├── lfm
    ├── __init__.py
    └── model.py
├── requirements.txt
├── scripts
    ├── tests.sh
    ├── test_name.sh
    ├── code_quality.sh
    └── merge_all_prs.sh
├── .github
    ├── workflows
    │   ├── ruff.yml
    │   ├── pull-request-links.yml
    │   ├── docs.yml
    │   ├── lints.yml
    │   ├── testing.yml
    │   ├── quality.yml
    │   ├── pr_request_checks.yml
    │   ├── label.yml
    │   ├── run_test.yml
    │   ├── welcome.yml
    │   ├── pylint.yml
    │   ├── docs_test.yml
    │   ├── unit-test.yml
    │   ├── python-publish.yml
    │   ├── code_quality_control.yml
    │   ├── stale.yml
    │   ├── cos_integration.yml
    │   └── test.yml
    ├── dependabot.yml
    ├── ISSUE_TEMPLATE
    │   ├── feature_request.md
    │   └── bug_report.md
    ├── FUNDING.yml
    ├── PULL_REQUEST_TEMPLATE.yml
    └── labeler.yml
├── .pre-commit-config.yaml
├── LICENSE
├── pyproject.toml
├── .gitignore
└── README.md


/lfm/__init__.py:
--------------------------------------------------------------------------------
1 | 


--------------------------------------------------------------------------------
/requirements.txt:
--------------------------------------------------------------------------------
1 | torch
2 | zetascale
3 | swarms
4 | 


--------------------------------------------------------------------------------
/scripts/tests.sh:
--------------------------------------------------------------------------------
1 | find ./tests -name '*.py' -exec pytest {} \;


--------------------------------------------------------------------------------
/.github/workflows/ruff.yml:
--------------------------------------------------------------------------------
1 | name: Ruff
2 | on: [ push, pull_request ]
3 | jobs:
4 |   ruff:
5 |     runs-on: ubuntu-latest
6 |     steps:
7 |       - uses: actions/checkout@v4
8 |       - uses: chartboost/ruff-action@v1
9 | 


--------------------------------------------------------------------------------
/scripts/test_name.sh:
--------------------------------------------------------------------------------
1 | find ./tests -name "*.py" -type f | while read file
2 | do
3 |   filename=$(basename "$file")
4 |   dir=$(dirname "$file")
5 |   if [[ $filename != test_* ]]; then
6 |     mv "$file" "$dir/test_$filename"
7 |   fi
8 | done


--------------------------------------------------------------------------------
/.github/workflows/pull-request-links.yml:
--------------------------------------------------------------------------------
 1 | name: readthedocs/actions
 2 | on:
 3 |   pull_request_target:
 4 |     types:
 5 |       - opened
 6 |     paths:
 7 |       - "docs/**"
 8 | 
 9 | permissions:
10 |   pull-requests: write
11 | 
12 | jobs:
13 |   pull-request-links:
14 |     runs-on: ubuntu-latest
15 |     steps:
16 |       - uses: readthedocs/actions/preview@v1
17 |         with:
18 |           project-slug: swarms_torch


--------------------------------------------------------------------------------
/.github/dependabot.yml:
--------------------------------------------------------------------------------
 1 | # https://docs.github.com/en/code-security/supply-chain-security/keeping-your-dependencies-updated-automatically/configuration-options-for-dependency-updates
 2 | 
 3 | version: 2
 4 | updates:
 5 |   - package-ecosystem: "github-actions"
 6 |     directory: "/"
 7 |     schedule:
 8 |       interval: "weekly"
 9 | 
10 |   - package-ecosystem: "pip"
11 |     directory: "/"
12 |     schedule:
13 |       interval: "weekly"
14 | 
15 | 


--------------------------------------------------------------------------------
/.github/workflows/docs.yml:
--------------------------------------------------------------------------------
 1 | name: Docs WorkFlow
 2 | 
 3 | on:
 4 |   push:
 5 |     branches:
 6 |       - master
 7 |       - main
 8 |       - develop
 9 | jobs:
10 |   deploy:
11 |     runs-on: ubuntu-latest
12 |     steps:
13 |       - uses: actions/checkout@v4
14 |       - uses: actions/setup-python@v5
15 |         with:
16 |           python-version: '3.10'
17 |       - run: pip install mkdocs-material
18 |       - run: pip install "mkdocstrings[python]"
19 |       - run: mkdocs gh-deploy --force


--------------------------------------------------------------------------------
/.pre-commit-config.yaml:
--------------------------------------------------------------------------------
 1 | repos:
 2 |   - repo: https://github.com/ambv/black
 3 |     rev: 22.3.0
 4 |     hooks:
 5 |     - id: black
 6 |   - repo: https://github.com/charliermarsh/ruff-pre-commit
 7 |     rev: 'v0.0.255'
 8 |     hooks:
 9 |       - id: ruff
10 |         args: [--fix]
11 |   - repo: https://github.com/nbQA-dev/nbQA
12 |     rev: 1.6.3
13 |     hooks:
14 |     - id: nbqa-black
15 |       additional_dependencies: [ipython==8.12, black]
16 |     - id: nbqa-ruff 
17 |       args: ["--ignore=I001"]
18 |       additional_dependencies: [ipython==8.12, ruff]


--------------------------------------------------------------------------------
/.github/workflows/lints.yml:
--------------------------------------------------------------------------------
 1 | name: Linting
 2 | 
 3 | on:
 4 |   push:
 5 |     branches:
 6 |       - master
 7 | 
 8 | jobs:
 9 |   lint:
10 |     runs-on: ubuntu-latest
11 | 
12 |     steps:
13 |       - name: Checkout code
14 |         uses: actions/checkout@v4
15 | 
16 |       - name: Set up Python
17 |         uses: actions/setup-python@v5
18 |         with:
19 |           python-version: '3.10'
20 | 
21 |       - name: Install dependencies
22 |         run: pip install --no-cache-dir -r requirements.txt
23 | 
24 |       - name: Run linters
25 |         run: pylint swarms_torch


