mirror of
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Restructure docs (#494)
Rendered docs at: https://llama-stack.readthedocs.io/en/doc-simplify/
This commit is contained in:
Dinesh Yeduguru
GitHub
parent
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# Llama Stack Distributions
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```{toctree}
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:maxdepth: 2
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:hidden:
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self_hosted_distro/index
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remote_hosted_distro/index
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ondevice_distro/index
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```
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## Introduction
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Llama Stack Distributions are pre-built Docker containers/Conda environments that assemble APIs and Providers to provide a consistent whole to the end application developer.
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These distributions allow you to mix-and-match providers - some could be backed by local code and some could be remote. This flexibility enables you to choose the optimal setup for your use case, such as serving a small model locally while using a cloud provider for larger models, all while maintaining a consistent API interface for your application.
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## Decide Your Build Type
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There are two ways to start a Llama Stack:
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- **Docker**: we provide a number of pre-built Docker containers allowing you to get started instantly. If you are focused on application development, we recommend this option.
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- **Conda**: the `llama` CLI provides a simple set of commands to build, configure and run a Llama Stack server containing the exact combination of providers you wish. We have provided various templates to make getting started easier.
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Both of these provide options to run model inference using our reference implementations, Ollama, TGI, vLLM or even remote providers like Fireworks, Together, Bedrock, etc.
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### Decide Your Inference Provider
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Running inference on the underlying Llama model is one of the most critical requirements. Depending on what hardware you have available, you have various options. Note that each option have different necessary prerequisites.
|
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- **Do you have access to a machine with powerful GPUs?**
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If so, we suggest:
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- [distribution-meta-reference-gpu](./self_hosted_distro/meta-reference-gpu.md)
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- [distribution-tgi](./self_hosted_distro/tgi.md)
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- **Are you running on a "regular" desktop machine?**
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If so, we suggest:
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- [distribution-ollama](./self_hosted_distro/ollama.md)
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|
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- **Do you have an API key for a remote inference provider like Fireworks, Together, etc.?** If so, we suggest:
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- [distribution-together](./remote_hosted_distro/together.md)
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- [distribution-fireworks](./remote_hosted_distro/fireworks.md)
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- **Do you want to run Llama Stack inference on your iOS / Android device** If so, we suggest:
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- [iOS](./ondevice_distro/ios_sdk.md)
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- [Android](https://github.com/meta-llama/llama-stack-client-kotlin) (coming soon)
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|
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Please see our pages in detail for the types of distributions we offer:
|
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|
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1. [Self-Hosted Distribution](./self_hosted_distro/index.md): If you want to run Llama Stack inference on your local machine.
|
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2. [Remote-Hosted Distribution](./remote_hosted_distro/index.md): If you want to connect to a remote hosted inference provider.
|
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3. [On-device Distribution](./ondevice_distro/index.md): If you want to run Llama Stack inference on your iOS / Android device.
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## Building Your Own Distribution
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### Prerequisites
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```bash
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$ git clone git@github.com:meta-llama/llama-stack.git
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```
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### Starting the Distribution
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::::{tab-set}
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:::{tab-item} meta-reference-gpu
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##### System Requirements
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Access to Single-Node GPU to start a local server.
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##### Downloading Models
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Please make sure you have Llama model checkpoints downloaded in `~/.llama` before proceeding. See [installation guide](../cli_reference/download_models.md) here to download the models.
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```
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$ ls ~/.llama/checkpoints
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Llama3.1-8B Llama3.2-11B-Vision-Instruct Llama3.2-1B-Instruct Llama3.2-90B-Vision-Instruct Llama-Guard-3-8B
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Llama3.1-8B-Instruct Llama3.2-1B Llama3.2-3B-Instruct Llama-Guard-3-1B Prompt-Guard-86M
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```
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:::
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:::{tab-item} vLLM
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##### System Requirements
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Access to Single-Node GPU to start a vLLM server.
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:::
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:::{tab-item} tgi
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##### System Requirements
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Access to Single-Node GPU to start a TGI server.
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:::
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:::{tab-item} ollama
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##### System Requirements
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Access to Single-Node CPU/GPU able to run ollama.
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:::
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:::{tab-item} together
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##### System Requirements
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Access to Single-Node CPU with Together hosted endpoint via API_KEY from [together.ai](https://api.together.xyz/signin).
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:::
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|
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:::{tab-item} fireworks
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##### System Requirements
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Access to Single-Node CPU with Fireworks hosted endpoint via API_KEY from [fireworks.ai](https://fireworks.ai/).
