (c) 2026 Datamine Network contributors
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+```
+
+## Unicode Character Database
+
+The tables of the kinds of signs, of decimal digits and of the decompositions of letters in crates/ilm-core/src/know/letters.rs, crates/ilm-core/src/know/signs.rs, crates/ilm-core/src/write/signs.rs and crates/ilm-data/src/sets/textmatch.rs come from the Unicode Character Database.
+
+```
+UNICODE LICENSE V3
+
+COPYRIGHT AND PERMISSION NOTICE
+
+Copyright © 1991-2023 Unicode, Inc.
+
+NOTICE TO USER: Carefully read the following legal agreement. BY
+DOWNLOADING, INSTALLING, COPYING OR OTHERWISE USING DATA FILES, AND/OR
+SOFTWARE, YOU UNEQUIVOCALLY ACCEPT, AND AGREE TO BE BOUND BY, ALL OF THE
+TERMS AND CONDITIONS OF THIS AGREEMENT. IF YOU DO NOT AGREE, DO NOT
+DOWNLOAD, INSTALL, COPY, DISTRIBUTE OR USE THE DATA FILES OR SOFTWARE.
+
+Permission is hereby granted, free of charge, to any person obtaining a
+copy of data files and any associated documentation (the "Data Files") or
+software and any associated documentation (the "Software") to deal in the
+Data Files or Software without restriction, including without limitation
+the rights to use, copy, modify, merge, publish, distribute, and/or sell
+copies of the Data Files or Software, and to permit persons to whom the
+Data Files or Software are furnished to do so, provided that either (a)
+this copyright and permission notice appear with all copies of the Data
+Files or Software, or (b) this copyright and permission notice appear in
+associated Documentation.
+
+THE DATA FILES AND SOFTWARE ARE PROVIDED "AS IS", WITHOUT WARRANTY OF ANY
+KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT OF
+THIRD PARTY RIGHTS.
+
+IN NO EVENT SHALL THE COPYRIGHT HOLDER OR HOLDERS INCLUDED IN THIS NOTICE
+BE LIABLE FOR ANY CLAIM, OR ANY SPECIAL INDIRECT OR CONSEQUENTIAL DAMAGES,
+OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION,
+ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THE DATA
+FILES OR SOFTWARE.
+
+Except as contained in this notice, the name of a copyright holder shall
+not be used in advertising or otherwise to promote the sale, use or other
+dealings in these Data Files or Software without prior written
+authorization of the copyright holder.
+```

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