--------------------------------------------------------------------------------
/.github/workflows/testing.yml:
--------------------------------------------------------------------------------
 1 | name: Unit Tests
 2 | 
 3 | on:
 4 |   push:
 5 |     branches:
 6 |       - master
 7 | 
 8 | jobs:
 9 |   test:
10 |     runs-on: ubuntu-latest
11 | 
12 |     steps:
13 |       - name: Checkout code
14 |         uses: actions/checkout@v4
15 | 
16 |       - name: Set up Python
17 |         uses: actions/setup-python@v5
18 |         with:
19 |           python-version: '3.10'
20 | 
21 |       - name: Install dependencies
22 |         run: pip install --no-cache-dir -r requirements.txt
23 | 
24 |       - name: Run unit tests
25 |         run: pytest tests/


--------------------------------------------------------------------------------
/.github/workflows/quality.yml:
--------------------------------------------------------------------------------
 1 | name: Quality
 2 | 
 3 | on:
 4 |   push:
 5 |     branches: [ "main" ]
 6 |   pull_request:
 7 |     branches: [ "main" ]
 8 | 
 9 | jobs:
10 |   lint:
11 |     runs-on: ubuntu-latest
12 |     strategy:
13 |       fail-fast: false
14 |     steps:
15 |       - name: Checkout actions
16 |         uses: actions/checkout@v4
17 |         with:
18 |           fetch-depth: 0
19 |       - name: Init environment 
20 |         uses: ./.github/actions/init-environment 
21 |       - name: Run linter
22 |         run: |
23 |           pylint `git diff --name-only --diff-filter=d origin/main HEAD | grep -E '\.py$' | tr '\n' ' '`


--------------------------------------------------------------------------------
/.github/workflows/pr_request_checks.yml:
--------------------------------------------------------------------------------
 1 | name: Pull Request Checks
 2 | 
 3 | on:
 4 |   pull_request:
 5 |     branches:
 6 |       - master
 7 | 
 8 | jobs:
 9 |   test:
10 |     runs-on: ubuntu-latest
11 | 
12 |     steps:
13 |       - name: Checkout code
14 |         uses: actions/checkout@v4
15 | 
16 |       - name: Set up Python
17 |         uses: actions/setup-python@v5
18 |         with:
19 |           python-version: '3.10'
20 | 
21 |       - name: Install dependencies
22 |         run: pip install --no-cache-dir -r requirements.txt
23 | 
24 |       - name: Run tests and checks
25 |         run: |
26 |           pytest tests/
27 |           pylint swarms_torch


--------------------------------------------------------------------------------
/.github/workflows/label.yml:
--------------------------------------------------------------------------------
 1 | # This workflow will triage pull requests and apply a label based on the
 2 | # paths that are modified in the pull request.
 3 | #
 4 | # To use this workflow, you will need to set up a .github/labeler.yml
 5 | # file with configuration.  For more information, see:
 6 | # https://github.com/actions/labeler
 7 | 
 8 | name: Labeler
 9 | on: [pull_request_target]
10 | 
11 | jobs:
12 |   label:
13 | 
14 |     runs-on: ubuntu-latest
15 |     permissions:
16 |       contents: read
17 |       pull-requests: write
18 | 
19 |     steps:
20 |     - uses: actions/labeler@v5.0.0
21 |       with:
22 |         repo-token: "${{ secrets.GITHUB_TOKEN }}"
23 | 


--------------------------------------------------------------------------------
/.github/workflows/run_test.yml:
--------------------------------------------------------------------------------
 1 | name: Python application test
 2 | 
 3 | on: [push]
 4 | 
 5 | jobs:
 6 |   build:
 7 | 
 8 |     runs-on: ubuntu-latest
 9 | 
10 |     steps:
11 |     - uses: actions/checkout@v4
12 |     - name: Set up Python 3.10
13 |       uses: actions/setup-python@v5
14 |       with:
15 |         python-version: '3.10'
16 |     - name: Install dependencies
17 |       run: |
18 |         python -m pip install --no-cache-dir --upgrade pip
19 |         pip install pytest
20 |         if [ -f requirements.txt ]; then pip install --no-cache-dir -r requirements.txt; fi
21 |     - name: Run tests with pytest
22 |       run: |
23 |         pytest tests/
24 | 


--------------------------------------------------------------------------------
/.github/workflows/welcome.yml:
--------------------------------------------------------------------------------
 1 | name: Welcome WorkFlow
 2 | 
 3 | on:
 4 |   issues:
 5 |     types: [opened]
 6 |   pull_request_target:
 7 |     types: [opened]
 8 | 
 9 | jobs:
10 |   build:
11 |     name: 👋 Welcome
12 |     permissions: write-all
13 |     runs-on: ubuntu-latest
14 |     steps:
15 |       - uses: actions/first-interaction@v1.3.0
16 |         with:
17 |           repo-token: ${{ secrets.GITHUB_TOKEN }}
18 |           issue-message: "Hello there, thank you for opening an Issue ! 🙏🏻 The team was notified and they will get back to you asap."
19 |           pr-message:  "Hello there, thank you for opening an PR ! 🙏🏻 The team was notified and they will get back to you asap."


--------------------------------------------------------------------------------
/.github/workflows/pylint.yml:
--------------------------------------------------------------------------------
 1 | name: Pylint
 2 | 
 3 | on: [push]
 4 | 
 5 | jobs:
 6 |   build:
 7 |     runs-on: ubuntu-latest
 8 |     strategy:
 9 |       matrix:
10 |         python-version: ["3.9", "3.10"]
11 |     steps:
12 |     - uses: actions/checkout@v4
13 |     - name: Set up Python ${{ matrix.python-version }}
14 |       uses: actions/setup-python@v5
15 |       with:
16 |         python-version: ${{ matrix.python-version }}
17 |     - name: Install dependencies
18 |       run: |
19 |         python -m pip install --no-cache-dir --upgrade pip
20 |         pip install pylint
21 |     - name: Analysing the code with pylint
22 |       run: |
23 |         pylint $(git ls-files '*.py')
24 | 


--------------------------------------------------------------------------------
/.github/workflows/docs_test.yml:
--------------------------------------------------------------------------------
 1 | name: Documentation Tests
 2 | 
 3 | on:
 4 |   push:
 5 |     branches:
 6 |       - master
 7 | 
 8 | jobs:
 9 |   test:
10 |     runs-on: ubuntu-latest
11 | 
12 |     steps:
13 |       - name: Checkout code
14 |         uses: actions/checkout@v4
15 | 
16 |       - name: Set up Python
17 |         uses: actions/setup-python@v5
18 |         with:
19 |           python-version: '3.10'
20 | 
21 |       - name: Install dependencies
22 |         run: pip install --no-cache-dir -r requirements.txt
23 | 
24 |       - name: Build documentation
25 |         run: make docs
26 | 
27 |       - name: Validate documentation
28 |         run: sphinx-build -b linkcheck docs build/docs