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:::
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::::
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::::{tab-set}
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:::{tab-item} meta-reference-gpu
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- [Start Meta Reference GPU Distribution](./self_hosted_distro/meta-reference-gpu.md)
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:::
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:::{tab-item} vLLM
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- [Start vLLM Distribution](./self_hosted_distro/remote-vllm.md)
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:::
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:::{tab-item} tgi
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- [Start TGI Distribution](./self_hosted_distro/tgi.md)
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:::
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:::{tab-item} ollama
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- [Start Ollama Distribution](./self_hosted_distro/ollama.md)
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:::
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|
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:::{tab-item} together
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- [Start Together Distribution](./self_hosted_distro/together.md)
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:::
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:::{tab-item} fireworks
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- [Start Fireworks Distribution](./self_hosted_distro/fireworks.md)
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:::
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::::
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### Troubleshooting
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- If you encounter any issues, search through our [GitHub Issues](https://github.com/meta-llama/llama-stack/issues), or file an new issue.
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- Use `--port <PORT>` flag to use a different port number. For docker run, update the `-p <PORT>:<PORT>` flag.
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@ -1,5 +1,12 @@
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# Remote-Hosted Distribution
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```{toctree}
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:maxdepth: 2
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:hidden:
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remote
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```
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|
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Remote-Hosted distributions are available endpoints serving Llama Stack API that you can directly connect to.
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|
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| Distribution | Endpoint | Inference | Agents | Memory | Safety | Telemetry |
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|
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@ -1,4 +1,10 @@
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# Bedrock 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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|
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### Connect to a Llama Stack Bedrock Endpoint
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- You may connect to Amazon Bedrock APIs for running LLM inference
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|
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@ -1,5 +1,12 @@
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# Dell-TGI 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-tgi` distribution consists of the following provider configurations.
|
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|
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|
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@ -1,5 +1,12 @@
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# Fireworks Distribution
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|
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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-fireworks` distribution consists of the following provider configurations.
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| API | Provider(s) |
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@ -1,20 +1,8 @@
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# Self-Hosted Distribution
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We offer deployable distributions where you can host your own Llama Stack server using local inference.
|
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|
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| **Distribution** | **Llama Stack Docker** | Start This Distribution | **Inference** | **Agents** | **Memory** | **Safety** | **Telemetry** |
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|:----------------: |:------------------------------------------: |:-----------------------: |:------------------: |:------------------: |:------------------: |:------------------: |:------------------: |
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| Meta Reference | [llamastack/distribution-meta-reference-gpu](https://hub.docker.com/repository/docker/llamastack/distribution-meta-reference-gpu/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/self_hosted_distro/meta-reference-gpu.html) | meta-reference | meta-reference | meta-reference; remote::pgvector; remote::chromadb | meta-reference | meta-reference |
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| Meta Reference Quantized | [llamastack/distribution-meta-reference-quantized-gpu](https://hub.docker.com/repository/docker/llamastack/distribution-meta-reference-quantized-gpu/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/self_hosted_distro/meta-reference-quantized-gpu.html) | meta-reference-quantized | meta-reference | meta-reference; remote::pgvector; remote::chromadb | meta-reference | meta-reference |
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| Ollama | [llamastack/distribution-ollama](https://hub.docker.com/repository/docker/llamastack/distribution-ollama/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/self_hosted_distro/ollama.html) | remote::ollama | meta-reference | remote::pgvector; remote::chromadb | meta-reference | meta-reference |
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| TGI | [llamastack/distribution-tgi](https://hub.docker.com/repository/docker/llamastack/distribution-tgi/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/self_hosted_distro/tgi.html) | remote::tgi | meta-reference | meta-reference; remote::pgvector; remote::chromadb | meta-reference | meta-reference |
|
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| Together | [llamastack/distribution-together](https://hub.docker.com/repository/docker/llamastack/distribution-together/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/self_hosted_distro/together.html) | remote::together | meta-reference | remote::weaviate | meta-reference | meta-reference |
|
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| Fireworks | [llamastack/distribution-fireworks](https://hub.docker.com/repository/docker/llamastack/distribution-fireworks/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/self_hosted_distro/fireworks.html) | remote::fireworks | meta-reference | remote::weaviate | meta-reference | meta-reference |
|
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| Bedrock | [llamastack/distribution-bedrock](https://hub.docker.com/repository/docker/llamastack/distribution-bedrock/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/self_hosted_distro/bedrock.html) | remote::bedrock | meta-reference | remote::weaviate | meta-reference | meta-reference |
|
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|
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|
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```{toctree}
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:maxdepth: 1
|
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:maxdepth: 2
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:hidden:
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|
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meta-reference-gpu
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meta-reference-quantized-gpu
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|
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@ -26,3 +14,15 @@ fireworks
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remote-vllm
|
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bedrock
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```
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|
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We offer deployable distributions where you can host your own Llama Stack server using local inference.
