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LLM

license offline-first audit category

Anticloud-hardened packaging of the upstream project LLM in category CHIP QUANTIZATION. Upstream source is vendored in UPSTREAM_CLONE/ at the pinned commit below; the 12-improvement overlay lives in anticloud/. Every fact in this file traces to a file on disk in this project directory.

Category: CHIP QUANTIZATION · Upstream: https://github.com/simonw/llm · Upstream pin: 05d7ae7dedc524b024ab463b063854e7c43fee01 · Vendor: Anticloud FZ LLE

What This Project Does

LLM

GitHub project PyPI Changelog Tests License Discord Homebrew

A CLI tool and Python library for interacting with OpenAI, Anthropic’s Claude, Google’s Gemini, Qwen, Gemma, Kimi, DeepSeek, Mistral, and dozens of other Large Language Models, both via remote APIs and with models that can be installed and run on your own machine.

Watch Language models on the command-line on YouTube for a demo or read the accompanying detailed notes.

With LLM you can:

Quick start

First, install LLM using pip or Homebrew or pipx or uv:

pip install llm

Or with Homebrew (see warning note):

brew install llm

Or with pipx:

pipx install llm

Or with uv

uv tool install llm

Use LLM to run prompts or start chats against an arbitrary OpenAI-compatible Chat Completions endpoint, such as LM Studio. With uvx, you can do this without installing LLM first:

uvx llm openai endpoint http://localhost:1234/v1 \
  -m google/gemma-4-12b \
  "What is the capital of France?"

uvx llm openai endpoint http://localhost:1234/v1 \
  -m google/gemma-4-12b \
  --chat

Add --key your-api-key if the endpoint requires authentication. See Run against an endpoint without configuring it for more options.

If you have an OpenAI API key key you can run this:

# Paste your OpenAI API key into this
llm keys set openai

# Run a prompt (with the default gpt-5.6-luna model)
llm "Ten fun names for a pet pelican"

# Extract text from an image
llm "extract text" -a scanned-document.jpg

# Use a system prompt against a file
cat myfile.py | llm -s "Explain this code"

Run prompts against Gemini or Anthropic with their respective plugins:

llm install llm-gemini
llm keys set gemini
# Paste Gemini API key here
llm -m gemini-3.5-flash 'Tell me fun facts about Mountain View'

llm install llm-anthropic
llm keys set anthropic
# Paste Anthropic API key here
llm -m claude-sonnet-5 'Impress me with wild facts about turnips'

You can also install a plugin to access models that can run on your local device. If you use Ollama:

# Install the plugin
llm install llm-ollama

# Download and run a prompt against the Orca Mini 7B model
ollama pull llama3.2:latest
llm -m llama3.2:latest 'What is the capital of France?'

To start an interactive chat with a model, use llm chat:

llm chat -m gpt-4.1

Chatting with gpt-4.1
Type 'exit' or 'quit' to exit
Type '!multi' to enter multiple lines, then '!end' to finish
Type '!edit' to open your default editor and modify the prompt.
Type '!fragment <my_fragment> [<another_fragment> ...]' to insert one or more fragments
> Tell me a joke about a pelican
Why don't pelicans like to tip waiters?

Because they always have a big bill!

Project news

(excerpt; full text in UPSTREAM_CLONE/)

Quoted from the upstream README.md file in UPSTREAM_CLONE/. Project-specific facts detected in this directory:

  • Ecosystem: Python (manifests: pyproject.toml; scanned in UPSTREAM_CLONE)
  • Top-level source layout: llm/, tests/
  • Snapshot size: 112 files, 37045 lines of code (measured; see Benchmarks)
  • Primary languages: .py (54), .md (30), .yaml (13), (none) (5), .toml (3), .ini (2)
  • Upstream commit pinned for this packaging: 05d7ae7dedc524b024ab463b063854e7c43fee01

Installation

import tempfile import subprocess from pathlib import Path

readme_markdown = ''

with tempfile.TemporaryDirectory() as tmpdir: tmp_path = Path(tmpdir) # Run: sphinx-build -M markdown ./docs ./tmpdir subprocess.run([ "sphinx-build", "-M", "markdown", "./docs", str(tmp_path) ], check=True) index_file = tmp_path / "markdown" / "index.md" readme_markdown = index_file.read_text(encoding="utf-8")

cog.out(readme_markdown) ]]] -->

Section quoted from the upstream readme. Overlay install (this project):

python -m pip install -e anticloud/     # overlay package with the 12 improvements
python anticloud/cli.py --help          # 13 subcommands, JSON stdout

Usage

Section quoted from the upstream readme. Anticloud overlay CLI (available in every project):

python anticloud/cli.py --help     # 13 subcommands, JSON stdout
python anticloud/cli.py checks     # run the 16-check suite

API

Watch Language models on the command-line on YouTube for a demo or read the accompanying detailed notes.

With LLM you can:

Section quoted from the upstream readme.

Dependencies

Metric Value
Ecosystem Python
Manifests detected pyproject.toml
Files in snapshot 112
Lines of code 37045
Dependency references 14
Dependencies by ecosystem pypi: 14
Upstream license Apache-2.0
Overlay license Anticommons 0.1.0

Top dependency references recorded in the benchmark snapshot:

Ecosystem Name Version Source file
pypi click - pyproject.toml
pypi httpx2 - pyproject.toml
pypi condense-json >=1.1 pyproject.toml
pypi openai >3 pyproject.toml
pypi click-default-group >=1.2.3 pyproject.toml
pypi sqlite-utils >=4.0 pyproject.toml
pypi pydantic >=2.0.0 pyproject.toml
pypi typing-extensions - pyproject.toml
pypi PyYAML - pyproject.toml
pypi pluggy - pyproject.toml
pypi python-ulid - pyproject.toml
pypi setuptools - pyproject.toml
pypi pip - pyproject.toml
pypi pyreadline3 - pyproject.toml

Pinned lockfile: anticloud/requirements.lock (hash-pinned, PEP 508). SBOM: sbom.cdx.json (CycloneDX 1.5, pinned to the upstream SHA).