--------------------------------------------------------------------------------
/.github/ISSUE_TEMPLATE/feature_request.md:
--------------------------------------------------------------------------------
 1 | ---
 2 | name: Feature request
 3 | about: Suggest an idea for this project
 4 | title: ''
 5 | labels: ''
 6 | assignees: 'kyegomez'
 7 | 
 8 | ---
 9 | 
10 | **Is your feature request related to a problem? Please describe.**
11 | A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
12 | 
13 | **Describe the solution you'd like**
14 | A clear and concise description of what you want to happen.
15 | 
16 | **Describe alternatives you've considered**
17 | A clear and concise description of any alternative solutions or features you've considered.
18 | 
19 | **Additional context**
20 | Add any other context or screenshots about the feature request here.
21 | 


--------------------------------------------------------------------------------
/.github/FUNDING.yml:
--------------------------------------------------------------------------------
 1 | # These are supported funding model platforms
 2 | 
 3 | github: [kyegomez]
 4 | patreon: # Replace with a single Patreon username
 5 | open_collective: # Replace with a single Open Collective username
 6 | ko_fi: # Replace with a single Ko-fi username
 7 | tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel
 8 | community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry
 9 | liberapay: # Replace with a single Liberapay username
10 | issuehunt: # Replace with a single IssueHunt username
11 | otechie: # Replace with a single Otechie username
12 | lfx_crowdfunding: # Replace with a single LFX Crowdfunding project-name e.g., cloud-foundry
13 | custom: #Nothing
14 | 


--------------------------------------------------------------------------------
/.github/workflows/unit-test.yml:
--------------------------------------------------------------------------------
 1 | name: build
 2 | 
 3 | on:
 4 |   push:
 5 |     branches: [ main ]
 6 |   pull_request:
 7 |     branches: [ main ]
 8 | 
 9 | jobs:
10 | 
11 |   build:
12 | 
13 |     runs-on: ubuntu-latest
14 | 
15 |     steps:
16 |     - uses: actions/checkout@v4
17 | 
18 |     - name: Setup Python
19 |       uses: actions/setup-python@v5
20 |       with:
21 |         python-version: '3.10'
22 | 
23 |     - name: Install dependencies
24 |       run: pip install --no-cache-dir -r requirements.txt
25 | 
26 |     - name: Run Python unit tests
27 |       run: python3 -m unittest tests/
28 | 
29 |     - name: Verify that the Docker image for the action builds
30 |       run: docker build . --file Dockerfile
31 | 
32 |     - name: Verify integration test results
33 |       run: python3 -m unittest tests/
34 | 


--------------------------------------------------------------------------------
/.github/ISSUE_TEMPLATE/bug_report.md:
--------------------------------------------------------------------------------
 1 | ---
 2 | name: Bug report
 3 | about: Create a detailed report on the bug and it's root cause. Conduct root cause error analysis
 4 | title: "[BUG] "
 5 | labels: bug
 6 | assignees: kyegomez
 7 | 
 8 | ---
 9 | 
10 | **Describe the bug**
11 | A clear and concise description of what the bug is and what the main root cause error is. Test very thoroughly before submitting.
12 | 
13 | **To Reproduce**
14 | Steps to reproduce the behavior:
15 | 1. Go to '...'
16 | 2. Click on '....'
17 | 3. Scroll down to '....'
18 | 4. See error
19 | 
20 | **Expected behavior**
21 | A clear and concise description of what you expected to happen.
22 | 
23 | **Screenshots**
24 | If applicable, add screenshots to help explain your problem.
25 | 
26 | **Additional context**
27 | Add any other context about the problem here.
28 | 


--------------------------------------------------------------------------------
/.github/workflows/python-publish.yml:
--------------------------------------------------------------------------------
 1 | 
 2 | name: Upload Python Package
 3 | 
 4 | on:
 5 |   release:
 6 |     types: [published]
 7 | 
 8 | permissions:
 9 |   contents: read
10 | 
11 | jobs:
12 |   deploy:
13 | 
14 |     runs-on: ubuntu-latest
15 | 
16 |     steps:
17 |     - uses: actions/checkout@v4
18 |     - name: Set up Python
19 |       uses: actions/setup-python@v5
20 |       with:
21 |         python-version: '3.10'
22 |     - name: Install dependencies
23 |       run: |
24 |         python -m pip install --no-cache-dir --upgrade pip
25 |         pip install build
26 |     - name: Build package
27 |       run: python -m build
28 |     - name: Publish package
29 |       uses: pypa/gh-action-pypi-publish@ec4db0b4ddc65acdf4bff5fa45ac92d78b56bdf0
30 |       with:
31 |         user: __token__
32 |         password: ${{ secrets.PYPI_API_TOKEN }}


--------------------------------------------------------------------------------
/.github/workflows/code_quality_control.yml:
--------------------------------------------------------------------------------
 1 | name: Linting and Formatting
 2 | 
 3 | on:
 4 |   push:
 5 |     branches:
 6 |       - main
 7 | 
 8 | jobs:
 9 |   lint_and_format:
10 |     runs-on: ubuntu-latest
11 | 
12 |     steps:
13 |       - name: Checkout code
14 |         uses: actions/checkout@v4
15 | 
16 |       - name: Set up Python
17 |         uses: actions/setup-python@v5
18 |         with:
19 |           python-version: '3.10'
20 | 
21 |       - name: Install dependencies
22 |         run: pip install --no-cache-dir -r requirements.txt
23 | 
24 |       - name: Find Python files
25 |         run: find swarms_torch -name "*.py" -type f -exec autopep8 --in-place --aggressive --aggressive {} +
26 | 
27 |       - name: Push changes
28 |         uses: ad-m/github-push-action@master
29 |         with:
30 |           github_token: ${{ secrets.GITHUB_TOKEN }}