|
||||
|
||||
| **Distribution** | **Llama Stack Docker** | Start This Distribution |
|
||||
|:----------------: |:------------------------------------------: |:-----------------------: |
|
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| Meta Reference | [llamastack/distribution-meta-reference-gpu](https://hub.docker.com/repository/docker/llamastack/distribution-meta-reference-gpu/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/self_hosted_distro/meta-reference-gpu.html) |
|
||||
| Meta Reference Quantized | [llamastack/distribution-meta-reference-quantized-gpu](https://hub.docker.com/repository/docker/llamastack/distribution-meta-reference-quantized-gpu/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/self_hosted_distro/meta-reference-quantized-gpu.html) |
|
||||
| Ollama | [llamastack/distribution-ollama](https://hub.docker.com/repository/docker/llamastack/distribution-ollama/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/self_hosted_distro/ollama.html) |
|
||||
| TGI | [llamastack/distribution-tgi](https://hub.docker.com/repository/docker/llamastack/distribution-tgi/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/self_hosted_distro/tgi.html) |
|
||||
| Together | [llamastack/distribution-together](https://hub.docker.com/repository/docker/llamastack/distribution-together/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/self_hosted_distro/together.html) |
|
||||
| Fireworks | [llamastack/distribution-fireworks](https://hub.docker.com/repository/docker/llamastack/distribution-fireworks/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/self_hosted_distro/fireworks.html) |
|
||||
| Bedrock | [llamastack/distribution-bedrock](https://hub.docker.com/repository/docker/llamastack/distribution-bedrock/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/self_hosted_distro/bedrock.html) |
|
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|
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@ -1,5 +1,12 @@
|
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# Meta Reference Distribution
|
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|
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```{toctree}
|
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:maxdepth: 2
|
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:hidden:
|
||||
|
||||
self
|
||||
```
|
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|
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The `llamastack/distribution-meta-reference-gpu` distribution consists of the following provider configurations:
|
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|
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| API | Provider(s) |
|
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|
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@ -1,5 +1,12 @@
|
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# Meta Reference Quantized Distribution
|
||||
|
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```{toctree}
|
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:maxdepth: 2
|
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:hidden:
|
||||
|
||||
self
|
||||
```
|
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|
||||
The `llamastack/distribution-meta-reference-quantized-gpu` distribution consists of the following provider configurations.
|
||||
|
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|
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|
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@ -1,5 +1,12 @@
|
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# Ollama Distribution
|
||||
|
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```{toctree}
|
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:maxdepth: 2
|
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:hidden:
|
||||
|
||||
self
|
||||
```
|
||||
|
||||
The `llamastack/distribution-ollama` distribution consists of the following provider configurations.
|
||||
|
||||
| API | Provider(s) |
|
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|
|
@ -1,5 +1,12 @@
|
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# Remote vLLM Distribution
|
||||
|
||||
```{toctree}
|
||||
:maxdepth: 2
|
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:hidden:
|
||||
|
||||
self
|
||||
```
|
||||
|
||||
The `llamastack/distribution-remote-vllm` distribution consists of the following provider configurations:
|
||||
|
||||
| API | Provider(s) |
|
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|
|
@ -1,5 +1,12 @@
|
|||
# TGI Distribution
|
||||
|
||||
```{toctree}
|
||||
:maxdepth: 2
|
||||
:hidden:
|
||||
|
||||
self
|
||||
```
|
||||
|
||||
The `llamastack/distribution-tgi` distribution consists of the following provider configurations.
|
||||
|
||||
| API | Provider(s) |
|
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|
|
@ -1,4 +1,11 @@
|
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# Fireworks Distribution
|
||||
# Together Distribution
|
||||
|
||||
```{toctree}
|
||||
:maxdepth: 2
|
||||
:hidden:
|
||||
|
||||
self
|
||||
```
|
||||
|
||||
The `llamastack/distribution-together` distribution consists of the following provider configurations.
|
||||
|
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|
|
@ -1,194 +1,208 @@
|
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# Getting Started
|
||||
# Getting Started with Llama Stack
|
||||
|
||||
```{toctree}
|
||||
:maxdepth: 2
|
||||
:hidden:
|
||||
|
||||
distributions/self_hosted_distro/index
|
||||
distributions/remote_hosted_distro/index
|
||||
distributions/ondevice_distro/index
|
||||
In this guide, we'll walk through using ollama as the inference provider and build a simple python application that uses the Llama Stack Client SDK
|
||||
|
||||
Llama stack consists of a distribution server and an accompanying client SDK. The distribution server can be configured for different providers for inference, memory, agents, evals etc. This configuration is defined in a yaml file called `run.yaml`.
|
||||
|
||||
Running inference on the underlying Llama model is one of the most critical requirements. Depending on what hardware you have available, you have various options. Note that each option have different necessary prerequisites. We will use ollama as the inference provider as it is the easiest to get started with.
