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Merge branch 'abetlen:main' into main
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‎.dockerignore

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_skbuild/
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.envrc
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models/
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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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share/python-wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.nox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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*.py,cover
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.hypothesis/
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.pytest_cache/
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cover/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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local_settings.py
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db.sqlite3
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db.sqlite3-journal
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# Flask stuff:
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instance/
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.webassets-cache
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# Scrapy stuff:
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.scrapy
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# Sphinx documentation
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docs/_build/
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# PyBuilder
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.pybuilder/
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target/
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# Jupyter Notebook
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.ipynb_checkpoints
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# IPython
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profile_default/
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ipython_config.py
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# pyenv
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# For a library or package, you might want to ignore these files since the code is
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# intended to run in multiple environments; otherwise, check them in:
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# .python-version
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# pipenv
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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# However, in case of collaboration, if having platform-specific dependencies or dependencies
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# having no cross-platform support, pipenv may install dependencies that don't work, or not
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# install all needed dependencies.
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#Pipfile.lock
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# poetry
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# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
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# This is especially recommended for binary packages to ensure reproducibility, and is more
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# commonly ignored for libraries.
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# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
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#poetry.lock
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# pdm
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# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
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#pdm.lock
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# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
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# in version control.
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# https://pdm.fming.dev/#use-with-ide
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.pdm.toml
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
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__pypackages__/
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# Celery stuff
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celerybeat-schedule
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celerybeat.pid
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# SageMath parsed files
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*.sage.py
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# Environments
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.env
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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# Spyder project settings
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.spyderproject
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.spyproject
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# Rope project settings
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.ropeproject
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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.dmypy.json
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dmypy.json
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# Pyre type checker
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.pyre/
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# pytype static type analyzer
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.pytype/
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# Cython debug symbols
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cython_debug/
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# PyCharm
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# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
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# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
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# and can be added to the global gitignore or merged into this file. For a more nuclear
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# option (not recommended) you can uncomment the following to ignore the entire idea folder.
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.idea/

‎.github/workflows/build-docker.yaml

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name: Build Docker
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on: workflow_dispatch
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permissions:
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contents: write
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packages: write
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jobs:
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docker:
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name: Build and push Docker image
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runs-on: ubuntu-latest
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steps:
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- name: Checkout
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uses: actions/checkout@v3
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with:
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submodules: "true"
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- name: Set up QEMU
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uses: docker/setup-qemu-action@v2
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- name: Set up Docker Buildx
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uses: docker/setup-buildx-action@v2
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- name: Login to GitHub Container Registry
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uses: docker/login-action@v2
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with:
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registry: ghcr.io
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username: ${{ github.repository_owner }}
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password: ${{ secrets.GITHUB_TOKEN }}
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- name: Build and push
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uses: docker/build-push-action@v4
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with:
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context: .
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push: true # push to registry
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pull: true # always fetch the latest base images
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platforms: linux/amd64,linux/arm64 # build for both amd64 and arm64
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tags: ghcr.io/abetlen/llama-cpp-python:latest

‎.github/workflows/publish.yaml

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# if: startsWith(github.ref, 'refs/tags')
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uses: pypa/gh-action-pypi-publish@release/v1
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with:
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password: ${{ secrets.PYPI_API_TOKEN }}
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password: ${{ secrets.PYPI_API_TOKEN }}

‎Dockerfile

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FROM python:3-bullseye
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# We need to set the host to 0.0.0.0 to allow outside access
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ENV HOST 0.0.0.0
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COPY . .
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# Install the package
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RUN apt update && apt install -y libopenblas-dev
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RUN python -m pip install --upgrade pip pytest cmake scikit-build setuptools fastapi uvicorn sse-starlette
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RUN LLAMA_OPENBLAS=1 python3 setup.py develop
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# Run the server
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CMD python3 -m llama_cpp.server

‎README.md

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Navigate to [http://localhost:8000/docs](http://localhost:8000/docs) to see the OpenAPI documentation.
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## Docker image
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A Docker image is available on [GHCR](https://ghcr.io/abetlen/llama-cpp-python). To run the server:
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```bash
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docker run --rm -it -p8000:8000 -v /path/to/models:/models -eMODEL=/models/ggml-model-name.bin ghcr.io/abetlen/llama-cpp-python:latest
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```
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## Low-level API
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The low-level API is a direct `ctypes` binding to the C API provided by `llama.cpp`.
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To get started, clone the repository and install the package in development mode:
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```bash
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git clone git@github.com:abetlen/llama-cpp-python.git
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git submodule update --init --recursive
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git clone --recurse-submodules git@github.com:abetlen/llama-cpp-python.git
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# Will need to be re-run any time vendor/llama.cpp is updated
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python3 setup.py develop
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```

‎llama_cpp/llama.py

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llama_cpp.llama_sample_typical(
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ctx=self.ctx,
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candidates=llama_cpp.ctypes.pointer(candidates),
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p=llama_cpp.c_float(1.0)
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p=llama_cpp.c_float(1.0),
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)
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llama_cpp.llama_sample_top_p(
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ctx=self.ctx,
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self.detokenize([token]).decode("utf-8", errors="ignore")
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for token in all_tokens
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]
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all_logprobs = [
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[Llama.logit_to_logprob(logit) for logit in row]
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for row in self.eval_logits
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]
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all_logprobs = [Llama._logits_to_logprobs(row) for row in self.eval_logits]
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for token, token_str, logprobs_token in zip(
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all_tokens, all_token_strs, all_logprobs
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):
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@staticmethod
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def logit_to_logprob(x: float) -> float:
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return math.log(1.0 + math.exp(x))
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def logits_to_logprobs(logits: List[llama_cpp.c_float]) -> List[llama_cpp.c_float]:
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exps = [math.exp(float(x)) for x in logits]
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sum_exps = sum(exps)
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return [llama_cpp.c_float(math.log(x / sum_exps)) for x in exps]

‎llama_cpp/llama_cpp.py

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) # tok_embeddings.weight and output.weight are F16
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LLAMA_FTYPE_MOSTLY_Q4_2 = ctypes.c_int(5) # except 1d tensors
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# LLAMA_FTYPE_MOSTYL_Q4_3 = ctypes.c_int(6) # except 1d tensors
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LLAMA_FTYPE_MOSTYL_Q8_0 = ctypes.c_int(7) # except 1d tensors
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LLAMA_FTYPE_MOSTYL_Q5_0 = ctypes.c_int(8) # except 1d tensors
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LLAMA_FTYPE_MOSTYL_Q5_1 = ctypes.c_int(9) # except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q8_0 = ctypes.c_int(7) # except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q5_0 = ctypes.c_int(8) # except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q5_1 = ctypes.c_int(9) # except 1d tensors
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# Functions
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‎llama_cpp/server/__main__.py

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import os
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import uvicorn
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from llama_cpp.server.app import app, init_llama
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from llama_cpp.server.app import create_app
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if __name__ == "__main__":
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init_llama()
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app = create_app()
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uvicorn.run(
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app, host=os.getenv("HOST", "localhost"), port=int(os.getenv("PORT", 8000))

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