--------------------------------------------------------------------------------
/.github/workflows/stale.yml:
--------------------------------------------------------------------------------
 1 | # This workflow warns and then closes issues and PRs that have had no activity for a specified amount of time.
 2 | #
 3 | # You can adjust the behavior by modifying this file.
 4 | # For more information, see:
 5 | # https://github.com/actions/stale
 6 | name: Mark stale issues and pull requests
 7 | 
 8 | on:
 9 |   schedule:
10 |   - cron: '26 12 * * *'
11 | 
12 | jobs:
13 |   stale:
14 | 
15 |     runs-on: ubuntu-latest
16 |     permissions:
17 |       issues: write
18 |       pull-requests: write
19 | 
20 |     steps:
21 |     - uses: actions/stale@v9
22 |       with:
23 |         repo-token: ${{ secrets.GITHUB_TOKEN }}
24 |         stale-issue-message: 'Stale issue message'
25 |         stale-pr-message: 'Stale pull request message'
26 |         stale-issue-label: 'no-issue-activity'
27 |         stale-pr-label: 'no-pr-activity'


--------------------------------------------------------------------------------
/scripts/code_quality.sh:
--------------------------------------------------------------------------------
 1 | #!/bin/bash
 2 | 
 3 | # Navigate to the directory containing the 'package' folder
 4 | # cd /path/to/your/code/directory
 5 | 
 6 | # Run autopep8 with max aggressiveness (-aaa) and in-place modification (-i)
 7 | # on all Python files (*.py) under the 'package' directory.
 8 | autopep8 --in-place --aggressive --aggressive --recursive --experimental --list-fixes package/
 9 | 
10 | # Run black with default settings, since black does not have an aggressiveness level.
11 | # Black will format all Python files it finds in the 'package' directory.
12 | black --experimental-string-processing package/
13 | 
14 | # Run ruff on the 'package' directory.
15 | # Add any additional flags if needed according to your version of ruff.
16 | ruff --unsafe_fix
17 | 
18 | # YAPF
19 | yapf --recursive --in-place --verbose --style=google --parallel package
20 | 


--------------------------------------------------------------------------------
/scripts/merge_all_prs.sh:
--------------------------------------------------------------------------------
 1 | #!/bin/bash
 2 | 
 3 | # Check if we are inside a Git repository
 4 | if ! git rev-parse --git-dir > /dev/null 2>&1; then
 5 |     echo "Error: Must be run inside a Git repository."
 6 |     exit 1
 7 | fi
 8 | 
 9 | # Fetch all open pull requests
10 | echo "Fetching open PRs..."
11 | prs=$(gh pr list --state open --json number --jq '.[].number')
12 | 
13 | # Check if there are PRs to merge
14 | if [ -z "$prs" ]; then
15 |     echo "No open PRs to merge."
16 |     exit 0
17 | fi
18 | 
19 | echo "Found PRs: $prs"
20 | 
21 | # Loop through each pull request number and merge it
22 | for pr in $prs; do
23 |     echo "Attempting to merge PR #$pr"
24 |     merge_output=$(gh pr merge $pr --auto --merge)
25 |     merge_status=$?
26 |     if [ $merge_status -ne 0 ]; then
27 |         echo "Failed to merge PR #$pr. Error: $merge_output"
28 |     else
29 |         echo "Successfully merged PR #$pr"
30 |     fi
31 | done
32 | 
33 | echo "Processing complete."
34 | 


--------------------------------------------------------------------------------
/.github/workflows/cos_integration.yml:
--------------------------------------------------------------------------------
 1 | name: Continuous Integration
 2 | 
 3 | on:
 4 |   push:
 5 |     branches:
 6 |       - main
 7 | 
 8 | jobs:
 9 |   test:
10 |     runs-on: ubuntu-latest
11 |     steps:
12 |       - name: Checkout code
13 |         uses: actions/checkout@v4
14 | 
15 |       - name: Set up Python
16 |         uses: actions/setup-python@v5
17 |         with:
18 |           python-version: '3.10'
19 | 
20 |       - name: Install dependencies
21 |         run: pip install --no-cache-dir -r requirements.txt
22 | 
23 |       - name: Run unit tests
24 |         run: pytest tests/unit
25 | 
26 |       - name: Run integration tests
27 |         run: pytest tests/integration
28 | 
29 |       - name: Run code coverage
30 |         run: pytest --cov=swarms tests/
31 | 
32 |       - name: Run linters
33 |         run: pylint swarms
34 | 
35 |       - name: Build documentation
36 |         run: make docs
37 | 
38 |       - name: Validate documentation
39 |         run: sphinx-build -b linkcheck docs build/docs
40 | 
41 |       - name: Run performance tests
42 |         run: pytest tests/performance


--------------------------------------------------------------------------------
/.github/PULL_REQUEST_TEMPLATE.yml:
--------------------------------------------------------------------------------
 1 | <!-- Thank you for contributing to Zeta!
 2 | 
 3 | Replace this comment with:
 4 |   - Description: a description of the change, 
 5 |   - Issue: the issue # it fixes (if applicable),
 6 |   - Dependencies: any dependencies required for this change,
 7 |   - Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
 8 |   - Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
 9 | 
10 | If you're adding a new integration, please include:
11 |   1. a test for the integration, preferably unit tests that do not rely on network access,
12 |   2. an example notebook showing its use.
13 | 
14 | Maintainer responsibilities:
15 |   - nn / Misc / if you don't know who to tag: kye@apac.ai
16 |   - tokenizers: kye@apac.ai
17 |   - training / Prompts: kye@apac.ai
18 |   - models: kye@apac.ai
19 | 
20 | If no one reviews your PR within a few days, feel free to kye@apac.ai
21 | 
22 | See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/kyegomez/zeta


--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
 1 | MIT License
 2 | 
 3 | Copyright (c) 2023 Eternal Reclaimer
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 | 