|
||||
|
||||
### Step 1. Start the inference server
|
||||
```bash
|
||||
export LLAMA_STACK_PORT=5001
|
||||
export INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct"
|
||||
# ollama names this model differently, and we must use the ollama name when loading the model
|
||||
export OLLAMA_INFERENCE_MODEL="llama3.2:3b-instruct-fp16"
|
||||
ollama run $OLLAMA_INFERENCE_MODEL --keepalive 60m
|
||||
```
|
||||
|
||||
At the end of the guide, you will have learned how to:
|
||||
- get a Llama Stack server up and running
|
||||
- set up an agent (with tool-calling and vector stores) that works with the above server
|
||||
|
||||
To see more example apps built using Llama Stack, see [llama-stack-apps](https://github.com/meta-llama/llama-stack-apps/tree/main).
|
||||
|
||||
## Step 1. Starting Up Llama Stack Server
|
||||
|
||||
### Decide Your Build Type
|
||||
There are two ways to start a Llama Stack:
|
||||
|
||||
- **Docker**: we provide a number of pre-built Docker containers allowing you to get started instantly. If you are focused on application development, we recommend this option.
|
||||
- **Conda**: the `llama` CLI provides a simple set of commands to build, configure and run a Llama Stack server containing the exact combination of providers you wish. We have provided various templates to make getting started easier.
|
||||
|
||||
Both of these provide options to run model inference using our reference implementations, Ollama, TGI, vLLM or even remote providers like Fireworks, Together, Bedrock, etc.
|
||||
|
||||
### Decide Your Inference Provider
|
||||
|
||||
Running inference on the underlying Llama model is one of the most critical requirements. Depending on what hardware you have available, you have various options. Note that each option have different necessary prerequisites.
|
||||
|
||||
- **Do you have access to a machine with powerful GPUs?**
|
||||
If so, we suggest:
|
||||
- [distribution-meta-reference-gpu](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/self_hosted_distro/meta-reference-gpu.html)
|
||||
- [distribution-tgi](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/tgi.html)
|
||||
|
||||
- **Are you running on a "regular" desktop machine?**
|
||||
If so, we suggest:
|
||||
- [distribution-ollama](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/self_hosted_distro/ollama.html)
|
||||
|
||||
- **Do you have an API key for a remote inference provider like Fireworks, Together, etc.?** If so, we suggest:
|
||||
- [distribution-together](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/remote_hosted_distro/together.html)
|
||||
- [distribution-fireworks](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/remote_hosted_distro/fireworks.html)
|
||||
|
||||
- **Do you want to run Llama Stack inference on your iOS / Android device** If so, we suggest:
|
||||
- [iOS](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/ondevice_distro/ios_sdk.html)
|
||||
- [Android](https://github.com/meta-llama/llama-stack-client-kotlin) (coming soon)
|
||||
|
||||
Please see our pages in detail for the types of distributions we offer:
|
||||
|
||||
1. [Self-Hosted Distribution](./distributions/self_hosted_distro/index.md): If you want to run Llama Stack inference on your local machine.
|
||||
2. [Remote-Hosted Distribution](./distributions/remote_hosted_distro/index.md): If you want to connect to a remote hosted inference provider.
|
||||
3. [On-device Distribution](./distributions/ondevice_distro/index.md): If you want to run Llama Stack inference on your iOS / Android device.
|
||||
|
||||
|
||||
### Table of Contents
|
||||
|
||||
Once you have decided on the inference provider and distribution to use, use the following guides to get started.
|
||||
|
||||
##### 1.0 Prerequisite
|
||||
|
||||
```
|
||||
$ git clone git@github.com:meta-llama/llama-stack.git
|
||||
```
|
||||
|
||||
::::{tab-set}
|
||||
|
||||
:::{tab-item} meta-reference-gpu
|
||||
##### System Requirements
|
||||
Access to Single-Node GPU to start a local server.
|
||||
|
||||
##### Downloading Models
|
||||
Please make sure you have Llama model checkpoints downloaded in `~/.llama` before proceeding. See [installation guide](https://llama-stack.readthedocs.io/en/latest/cli_reference/download_models.html) here to download the models.
|
||||
|
||||
```
|
||||
$ ls ~/.llama/checkpoints
|
||||
Llama3.1-8B Llama3.2-11B-Vision-Instruct Llama3.2-1B-Instruct Llama3.2-90B-Vision-Instruct Llama-Guard-3-8B
|
||||
Llama3.1-8B-Instruct Llama3.2-1B Llama3.2-3B-Instruct Llama-Guard-3-1B Prompt-Guard-86M
|
||||
```
|
||||
|
||||
:::
|
||||
|
||||
:::{tab-item} vLLM
|
||||
##### System Requirements
|
||||
Access to Single-Node GPU to start a vLLM server.
|
||||
:::
|
||||
|
||||
:::{tab-item} tgi
|
||||
##### System Requirements
|
||||
Access to Single-Node GPU to start a TGI server.
