mirror of
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* Add distribution CLI scaffolding * More progress towards `llama distribution install` * getting closer to a distro definition, distro install + configure works * Distribution server now functioning * read existing configuration, save enums properly * Remove inference uvicorn server entrypoint and llama inference CLI command * updated dependency and client model name * Improved exception handling * local imports for faster cli * undo a typo, add a passthrough distribution * implement full-passthrough in the server * add safety adapters, configuration handling, server + clients * cleanup, moving stuff to common, nuke utils * Add a Path() wrapper at the earliest place * fixes * Bring agentic system api to toolchain Add adapter dependencies and resolve adapters using a topological sort * refactor to reduce size of `agentic_system` * move straggler files and fix some important existing bugs * ApiSurface -> Api * refactor a method out * Adapter -> Provider * Make each inference provider into its own subdirectory * installation fixes * Rename Distribution -> DistributionSpec, simplify RemoteProviders * dict key instead of attr * update inference config to take model and not model_dir * Fix passthrough streaming, send headers properly not part of body :facepalm * update safety to use model sku ids and not model dirs * Update cli_reference.md * minor fixes * add DistributionConfig, fix a bug in model download * Make install + start scripts do proper configuration automatically * Update CLI_reference * Nuke fp8_requirements, fold fbgemm into common requirements * Update README, add newline between API surface configurations * Refactor download functionality out of the Command so can be reused * Add `llama model download` alias for `llama download` * Show message about checksum file so users can check themselves * Simpler intro statements * get ollama working * Reduce a bunch of dependencies from toolchain Some improvements to the distribution install script * Avoid using `conda run` since it buffers everything * update dependencies and rely on LLAMA_TOOLCHAIN_DIR for dev purposes * add validation for configuration input * resort imports * make optional subclasses default to yes for configuration * Remove additional_pip_packages; move deps to providers * for inline make 8b model the default * Add scripts to MANIFEST * allow installing from test.pypi.org * Fix #2 to help with testing packages * Must install llama-models at that same version first * fix PIP_ARGS --------- Co-authored-by: Hardik Shah <hjshah@fb.com> Co-authored-by: Hardik Shah <hjshah@meta.com>
63 lines
2.9 KiB
Markdown
63 lines
2.9 KiB
Markdown
# llama-toolchain
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[](https://pypi.org/project/llama-toolchain/)
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[](https://discord.gg/TZAAYNVtrU)
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This repository contains the specifications and implementations of the APIs which are part of the Llama Stack.
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The [Llama Stack](https://github.com/meta-llama/llama-toolchain/pull/8) defines and standardizes the building blocks needed to bring generative AI applications to market. These blocks span the entire development lifecycle: from model training and fine-tuning, through product evaluation, to invoking AI agents in production. Beyond definition, we're developing open-source versions and partnering with cloud providers, ensuring developers can assemble AI solutions using consistent, interlocking pieces across platforms. The ultimate goal is to accelerate innovation in the AI space.
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The Stack APIs are rapidly improving, but still very much Work in Progress and we invite feedback as well as direct contributions.
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## APIs
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The Llama Stack consists of the following set of APIs:
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- Inference
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- Safety
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- Memory
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- Agentic System
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- Evaluation
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- Post Training
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- Synthetic Data Generation
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- Reward Scoring
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Each of the APIs themselves is a collection of REST endpoints.
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## API Providers
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A Provider is what makes the API real -- they provide the actual implementation backing the API.
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As an example, for Inference, we could have the implementation be backed by primitives from `[ torch | vLLM | TensorRT ]` as possible options.
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A provider can also be just a pointer to a remote REST service -- for example, cloud providers like `[ aws | gcp ]` could possibly serve these APIs.
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## Llama Stack Distribution
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A Distribution is where APIs and Providers are assembled together to provide a consistent whole to the end application developer. You can mix-and-match providers -- some could be backed by inline code and some could be remote. As a hobbyist, you can serve a small model locally, but can choose a cloud provider for a large model. Regardless, the higher level APIs your app needs to work with don't need to change at all. You can even imagine moving across the server / mobile-device boundary as well always using the same uniform set of APIs for developing Generative AI applications.
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## Installation
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You can install this repository as a [package](https://pypi.org/project/llama-toolchain/) with `pip install llama-toolchain`
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If you want to install from source:
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```bash
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mkdir -p ~/local
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cd ~/local
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git clone git@github.com:meta-llama/llama-toolchain.git
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conda create -n toolchain python=3.10
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conda activate toolchain
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cd llama-toolchain
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pip install -e .
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```
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## The Llama CLI
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The `llama` CLI makes it easy to work with the Llama Stack set of tools, including installing and running Distributions, downloading models, studying model prompt formats, etc. Please see the [CLI reference](docs/cli_reference.md) for details.
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