--------------------------------------------------------------------------------
/.github/workflows/test.yml:
--------------------------------------------------------------------------------
 1 | name: test
 2 | 
 3 | on:
 4 |   push:
 5 |     branches: [master]
 6 |   pull_request:
 7 |   workflow_dispatch:
 8 | 
 9 | env:
10 |   POETRY_VERSION: "1.4.2"
11 | 
12 | jobs:
13 |   build:
14 |     runs-on: ubuntu-latest
15 |     strategy:
16 |       matrix:
17 |         python-version:
18 |           - "3.9"
19 |           - "3.10"
20 |           - "3.11"
21 |         test_type:
22 |           - "core"
23 |           - "extended"
24 |     name: Python ${{ matrix.python-version }} ${{ matrix.test_type }}
25 |     steps:
26 |       - uses: actions/checkout@v4
27 |       - name: Set up Python ${{ matrix.python-version }}
28 |         uses: "./.github/actions/poetry_setup"
29 |         with:
30 |           python-version: ${{ matrix.python-version }}
31 |           poetry-version: "1.4.2"
32 |           cache-key: ${{ matrix.test_type }}
33 |           install-command: |
34 |               if [ "${{ matrix.test_type }}" == "core" ]; then
35 |                 echo "Running core tests, installing dependencies with poetry..."
36 |                 poetry install
37 |               else
38 |                 echo "Running extended tests, installing dependencies with poetry..."
39 |                 poetry install -E extended_testing
40 |               fi
41 |       - name: Run ${{matrix.test_type}} tests
42 |         run: |
43 |           if [ "${{ matrix.test_type }}" == "core" ]; then
44 |             make test
45 |           else
46 |             make extended_tests
47 |           fi
48 |         shell: bash


--------------------------------------------------------------------------------
/pyproject.toml:
--------------------------------------------------------------------------------
 1 | [build-system]
 2 | requires = ["poetry-core>=1.0.0"]
 3 | build-backend = "poetry.core.masonry.api"
 4 | 
 5 | [tool.poetry]
 6 | name = "paper"
 7 | version = "0.0.1"
 8 | description = "Paper - Pytorch"
 9 | license = "MIT"
10 | authors = ["Kye Gomez <kye@apac.ai>"]
11 | homepage = "https://github.com/kyegomez/paper"
12 | documentation = "https://github.com/kyegomez/paper"  # Add this if you have documentation.
13 | readme = "README.md"  # Assuming you have a README.md
14 | repository = "https://github.com/kyegomez/paper"
15 | keywords = ["artificial intelligence", "deep learning", "optimizers", "Prompt Engineering"]
16 | classifiers = [
17 |     "Development Status :: 4 - Beta",
18 |     "Intended Audience :: Developers",
19 |     "Topic :: Scientific/Engineering :: Artificial Intelligence",
20 |     "License :: OSI Approved :: MIT License",
21 |     "Programming Language :: Python :: 3.9"
22 | ]
23 | 
24 | [tool.poetry.dependencies]
25 | python = "^3.10"
26 | swarms = "*"
27 | zetascale = "*"
28 | 
29 | [tool.poetry.dev-dependencies]
30 | # Add development dependencies here
31 | 
32 | 
33 | [tool.poetry.group.lint.dependencies]
34 | ruff = "^0.1.6"
35 | types-toml = "^0.10.8.1"
36 | types-redis = "^4.3.21.6"
37 | types-pytz = "^2023.3.0.0"
38 | black = "^23.1.0"
39 | types-chardet = "^5.0.4.6"
40 | mypy-protobuf = "^3.0.0"
41 | 
42 | 
43 | [tool.autopep8]
44 | max_line_length = 80
45 | ignore = "E501,W6"  # or ["E501", "W6"]
46 | in-place = true
47 | recursive = true
48 | aggressive = 3
49 | 
50 | 
51 | [tool.ruff]
52 | line-length = 70
53 | 
54 | [tool.black]
55 | line-length = 70
56 | target-version = ['py38']
57 | preview = true
58 | 


--------------------------------------------------------------------------------
/.github/labeler.yml:
--------------------------------------------------------------------------------
 1 | # this is a config file for the github action labeler
 2 | 
 3 | # Add 'root' label to any root file changes
 4 | # Quotation marks are required for the leading asterisk
 5 | root:
 6 | - changed-files:
 7 |   - any-glob-to-any-file: '*'
 8 | 
 9 | # Add 'Documentation' label to any changes within 'docs' folder or any subfolders
10 | Documentation:
11 | - changed-files:
12 |   - any-glob-to-any-file: docs/**
13 | 
14 | # Add 'Tests' label to any file changes within 'docs' folder
15 | Tests:
16 | - changed-files:
17 |   - any-glob-to-any-file: tests/*
18 | 
19 | # Add 'Documentation' label to any file changes within 'docs' or 'guides' folders
20 | ghactions:
21 | - changed-files:
22 |   - any-glob-to-any-file:
23 |     - .github/workflows/*
24 |     - .github/*
25 | 
26 | # Add 'Scripts' label to any file changes within 'docs' folder
27 | Scripts:
28 | - changed-files:
29 |   - any-glob-to-any-file: scripts/*
30 |   
31 | ## Equivalent of the above mentioned configuration using another syntax
32 | Documentation:
33 | - changed-files:
34 |   - any-glob-to-any-file: ['docs/*', 'guides/*']
35 | 
36 | # Add 'Documentation' label to any change to .md files within the entire repository 
37 | Documentation:
38 | - changed-files:
39 |   - any-glob-to-any-file: '**/*.md'
40 | 
41 | # Add 'source' label to any change to src files within the source dir EXCEPT for the docs sub-folder
42 | source:
43 | - all:
44 |   - changed-files:
45 |     - any-glob-to-any-file: 'src/**/*'
46 |     - all-globs-to-all-files: '!src/docs/*'
47 | 
48 | # Add 'feature' label to any PR where the head branch name starts with `feature` or has a `feature` section in the name
49 | feature:
50 |  - head-branch: ['^feature', 'feature']
51 | 
52 | # Add 'release' label to any PR that is opened against the `main` branch
53 | release:
54 |  - base-branch: 'main'
55 | 