|
||||
:::
|
||||
|
||||
:::{tab-item} ollama
|
||||
##### System Requirements
|
||||
Access to Single-Node CPU/GPU able to run ollama.
|
||||
:::
|
||||
|
||||
:::{tab-item} together
|
||||
##### System Requirements
|
||||
Access to Single-Node CPU with Together hosted endpoint via API_KEY from [together.ai](https://api.together.xyz/signin).
|
||||
:::
|
||||
|
||||
:::{tab-item} fireworks
|
||||
##### System Requirements
|
||||
Access to Single-Node CPU with Fireworks hosted endpoint via API_KEY from [fireworks.ai](https://fireworks.ai/).
|
||||
:::
|
||||
|
||||
::::
|
||||
|
||||
##### 1.1. Start the distribution
|
||||
|
||||
::::{tab-set}
|
||||
:::{tab-item} meta-reference-gpu
|
||||
- [Start Meta Reference GPU Distribution](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/self_hosted_distro/meta-reference-gpu.html)
|
||||
:::
|
||||
|
||||
:::{tab-item} vLLM
|
||||
- [Start vLLM Distribution](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/self_hosted_distro/remote-vllm.html)
|
||||
:::
|
||||
|
||||
:::{tab-item} tgi
|
||||
- [Start TGI Distribution](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/self_hosted_distro/tgi.html)
|
||||
:::
|
||||
|
||||
:::{tab-item} ollama
|
||||
- [Start Ollama Distribution](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/self_hosted_distro/ollama.html)
|
||||
:::
|
||||
|
||||
:::{tab-item} together
|
||||
- [Start Together Distribution](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/self_hosted_distro/together.html)
|
||||
:::
|
||||
|
||||
:::{tab-item} fireworks
|
||||
- [Start Fireworks Distribution](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/self_hosted_distro/fireworks.html)
|
||||
:::
|
||||
|
||||
::::
|
||||
|
||||
##### Troubleshooting
|
||||
- If you encounter any issues, search through our [GitHub Issues](https://github.com/meta-llama/llama-stack/issues), or file an new issue.
|
||||
- Use `--port <PORT>` flag to use a different port number. For docker run, update the `-p <PORT>:<PORT>` flag.
|
||||
|
||||
|
||||
## Step 2. Run Llama Stack App
|
||||
|
||||
### Chat Completion Test
|
||||
Once the server is set up, we can test it with a client to verify it's working correctly. The following command will send a chat completion request to the server's `/inference/chat_completion` API:
|
||||
### Step 2. Start the Llama Stack server
|
||||
|
||||
```bash
|
||||
$ curl http://localhost:5000/alpha/inference/chat-completion \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"model_id": "meta-llama/Llama-3.1-8B-Instruct",
|
||||
"messages": [
|
||||
export LLAMA_STACK_PORT=5001
|
||||
docker run \
|
||||
-it \
|
||||
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
|
||||
-v ~/.llama:/root/.llama \
|
||||
llamastack/distribution-ollama \
|
||||
--port $LLAMA_STACK_PORT \
|
||||
--env INFERENCE_MODEL=$INFERENCE_MODEL \
|
||||
--env OLLAMA_URL=http://host.docker.internal:11434
|
||||
|
||||
```
|
||||
|
||||
### Step 3. Use the Llama Stack client SDK
|
||||
```bash
|
||||
pip install llama-stack-client
|
||||
```
|
||||
|
||||
We will use the `llama-stack-client` CLI to check the connectivity to the server. This should be installed in your environment if you installed the SDK.
|
||||
```bash
|
||||
llama-stack-client --endpoint http://localhost:5001 models list
|
||||
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┓
|
||||
┃ identifier ┃ provider_id ┃ provider_resource_id ┃ metadata ┃
|
||||
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━┩
|
||||
│ meta-llama/Llama-3.2-3B-Instruct │ ollama │ llama3.2:3b-instruct-fp16 │ {} │
|
||||
└──────────────────────────────────┴─────────────┴───────────────────────────┴──────────┘
|
||||
```
|
||||
|
||||
Chat completion using the CLI
|
||||
```bash
|
||||
llama-stack-client --endpoint http://localhost:5001 inference chat_completion --message "hello, what model are you?"
