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Merge branch 'meta-llama:main' into feat/litellm_sambanova_usage
This commit is contained in:
Jorge Piedrahita Ortiz
GitHub
commit
716cb09056
145 changed files with 21384 additions and 1283 deletions
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@ -8,12 +8,12 @@ Features:
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- Remote Inferencing: Perform inferencing tasks remotely with Llama models hosted on a remote connection (or serverless localhost).
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- Simple Integration: With easy-to-use APIs, a developer can quickly integrate Llama Stack in their Android app. The difference with local vs remote inferencing is also minimal.
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Latest Release Notes: [v0.0.58](https://github.com/meta-llama/llama-stack-client-kotlin/releases/tag/v0.0.58)
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Latest Release Notes: [link](https://github.com/meta-llama/llama-stack-client-kotlin/tree/latest-release)
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*Tagged releases are stable versions of the project. While we strive to maintain a stable main branch, it's not guaranteed to be free of bugs or issues.*
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## Android Demo App
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Check out our demo app to see how to integrate Llama Stack into your Android app: [Android Demo App](https://github.com/meta-llama/llama-stack-apps/tree/android-kotlin-app-latest/examples/android_app)
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Check out our demo app to see how to integrate Llama Stack into your Android app: [Android Demo App](https://github.com/meta-llama/llama-stack-client-kotlin/tree/examples/android_app)
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The key files in the app are `ExampleLlamaStackLocalInference.kt`, `ExampleLlamaStackRemoteInference.kts`, and `MainActivity.java`. With encompassed business logic, the app shows how to use Llama Stack for both the environments.
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@ -24,7 +24,7 @@ The key files in the app are `ExampleLlamaStackLocalInference.kt`, `ExampleLlama
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Add the following dependency in your `build.gradle.kts` file:
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```
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dependencies {
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implementation("com.llama.llamastack:llama-stack-client-kotlin:0.0.58")
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implementation("com.llama.llamastack:llama-stack-client-kotlin:0.1.4.2")
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}
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```
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This will download jar files in your gradle cache in a directory like `~/.gradle/caches/modules-2/files-2.1/com.llama.llamastack/`
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@ -36,13 +36,13 @@ If you plan on doing remote inferencing this is sufficient to get started.
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For local inferencing, it is required to include the ExecuTorch library into your app.
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Include the ExecuTorch library by:
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1. Download the `download-prebuilt-et-lib.sh` script file from the [llama-stack-client-kotlin-client-local](https://github.com/meta-llama/llama-stack-client-kotlin/blob/release/0.0.58/llama-stack-client-kotlin-client-local/download-prebuilt-et-lib.sh) directory to your local machine.
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1. Download the `download-prebuilt-et-lib.sh` script file from the [llama-stack-client-kotlin-client-local](https://github.com/meta-llama/llama-stack-client-kotlin/tree/latest-release/llama-stack-client-kotlin-client-local/download-prebuilt-et-lib.sh) directory to your local machine.
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2. Move the script to the top level of your Android app where the app directory resides:
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<p align="center">
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<img src="https://raw.githubusercontent.com/meta-llama/llama-stack-client-kotlin/refs/heads/release/0.0.58/doc/img/example_android_app_directory.png" style="width:300px">
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<img src="https://github.com/meta-llama/llama-stack-client-kotlin/blob/latest-release/doc/img/example_android_app_directory.png" style="width:300px">
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</p>
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3. Run `sh download-prebuilt-et-lib.sh` to create an `app/libs` directory and download the `executorch.aar` in that path. This generates an ExecuTorch library for the XNNPACK delegate with commit: [0a12e33](https://github.com/pytorch/executorch/commit/0a12e33d22a3d44d1aa2af5f0d0673d45b962553).
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3. Run `sh download-prebuilt-et-lib.sh` to create an `app/libs` directory and download the `executorch.aar` in that path. This generates an ExecuTorch library for the XNNPACK delegate.
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4. Add the `executorch.aar` dependency in your `build.gradle.kts` file:
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```
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dependencies {
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@ -58,12 +58,12 @@ Breaking down the demo app, this section will show the core pieces that are used
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### Setup Remote Inferencing
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Start a Llama Stack server on localhost. Here is an example of how you can do this using the firework.ai distribution:
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```
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conda create -n stack-fireworks python=3.10
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conda create -n stack-fireworks python=3.10
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conda activate stack-fireworks
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pip install llama-stack=0.0.58
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pip install --no-cache llama-stack==0.1.4
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llama stack build --template fireworks --image-type conda
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export FIREWORKS_API_KEY=<SOME_KEY>
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llama stack run /Users/<your_username>/.llama/distributions/llamastack-fireworks/fireworks-run.yaml --port=5050
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llama stack run fireworks --port 5050
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```
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Ensure the Llama Stack server version is the same as the Kotlin SDK Library for maximum compatibility.
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@ -146,7 +146,7 @@ The purpose of this section is to share more details with users that would like
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### Prerequisite
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You must complete the following steps:
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1. Clone the repo (`git clone https://github.com/meta-llama/llama-stack-client-kotlin.git -b release/0.0.58`)
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1. Clone the repo (`git clone https://github.com/meta-llama/llama-stack-client-kotlin.git -b latest-release`)
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2. Port the appropriate ExecuTorch libraries over into your Llama Stack Kotlin library environment.