--------------------------------------------------------------------------------
/.gitignore:
--------------------------------------------------------------------------------
  1 | # Byte-compiled / optimized / DLL files
  2 | __pycache__/
  3 | *.py[cod]
  4 | *$py.class
  5 | 
  6 | # C extensions
  7 | *.so
  8 | .vscode/
  9 | .vscode
 10 | 
 11 | # Distribution / packaging
 12 | .Python
 13 | build/
 14 | develop-eggs/
 15 | dist/
 16 | downloads/
 17 | eggs/
 18 | .eggs/
 19 | lib/
 20 | lib64/
 21 | parts/
 22 | .ruff_cache/
 23 | sdist/
 24 | var/
 25 | wheels/
 26 | share/python-wheels/
 27 | *.egg-info/
 28 | .installed.cfg
 29 | *.egg
 30 | MANIFEST
 31 | 
 32 | # PyInstaller
 33 | #  Usually these files are written by a python script from a template
 34 | #  before PyInstaller builds the exe, so as to inject date/other infos into it.
 35 | *.manifest
 36 | *.spec
 37 | 
 38 | # Installer logs
 39 | pip-log.txt
 40 | pip-delete-this-directory.txt
 41 | 
 42 | # Unit test / coverage reports
 43 | htmlcov/
 44 | .tox/
 45 | .nox/
 46 | .coverage
 47 | .coverage.*
 48 | .cache
 49 | nosetests.xml
 50 | coverage.xml
 51 | *.cover
 52 | *.py,cover
 53 | .hypothesis/
 54 | .pytest_cache/
 55 | cover/
 56 | 
 57 | # Translations
 58 | *.mo
 59 | *.pot
 60 | 
 61 | # Django stuff:
 62 | *.log
 63 | local_settings.py
 64 | db.sqlite3
 65 | db.sqlite3-journal
 66 | 
 67 | # Flask stuff:
 68 | instance/
 69 | .webassets-cache
 70 | 
 71 | # Scrapy stuff:
 72 | .scrapy
 73 | 
 74 | # Sphinx documentation
 75 | docs/_build/
 76 | 
 77 | # PyBuilder
 78 | .pybuilder/
 79 | target/
 80 | 
 81 | # Jupyter Notebook
 82 | .ipynb_checkpoints
 83 | 
 84 | # IPython
 85 | profile_default/
 86 | ipython_config.py
 87 | 
 88 | # pyenv
 89 | #   For a library or package, you might want to ignore these files since the code is
 90 | #   intended to run in multiple environments; otherwise, check them in:
 91 | # .python-version
 92 | 
 93 | # pipenv
 94 | #   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
 95 | #   However, in case of collaboration, if having platform-specific dependencies or dependencies
 96 | #   having no cross-platform support, pipenv may install dependencies that don't work, or not
 97 | #   install all needed dependencies.
 98 | #Pipfile.lock
 99 | 
100 | # poetry
101 | #   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
102 | #   This is especially recommended for binary packages to ensure reproducibility, and is more
103 | #   commonly ignored for libraries.
104 | #   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
105 | #poetry.lock
106 | 
107 | # pdm
108 | #   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
109 | #pdm.lock
110 | #   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
111 | #   in version control.
112 | #   https://pdm.fming.dev/#use-with-ide
113 | .pdm.toml
114 | 
115 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
116 | __pypackages__/
117 | 
118 | # Celery stuff
119 | celerybeat-schedule
120 | celerybeat.pid
121 | 
122 | # SageMath parsed files
123 | *.sage.py
124 | 
125 | # Environments
126 | .env
127 | .venv
128 | env/
129 | venv/
130 | ENV/
131 | env.bak/
132 | venv.bak/
133 | 
134 | # Spyder project settings
135 | .spyderproject
136 | .spyproject
137 | 
138 | # Rope project settings
139 | .ropeproject
140 | 
141 | # mkdocs documentation
142 | /site
143 | 
144 | # mypy
145 | .mypy_cache/
146 | .dmypy.json
147 | dmypy.json
148 | 
149 | # Pyre type checker
150 | .pyre/
151 | 
152 | # pytype static type analyzer
153 | .pytype/
154 | 
155 | # Cython debug symbols
156 | cython_debug/
157 | 
158 | # PyCharm
159 | #  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
160 | #  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
161 | #  and can be added to the global gitignore or merged into this file.  For a more nuclear
162 | #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
163 | #.idea/
164 | 