|
||||
```
|
||||
|
||||
Simple python example using the client SDK
|
||||
```python
|
||||
from llama_stack_client import LlamaStackClient
|
||||
|
||||
client = LlamaStackClient(base_url="http://localhost:5001")
|
||||
|
||||
# List available models
|
||||
models = client.models.list()
|
||||
print(models)
|
||||
|
||||
# Simple chat completion
|
||||
response = client.inference.chat_completion(
|
||||
model_id="meta-llama/Llama-3.2-3B-Instruct",
|
||||
messages=[
|
||||
{"role": "system", "content": "You are a helpful assistant."},
|
||||
{"role": "user", "content": "Write me a 2 sentence poem about the moon"}
|
||||
{"role": "user", "content": "Write a haiku about coding"}
|
||||
]
|
||||
)
|
||||
print(response.completion_message.content)
|
||||
```
|
||||
|
||||
### Step 4. Your first RAG agent
|
||||
```python
|
||||
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||
# All rights reserved.
|
||||
#
|
||||
# This source code is licensed under the terms described in the LICENSE file in
|
||||
# the root directory of this source tree.
|
||||
|
||||
import asyncio
|
||||
|
||||
import fire
|
||||
|
||||
from llama_stack_client import LlamaStackClient
|
||||
from llama_stack_client.lib.agents.agent import Agent
|
||||
from llama_stack_client.lib.agents.event_logger import EventLogger
|
||||
from llama_stack_client.types import Attachment
|
||||
from llama_stack_client.types.agent_create_params import AgentConfig
|
||||
|
||||
|
||||
async def run_main(host: str, port: int, disable_safety: bool = False):
|
||||
urls = [
|
||||
"memory_optimizations.rst",
|
||||
"chat.rst",
|
||||
"llama3.rst",
|
||||
"datasets.rst",
|
||||
"qat_finetune.rst",
|
||||
"lora_finetune.rst",
|
||||
]
|
||||
|
||||
attachments = [
|
||||
Attachment(
|
||||
content=f"https://raw.githubusercontent.com/pytorch/torchtune/main/docs/source/tutorials/{url}",
|
||||
mime_type="text/plain",
|
||||
)
|
||||
for i, url in enumerate(urls)
|
||||
]
|
||||
|
||||
client = LlamaStackClient(
|
||||
base_url=f"http://{host}:{port}",
|
||||
)
|
||||
|
||||
available_shields = [shield.identifier for shield in client.shields.list()]
|
||||
if not available_shields:
|
||||
print("No available shields. Disable safety.")
|
||||
else:
|
||||
print(f"Available shields found: {available_shields}")
|
||||
available_models = [model.identifier for model in client.models.list()]
|
||||
if not available_models:
|
||||
raise ValueError("No available models")
|
||||
else:
|
||||
selected_model = available_models[0]
|
||||
print(f"Using model: {selected_model}")
|
||||
|
||||
agent_config = AgentConfig(
|
||||
model=selected_model,
|
||||
instructions="You are a helpful assistant",
|
||||
sampling_params={
|
||||
"strategy": "greedy",
|
||||
"temperature": 1.0,
|
||||
"top_p": 0.9,
|
||||
},
|
||||
tools=[
|
||||
{
|
||||
"type": "memory",
|
||||
"memory_bank_configs": [],
|
||||
"query_generator_config": {"type": "default", "sep": " "},
|
||||
"max_tokens_in_context": 4096,
|
||||
"max_chunks": 10,
|
||||
},
|
||||
],
|
||||
"sampling_params": {"temperature": 0.7, "seed": 42, "max_tokens": 512}
|
||||
}'
|
||||
tool_choice="auto",
|
||||
tool_prompt_format="json",
|
||||
input_shields=available_shields if available_shields else [],
|
||||
output_shields=available_shields if available_shields else [],
|
||||
enable_session_persistence=False,
|
||||
)
|
||||
|
||||
Output:
|
||||
{'completion_message': {'role': 'assistant',
|
||||
'content': 'The moon glows softly in the midnight sky, \nA beacon of wonder, as it catches the eye.',
|
||||
'stop_reason': 'out_of_tokens',
|
||||
'tool_calls': []},
|
||||
'logprobs': null}
|
||||
agent = Agent(client, agent_config)
|
||||
session_id = agent.create_session("test-session")
|
||||
print(f"Created session_id={session_id} for Agent({agent.agent_id})")
|
||||
|
||||
user_prompts = [
|
||||
(
|
||||
"I am attaching some documentation for Torchtune. Help me answer questions I will ask next.",
|
||||
attachments,
|
||||
),
|
||||
(
|
||||
"What are the top 5 topics that were explained? Only list succinct bullet points.",
|
||||
None,
|
||||
),
|
||||
(
|
||||
"Was anything related to 'Llama3' discussed, if so what?",
|
||||
None,
|
||||
),
|
||||
(
|
||||
"Tell me how to use LoRA",
|
||||
None,
|
||||
),
|
||||
(
|
||||
"What about Quantization?",
|
||||
None,
|
||||
),
|
||||
]
|
||||
|
||||
for prompt in user_prompts:
|
||||
response = agent.create_turn(
|
||||
messages=[
|
||||
{
|
||||
"role": "user",
|
||||
"content": prompt[0],
|
||||
}
|
||||
],
|
||||
attachments=prompt[1],
|
||||
session_id=session_id,
|
||||
)
|
||||
|
||||
async for log in EventLogger().log(response):
|
||||
log.print()
|
||||
|
||||
|
||||
def main(host: str, port: int):
|
||||
asyncio.run(run_main(host, port))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
fire.Fire(main)
|
||||
```
|
||||
|
||||
### Run Agent App
|
||||
## Next Steps
|
||||
|
||||
To run an agent app, check out examples demo scripts with client SDKs to talk with the Llama Stack server in our [llama-stack-apps](https://github.com/meta-llama/llama-stack-apps/tree/main/examples) repo. To run a simple agent app:
|
||||
- You can mix and match different providers for inference, memory, agents, evals etc. See [Building custom distributions](../distributions/index.md)
|
||||
- [Developer Cookbook](developer_cookbook.md)
|
||||
|
||||
```bash
|
||||
$ git clone git@github.com:meta-llama/llama-stack-apps.git
|
||||
$ cd llama-stack-apps
|
||||
$ pip install -r requirements.txt
|
||||
|
||||
$ python -m examples.agents.client <host> <port>
|
||||
```
|
||||
|
||||
You will see outputs of the form --
|
||||
```
|
||||
User> I am planning a trip to Switzerland, what are the top 3 places to visit?