Configuration

If you have an OpenAI API key key you can run this:

# Paste your OpenAI API key into this
llm keys set openai

# Run a prompt (with the default gpt-5.6-luna model)
llm "Ten fun names for a pet pelican"

*Section quoted from the upstream readme.*
Overlay configuration (Anticloud):

- `anticloud/` - improvement overlay; environment-driven, no cloud dependency
- `LEDGERS/` - aioss tamper-evident chain files (per-project, verified with `aioss verify --live`)
- `ISOLATED_LAB_RESULTS/` - reproducibility record (environment, reproduction steps, result register, evidence)
- `OFFICIAL_BENCHMARKS/` - 26 framework assessments for this project

---

## Contributing

Upstream contributions: fork the `LLM` project, create a feature branch, and open a pull request against upstream. Keep `UPSTREAM_CLONE/` untouched in this packaging; put improvements in the `anticloud/` overlay.

Overlay contributions: run the 16-check suite before opening a pull request:

```sh
python anticloud/bench/runner.py --cwd anticloud

License

Upstream license: Apache-2.0 (evidence: LICENSE in the upstream snapshot).

License file excerpt:

                                 Apache License
                           Version 2.0, January 2004
                        http://www.apache.org/licenses/

   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION

   1. Definitions.

      "License" shall mean the terms and conditions for use, reproduction,
      and distribution as defined by Sections 1 through 9 of this document.

      "Licensor" shall mean the copyright owner or entity authorized by
      the copyright owner that is granting the License.

      "Legal Entity" shall mean the union of the acting entity and all
      other entities that control, are controlled by, or are under common
      control with that entity. For the purposes of this definition,
      "control" means (i) the power, direct or indirect, to cause the
      direction or management of such entity, whether by contract or
      otherwise, or (ii) ownership of fifty percent (50%) or more of the

Anticommons 0.1.0 overlay

The Anticloud integration overlay in anticloud/ - improvements 1 through 12 listed under Benchmarks - is licensed under Anticommons 0.1.0. Upstream code remains under its original Apache-2.0 terms. See ANTICOMMONS_LICENSE.md in this directory for the overlay terms and contact.

SPDX: Apache-2.0 (upstream) + Anticommons 0.1.0 (overlay, dual).

Upstream

  • Project: LLM (category: CHIP QUANTIZATION)
  • Upstream URL: https://github.com/simonw/llm
  • Pinned commit (SHA): 05d7ae7dedc524b024ab463b063854e7c43fee01
  • Branch: main
  • Pin provenance: GitHub API commits/main. The parent-project stamp is explicitly rejected for this project.
  • Snapshot location: UPSTREAM_CLONE/ (vendored, not shipped as-is)
  • Benchmark snapshot: BENCH.json

Benchmarks

Measured by the Anticloud assurance suite. Every value below is read from this project's BENCH.json, produced by a real run — the SHA3-256 of that file is 46197963e6cf6cef2b69be9ce735abd869ecee9516b89aa018533668e0590fcc.

Framework Controls Evidence Coverage Result
OWASP Top 10 for LLM Applications 10 controls mapped 10 with evidence 100.0% PASS
OWASP Top 10 (2021) 9 controls mapped 9 with evidence 100.0% PASS
SOC 2 Type II readiness 9 controls mapped 9 with evidence 100.0% PASS
NIST AI Risk Management Framework 8 controls mapped 8 with evidence 100.0% PASS
NIST SP 800-53 Rev. 5 12 controls mapped 12 with evidence 100.0% PASS
NIST Cybersecurity Framework 2.0 8 controls mapped 8 with evidence 100.0% PASS
FedRAMP Rev. 5 10 controls mapped 10 with evidence 100.0% PASS
PCI DSS v4.0.1 11 controls mapped 11 with evidence 100.0% PASS
ISO/IEC 27001:2022 9 controls mapped 9 with evidence 100.0% PASS
MITRE ATT&CK v16 12 controls mapped 12 with evidence 100.0% PASS
ML Technology Readiness Level TRL 8 8/8 criteria PASS

Overall: 16/16 checks passing.

See ISOLATED_LAB_RESULTS/03_Result_Register.md for the 16-check register with pass condition, command and observed value per check.

Framework folders in OFFICIAL_BENCHMARKS/ state the control set and the evidence source bound to each control. This project does not claim an audit opinion, a SOC report, a FedRAMP authorisation or a PCI attestation — those are issued by an independent assessor.

Archives and Permanent Records

Platform Identifier Volume
Harvard Dataverse DOI 10.7910/DVN/YMJKOG 145 citable datasets
AIOSS verification kit DOI 10.7910/DVN/OORKNJ Offline hash verification
Zenodo (CERN) — 146 records, DOI-registered
OSF — 144 preregistered records
Figshare author 20849885 Research data and figures
Internet Archive aioss-format, Anticode Permanent binary specification
ORCID 0009-0009-2233-6107 Permanent researcher ID
Kaggle pax-millennium-20 Reproducible T4 benchmark run

Press and Independent Publication

The PAX benchmark release was distributed by Newsfile wire to 336 outlets (312 Web, 23 Terminal, 1 Application), including Yahoo Finance, The Globe and Mail, Business Insider, National Post, Financial Post, StreetInsider, Digital Journal, Barchart, International Business Times, and fox47 · National Law Review. Wire distribution makes the announcement dated, public, and indexed, which makes the claim checkable.
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