--------------------------------------------------------------------------------
/lfm/model.py:
--------------------------------------------------------------------------------
  1 | import torch
  2 | import torch.nn as nn
  3 | import torch.nn.functional as F
  4 | from loguru import logger
  5 | from typing import Optional, Tuple
  6 | 
  7 | 
  8 | class AdaptiveLinear(nn.Module):
  9 |     """
 10 |     Adaptive Linear layer whose weight and bias adapt based on input.
 11 |     """
 12 | 
 13 |     def __init__(
 14 |         self, in_features: int, out_features: int, adapt_dim: int
 15 |     ):
 16 |         super(AdaptiveLinear, self).__init__()
 17 |         self.in_features = in_features
 18 |         self.out_features = out_features
 19 | 
 20 |         self.weight = nn.Parameter(
 21 |             torch.randn(out_features, in_features)
 22 |         )
 23 |         self.bias = nn.Parameter(torch.randn(out_features))
 24 | 
 25 |         # Linear transformation for adapting the weight based on input
 26 |         self.adapt = nn.Linear(adapt_dim, out_features * in_features)
 27 | 
 28 |     def forward(
 29 |         self, x: torch.Tensor, adapt_input: torch.Tensor
 30 |     ) -> torch.Tensor:
 31 |         adapt_weight = self.adapt(adapt_input).view(
 32 |             self.out_features, self.in_features
 33 |         )
 34 |         weight = self.weight + adapt_weight
 35 |         return F.linear(x, weight, self.bias)
 36 | 
 37 | 
 38 | class TokenMixing(nn.Module):
 39 |     """
 40 |     Token mixing layer that performs token-wise interactions using adaptive linear layers.
 41 |     Operates across the sequence dimension (sequence_length).
 42 |     """
 43 | 
 44 |     def __init__(self, token_dim: int, adapt_dim: int):
 45 |         super(TokenMixing, self).__init__()
 46 |         self.token_mixing = AdaptiveLinear(
 47 |             token_dim, token_dim, adapt_dim
 48 |         )
 49 | 
 50 |     def forward(
 51 |         self, x: torch.Tensor, adapt_input: torch.Tensor
 52 |     ) -> torch.Tensor:
 53 |         # x: [batch_size, sequence_length, embedding_dim]
 54 |         batch_size, seq_length, embed_dim = x.shape
 55 |         x = x.view(
 56 |             batch_size * seq_length, embed_dim
 57 |         )  # Flatten sequence for linear transformation
 58 |         x_mixed = self.token_mixing(x, adapt_input)
 59 |         return x_mixed.view(batch_size, seq_length, embed_dim)
 60 | 
 61 | 
 62 | class ChannelMixing(nn.Module):
 63 |     """
 64 |     Channel mixing layer that performs cross-channel interactions using adaptive linear layers.
 65 |     Operates across the embedding dimension (embedding_dim).
 66 |     """
 67 | 
 68 |     def __init__(self, channel_dim: int, adapt_dim: int):
 69 |         super(ChannelMixing, self).__init__()
 70 |         self.channel_mixing = AdaptiveLinear(
 71 |             channel_dim, channel_dim, adapt_dim
 72 |         )
 73 | 
 74 |     def forward(
 75 |         self, x: torch.Tensor, adapt_input: torch.Tensor
 76 |     ) -> torch.Tensor:
 77 |         # x: [batch_size, sequence_length, embedding_dim]
 78 |         return self.channel_mixing(x, adapt_input)
 79 | 
 80 | 
 81 | class MixtureOfExperts(nn.Module):
 82 |     """
 83 |     Mixture of Experts (MoE) module that dynamically selects experts based on input.
 84 |     Operates after channel and token mixing.
 85 |     """
 86 | 
 87 |     def __init__(
 88 |         self, expert_dim: int, num_experts: int, adapt_dim: int
 89 |     ):
 90 |         super(MixtureOfExperts, self).__init__()
 91 |         self.experts = nn.ModuleList(
 92 |             [
 93 |                 AdaptiveLinear(expert_dim, expert_dim, adapt_dim)
 94 |                 for _ in range(num_experts)
 95 |             ]
 96 |         )
 97 |         self.gating = nn.Linear(adapt_dim, num_experts)
 98 | 
 99 |     def forward(
100 |         self, x: torch.Tensor, adapt_input: torch.Tensor
101 |     ) -> torch.Tensor:
102 |         gate_scores = F.softmax(self.gating(adapt_input), dim=-1)
103 |         output = sum(
104 |             gate_scores[:, i].unsqueeze(1) * expert(x, adapt_input)
105 |             for i, expert in enumerate(self.experts)
106 |         )
107 |         return output
108 | 
109 | 
110 | class LFModel(nn.Module):
111 |     """
112 |     Custom LF Model architecture combining token mixing, channel mixing, and MoE.
113 |     Accepts 3D input tensor: [batch_size, sequence_length, embedding_dim].
114 |     """
115 | 
116 |     def __init__(
117 |         self,
118 |         token_dim: int,
119 |         channel_dim: int,
120 |         expert_dim: int,
121 |         adapt_dim: int,
122 |         num_experts: int,
123 |     ):
124 |         super(LFModel, self).__init__()
125 |         self.featurizer = nn.Linear(token_dim, adapt_dim)
126 |         self.token_mixer = TokenMixing(token_dim, adapt_dim)
127 |         self.channel_mixer = ChannelMixing(channel_dim, adapt_dim)
128 |         self.moe = MixtureOfExperts(
129 |             expert_dim, num_experts, adapt_dim
130 |         )
131 |         self.output_layer = nn.Linear(expert_dim, token_dim)
132 | 
133 |     def forward(self, x: torch.Tensor) -> torch.Tensor:
134 |         logger.info("Input shape: {}", x.shape)
135 | 
136 |         # Featurization stage
137 |         batch_size, seq_length, embed_dim = x.shape
138 |         adapt_input = self.featurizer(
139 |             x.mean(dim=1)
140 |         )  # Aggregate across sequence for adaptation
141 |         logger.info(
142 |             "Featurization complete. Shape: {}", adapt_input.shape
143 |         )
144 | 
145 |         # Token Mixing
146 |         token_mixed = self.token_mixer(x, adapt_input)
147 |         logger.info(
148 |             "Token mixing complete. Shape: {}", token_mixed.shape
149 |         )
150 | 
151 |         # Channel Mixing
152 |         channel_mixed = self.channel_mixer(token_mixed, adapt_input)
153 |         logger.info(
154 |             "Channel mixing complete. Shape: {}", channel_mixed.shape
155 |         )
156 | 
157 |         # Mixture of Experts
158 |         expert_output = self.moe(channel_mixed, adapt_input)
159 |         logger.info(
160 |             "Mixture of Experts complete. Shape: {}",
161 |             expert_output.shape,
162 |         )
163 | 
164 |         # Final Output
165 |         output = self.output_layer(expert_output)
166 |         logger.info("Output shape: {}", output.shape)
167 |         return output
168 | 
169 | 
170 | # Instantiate and test the model
171 | if __name__ == "__main__":
172 |     batch_size, seq_length, embedding_dim = 32, 128, 512
173 |     token_dim, channel_dim, expert_dim, adapt_dim, num_experts = (
174 |         embedding_dim,
175 |         embedding_dim,
176 |         embedding_dim,
177 |         128,
178 |         4,
179 |     )
180 |     model = LFModel(
181 |         token_dim, channel_dim, expert_dim, adapt_dim, num_experts
182 |     )
183 | 
184 |     input_tensor = torch.randn(
185 |         batch_size, seq_length, embedding_dim
186 |     )  # 3D text tensor
187 |     output = model(input_tensor)
188 |     logger.info("Model forward pass complete.")
189 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | <!-- [![Multi-Modality](agorabanner.png)](https://discord.com/servers/agora-999382051935506503) -->
 2 | 
 3 | # Liquid Foundation Models [LFMs]
 4 | 
 5 | [![Join our Discord](https://img.shields.io/badge/Discord-Join%20our%20server-5865F2?style=for-the-badge&logo=discord&logoColor=white)](https://discord.gg/agora-999382051935506503) [![Subscribe on YouTube](https://img.shields.io/badge/YouTube-Subscribe-red?style=for-the-badge&logo=youtube&logoColor=white)](https://www.youtube.com/@kyegomez3242) [![Connect on LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue?style=for-the-badge&logo=linkedin&logoColor=white)](https://www.linkedin.com/in/kye-g-38759a207/) [![Follow on X.com](https://img.shields.io/badge/X.com-Follow-1DA1F2?style=for-the-badge&logo=x&logoColor=white)](https://x.com/kyegomezb)
 6 | 