|
||||
inference> Switzerland is a beautiful country with a rich history, stunning landscapes, and vibrant culture. Here are three must-visit places to add to your itinerary:
|
||||
...
|
||||
|
||||
User> What is so special about #1?
|
||||
inference> Jungfraujoch, also known as the "Top of Europe," is a unique and special place for several reasons:
|
||||
...
|
||||
|
||||
User> What other countries should I consider to club?
|
||||
inference> Considering your interest in Switzerland, here are some neighboring countries that you may want to consider visiting:
|
||||
```
|
||||
For example applications and more detailed tutorials, visit our [llama-stack-apps](https://github.com/meta-llama/llama-stack-apps/tree/main/examples) repository.
|
||||
|
|
|
|||
|
|
@ -7,8 +7,7 @@ The Stack APIs are rapidly improving but still a work-in-progress. We invite fee
|
|||
|
||||
```{image} ../_static/llama-stack.png
|
||||
:alt: Llama Stack
|
||||
:width: 600px
|
||||
:align: center
|
||||
:width: 400px
|
||||
```
|
||||
|
||||
## APIs
|
||||
|
|
@ -86,8 +85,10 @@ You can find more example scripts with client SDKs to talk with the Llama Stack
|
|||
:maxdepth: 3
|
||||
|
||||
getting_started/index
|
||||
cli_reference/index
|
||||
cli_reference/download_models
|
||||
distributions/index
|
||||
llama_cli_reference/index
|
||||
llama_cli_reference/download_models
|
||||
llama_stack_client_cli_reference/index
|
||||
api_providers/index
|
||||
distribution_dev/index
|
||||
```
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
# CLI Reference
|
||||
# llama CLI Reference
|
||||
|
||||
The `llama` CLI tool helps you setup and use the Llama Stack. It should be available on your path after installing the `llama-stack` package.
|
||||
|
||||
|
|
@ -119,7 +119,7 @@ You should see a table like this:
|
|||
|
||||
To download models, you can use the llama download command.
|
||||
|
||||
#### Downloading from [Meta](https://llama.meta.com/llama-downloads/)
|
||||
### Downloading from [Meta](https://llama.meta.com/llama-downloads/)
|
||||
|
||||
Here is an example download command to get the 3B-Instruct/11B-Vision-Instruct model. You will need META_URL which can be obtained from [here](https://llama.meta.com/docs/getting_the_models/meta/)
|
||||
|
||||
|
|
@ -137,7 +137,7 @@ llama download --source meta --model-id Prompt-Guard-86M --meta-url META_URL
|
|||
llama download --source meta --model-id Llama-Guard-3-1B --meta-url META_URL
|
||||
```
|
||||
|
||||
#### Downloading from [Hugging Face](https://huggingface.co/meta-llama)
|
||||
### Downloading from [Hugging Face](https://huggingface.co/meta-llama)
|
||||
|
||||
Essentially, the same commands above work, just replace `--source meta` with `--source huggingface`.
|
||||
|
||||
162
docs/source/llama_stack_client_cli_reference/index.md
Normal file
162
docs/source/llama_stack_client_cli_reference/index.md
Normal file
|
|
@ -0,0 +1,162 @@
|
|||
# llama-stack-client CLI Reference
|
||||
|
||||
You may use the `llama-stack-client` to query information about the distribution.
|
||||
|
||||
## Basic Commands
|
||||
|
||||
### `llama-stack-client`
|
||||
```bash
|
||||
$ llama-stack-client -h
|
||||
|
||||
usage: llama-stack-client [-h] {models,memory_banks,shields} ...