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```
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cd llama-stack-client-kotlin-client-local
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@ -1,9 +1,8 @@
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# iOS SDK
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We offer both remote and on-device use of Llama Stack in Swift via two components:
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1. [llama-stack-client-swift](https://github.com/meta-llama/llama-stack-client-swift/)
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2. [LocalInferenceImpl](https://github.com/meta-llama/llama-stack/tree/main/llama_stack/providers/inline/ios/inference)
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We offer both remote and on-device use of Llama Stack in Swift via a single SDK [llama-stack-client-swift](https://github.com/meta-llama/llama-stack-client-swift/) that contains two components:
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1. LlamaStackClient for remote
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2. Local Inference for on-device
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```{image} ../../../_static/remote_or_local.gif
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:alt: Seamlessly switching between local, on-device inference and remote hosted inference
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@ -42,7 +41,7 @@ let request = Components.Schemas.CreateAgentTurnRequest(
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// ...
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```
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Check out [iOSCalendarAssistant](https://github.com/meta-llama/llama-stack-apps/tree/main/examples/ios_calendar_assistant) for a complete app demo.
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Check out [iOSCalendarAssistant](https://github.com/meta-llama/llama-stack-client-swift/tree/main/examples/ios_calendar_assistant) for a complete app demo.
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## LocalInference
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@ -58,7 +57,7 @@ let inference = LocalInference(queue: runnerQueue)
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let agents = LocalAgents(inference: self.inference)
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```
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Check out [iOSCalendarAssistantWithLocalInf](https://github.com/meta-llama/llama-stack-apps/tree/main/examples/ios_calendar_assistant) for a complete app demo.
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Check out [iOSCalendarAssistantWithLocalInf](https://github.com/meta-llama/llama-stack-client-swift/tree/main/examples/ios_calendar_assistant) for a complete app demo.
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### Installation
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@ -68,47 +67,6 @@ We're working on making LocalInference easier to set up. For now, you'll need t
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1. Install [Cmake](https://cmake.org/) for the executorch build`
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1. Drag `LocalInference.xcodeproj` into your project
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1. Add `LocalInference` as a framework in your app target
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1. Add a package dependency on https://github.com/pytorch/executorch (branch latest)
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1. Add all the kernels / backends from executorch (but not exectuorch itself!) as frameworks in your app target:
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- backend_coreml
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- backend_mps
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- backend_xnnpack
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- kernels_custom
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- kernels_optimized
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- kernels_portable
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- kernels_quantized
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1. In "Build Settings" > "Other Linker Flags" > "Any iOS Simulator SDK", add:
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```
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-force_load
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$(BUILT_PRODUCTS_DIR)/libkernels_optimized-simulator-release.a
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-force_load
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$(BUILT_PRODUCTS_DIR)/libkernels_custom-simulator-release.a
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-force_load
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$(BUILT_PRODUCTS_DIR)/libkernels_quantized-simulator-release.a
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-force_load
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$(BUILT_PRODUCTS_DIR)/libbackend_xnnpack-simulator-release.a
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-force_load
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$(BUILT_PRODUCTS_DIR)/libbackend_coreml-simulator-release.a
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-force_load
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$(BUILT_PRODUCTS_DIR)/libbackend_mps-simulator-release.a
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```
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1. In "Build Settings" > "Other Linker Flags" > "Any iOS SDK", add:
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```
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-force_load
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$(BUILT_PRODUCTS_DIR)/libkernels_optimized-simulator-release.a
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-force_load
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$(BUILT_PRODUCTS_DIR)/libkernels_custom-simulator-release.a
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-force_load
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$(BUILT_PRODUCTS_DIR)/libkernels_quantized-simulator-release.a
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-force_load
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$(BUILT_PRODUCTS_DIR)/libbackend_xnnpack-simulator-release.a
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-force_load
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$(BUILT_PRODUCTS_DIR)/libbackend_coreml-simulator-release.a
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-force_load
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$(BUILT_PRODUCTS_DIR)/libbackend_mps-simulator-release.a
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```
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### Preparing a model
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42
docs/source/distributions/self_hosted_distro/passthrough.md
Normal file
42
docs/source/distributions/self_hosted_distro/passthrough.md
Normal file
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@ -0,0 +1,42 @@
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---
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orphan: true
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---
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<!-- This file was auto-generated by distro_codegen.py, please edit source -->
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# Passthrough Distribution
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```{toctree}
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:maxdepth: 2
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:hidden:
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self
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```
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The `llamastack/distribution-passthrough` distribution consists of the following provider configurations.
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| API | Provider(s) |
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|-----|-------------|
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| agents | `inline::meta-reference` |
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| datasetio | `remote::huggingface`, `inline::localfs` |
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| eval | `inline::meta-reference` |
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| inference | `remote::passthrough`, `inline::sentence-transformers` |
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| safety | `inline::llama-guard` |
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| scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` |
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| telemetry | `inline::meta-reference` |
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| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `remote::wolfram-alpha`, `inline::code-interpreter`, `inline::rag-runtime`, `remote::model-context-protocol` |
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| vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
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### Environment Variables
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The following environment variables can be configured:
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- `LLAMA_STACK_PORT`: Port for the Llama Stack distribution server (default: `5001`)
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- `PASSTHROUGH_API_KEY`: Passthrough API Key (default: ``)
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- `PASSTHROUGH_URL`: Passthrough URL (default: ``)
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### Models
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The following models are available by default:
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- `llama3.1-8b-instruct `
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- `llama3.2-11b-vision-instruct `
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