 7 | **Welcome to the open-source implementation of Liquid Foundation Models (LFMs)** — the next generation of multi-modal, adaptive AI systems. LFMs represent a breakthrough in model architecture design, specifically tailored to meet the evolving demands of real-world applications across different modalities such as text, audio, image, and video. [The article provides some implementation details](https://www.liquid.ai/liquid-foundation-models)
 8 | 
 9 | ## Overview
10 | 
11 | The Liquid Foundation Models are a series of generative AI models designed with an entirely new architecture, optimized for performance, scalability, and efficiency across a wide variety of hardware platforms and modalities. Unlike traditional GPT-based models, LFMs introduce a novel computational paradigm that redefines how we approach token and channel mixing, weight sharing, feature sharing, and adaptive computation, making them suitable for a broader range of applications.
12 | 
13 | LFMs are built to support multi-modal inputs, which allows them to process text, audio, images, and other data types seamlessly. This flexibility makes them ideal for scenarios where combining various forms of data can yield more powerful insights and predictions.
14 | 
15 | ## Key Features of LFMs
16 | 
17 | ### 1. **Multi-Modal Support**
18 |    LFMs natively support multiple data modalities such as text, audio, images, and video. This is achieved through a robust featurization process that converts raw data into structured feature representations. This architecture allows LFMs to unify different types of data and process them under a single framework, enabling richer and more complex tasks like multi-modal reasoning, understanding, and generation.
19 | 
20 | ### 2. **Token Mixing & Channel Mixing**
21 |    The architecture of LFMs incorporates specialized computational units that perform **token mixing** and **channel mixing**:
22 |    
23 |    - **Token Mixing**: This operation focuses on how the model interacts with and mixes embeddings within a sequence of tokens. By leveraging adaptive linear operations, LFMs dynamically modulate the way tokens interact, leading to more efficient and expressive token representations.
24 |    - **Channel Mixing**: Channel mixing refers to interactions between different layers or channels within the model. By introducing adaptive mechanisms at the channel level, LFMs enhance the ability to model complex relationships across different layers, ensuring efficient information flow throughout the network.
25 | 
26 | ### 3. **Adaptive Linear Operators**
27 |    At the heart of LFMs are **adaptive linear operators**. These operators adjust their computational actions based on the input they receive, allowing the model to be more flexible and contextually aware of the data it processes. This adaptiveness reduces the computational overhead typically seen in traditional linear operations while improving the model's performance on diverse data types.
28 | 
29 | ### 4. **Weight & Feature Sharing**
30 |    LFMs incorporate **weight sharing** across depth groups to improve parameter efficiency and reduce memory consumption without sacrificing performance. In addition, **feature sharing** between different featurizer interconnections allows the model to seamlessly transfer learned features between different modalities, resulting in faster convergence and better generalization.
31 | 
32 | ### 5. **Mixture of Experts (MoE)**
33 |    One of the key innovations in LFMs is the **Mixture of Experts (MoE)** layer. This mechanism dynamically selects a subset of experts (sub-models) to process specific inputs. This results in a more efficient model, as only a fraction of the network is used for any given task, significantly reducing computational requirements while maintaining high performance. LFMs with MoE can scale to larger parameter counts while preserving compute efficiency, making them suitable for both cloud and edge deployments.
34 | 
35 | ### 6. **Hardware Optimization**
36 |    LFMs are built with hardware efficiency in mind. They can be optimized for a range of platforms, including Qualcomm, Apple, Cerebras, and AMD, ensuring that the model delivers high throughput regardless of the deployment environment. This optimization not only boosts inference speeds but also enables LFMs to run on cost-effective hardware, democratizing access to high-performance AI.
37 | 
38 | ### 7. **Scalable Architecture**
39 |    With models ranging from 1 billion to over 40 billion parameters, LFMs offer scalability without compromise. The architecture has been designed to maximize performance at every size, and the combination of adaptive computation and MoE allows LFMs to outperform models that are significantly larger in size, offering both efficiency and precision.
40 | 
41 | ## Benefits of LFMs
42 | 
43 | ### 1. **Multi-Modal Capabilities**
44 |    LFMs’ ability to seamlessly integrate and process multi-modal data makes them a powerful tool for applications requiring cross-modal reasoning, such as video analysis with audio context, or multi-modal document understanding. This allows enterprises to leverage a single model architecture to handle diverse data types, reducing the need for separate models and increasing operational efficiency.
45 | 
46 | ### 2. **Superior Performance at Reduced Costs**
47 |    By incorporating MoE and adaptive linear operations, LFMs are able to maintain performance levels comparable to models much larger than themselves. This reduces the cost of inference and training, making LFMs an ideal choice for organizations looking to scale AI without incurring prohibitive hardware costs.
48 | 
49 | ### 3. **Optimized for Real-World Use**
50 |    LFMs are designed with real-world applications in mind, with targeted optimizations for deployment on edge devices, cloud environments, and specialized hardware like GPUs and AI accelerators. Their hardware adaptability ensures smooth operation across diverse deployment environments, from mobile devices to large-scale data centers.
51 | 
52 | ### 4. **Efficient Training and Inference**
53 |    LFMs' unique architecture reduces the overall number of computations required per task, speeding up both training and inference times. Enterprises can expect faster model iterations, quicker deployment of new features, and a more efficient feedback loop for continuous improvement.
54 | 
55 | ### 5. **Scalability and Flexibility**
56 |    With LFMs, enterprises can scale their AI capabilities flexibly. Whether working with smaller 1B parameter models or larger 40B models, LFMs provide scalability that is driven by the needs of the task rather than hardware constraints. This allows businesses to balance performance with cost-effectiveness depending on their specific application.
57 | 
58 | ### 6. **Enterprise-Ready Design**
59 |    LFMs are architected with a focus on deployment at scale. The model’s ability to optimize for different hardware platforms means enterprises can quickly integrate LFMs into their existing workflows without major infrastructure overhauls. Moreover, the inclusion of the Mixture of Experts layer provides higher throughput and lower latency, crucial for time-sensitive applications.
60 | 
61 | ## Conclusion
62 | 
63 | Liquid Foundation Models (LFMs) mark the next step in the evolution of generative AI models. With their innovative architecture, LFMs provide unmatched performance across multi-modal tasks while being highly efficient and scalable. Their adaptive computational units, token and channel mixing, and Mixture of Experts (MoE) technology offer a new balance between model size, performance, and hardware compatibility.
64 | 
65 | LFMs are the foundation for the future of AI systems, designed to be deployed in enterprise environments, across diverse hardware setups, and to handle real-world tasks that span multiple data types.
66 | 
67 | This open-source implementation provides a glimpse into the powerful potential of LFMs, enabling businesses and researchers to experiment with the most advanced model architectures available today.
68 | 
69 | For more information on the Liquid Foundation Models and their broader applications, visit [Liquid AI](https://www.liquid.ai/liquid-foundation-models).
70 | 
71 | ---
72 | 


--------------------------------------------------------------------------------