|
||||
|
||||
Welcome to the LlamaStackClient CLI
|
||||
|
||||
options:
|
||||
-h, --help show this help message and exit
|
||||
|
||||
subcommands:
|
||||
{models,memory_banks,shields}
|
||||
```
|
||||
|
||||
### `llama-stack-client configure`
|
||||
```bash
|
||||
$ llama-stack-client configure
|
||||
> Enter the host name of the Llama Stack distribution server: localhost
|
||||
> Enter the port number of the Llama Stack distribution server: 5000
|
||||
Done! You can now use the Llama Stack Client CLI with endpoint http://localhost:5000
|
||||
```
|
||||
|
||||
## Provider Commands
|
||||
|
||||
### `llama-stack-client providers list`
|
||||
```bash
|
||||
$ llama-stack-client providers list
|
||||
```
|
||||
```
|
||||
+-----------+----------------+-----------------+
|
||||
| API | Provider ID | Provider Type |
|
||||
+===========+================+=================+
|
||||
| scoring | meta0 | meta-reference |
|
||||
+-----------+----------------+-----------------+
|
||||
| datasetio | meta0 | meta-reference |
|
||||
+-----------+----------------+-----------------+
|
||||
| inference | tgi0 | remote::tgi |
|
||||
+-----------+----------------+-----------------+
|
||||
| memory | meta-reference | meta-reference |
|
||||
+-----------+----------------+-----------------+
|
||||
| agents | meta-reference | meta-reference |
|
||||
+-----------+----------------+-----------------+
|
||||
| telemetry | meta-reference | meta-reference |
|
||||
+-----------+----------------+-----------------+
|
||||
| safety | meta-reference | meta-reference |
|
||||
+-----------+----------------+-----------------+
|
||||
```
|
||||
|
||||
## Model Management
|
||||
|
||||
### `llama-stack-client models list`
|
||||
```bash
|
||||
$ llama-stack-client models list
|
||||
```
|
||||
```
|
||||
+----------------------+----------------------+---------------+----------------------------------------------------------+
|
||||
| identifier | llama_model | provider_id | metadata |
|
||||
+======================+======================+===============+==========================================================+
|
||||
| Llama3.1-8B-Instruct | Llama3.1-8B-Instruct | tgi0 | {'huggingface_repo': 'meta-llama/Llama-3.1-8B-Instruct'} |
|
||||
+----------------------+----------------------+---------------+----------------------------------------------------------+
|
||||
```
|
||||
|
||||
### `llama-stack-client models get`
|
||||
```bash
|
||||
$ llama-stack-client models get Llama3.1-8B-Instruct
|
||||
```
|
||||
|
||||
```
|
||||
+----------------------+----------------------+----------------------------------------------------------+---------------+
|
||||
| identifier | llama_model | metadata | provider_id |
|
||||
+======================+======================+==========================================================+===============+
|
||||
| Llama3.1-8B-Instruct | Llama3.1-8B-Instruct | {'huggingface_repo': 'meta-llama/Llama-3.1-8B-Instruct'} | tgi0 |
|
||||
+----------------------+----------------------+----------------------------------------------------------+---------------+
|
||||
```
|
||||
|
||||
|
||||
```bash
|
||||
$ llama-stack-client models get Random-Model
|
||||
|
||||
Model RandomModel is not found at distribution endpoint host:port. Please ensure endpoint is serving specified model.
|
||||
```
|
||||
|
||||
### `llama-stack-client models register`
|
||||
|
||||
```bash
|
||||
$ llama-stack-client models register <model_id> [--provider-id <provider_id>] [--provider-model-id <provider_model_id>] [--metadata <metadata>]
|
||||
```
|
||||
|
||||
### `llama-stack-client models update`
|
||||
|
||||
```bash
|
||||
$ llama-stack-client models update <model_id> [--provider-id <provider_id>] [--provider-model-id <provider_model_id>] [--metadata <metadata>]
|
||||
```
|
||||
|
||||
### `llama-stack-client models delete`
|
||||
|
||||
```bash
|
||||
$ llama-stack-client models delete <model_id>
|
||||
```
|
||||
|
||||
## Memory Bank Management
|
||||
|
||||
### `llama-stack-client memory_banks list`
|
||||
```bash
|
||||
$ llama-stack-client memory_banks list
|
||||
```
|
||||
```
|
||||
+--------------+----------------+--------+-------------------+------------------------+--------------------------+
|
||||
| identifier | provider_id | type | embedding_model | chunk_size_in_tokens | overlap_size_in_tokens |
|
||||
+==============+================+========+===================+========================+==========================+
|
||||
| test_bank | meta-reference | vector | all-MiniLM-L6-v2 | 512 | 64 |
|
||||
+--------------+----------------+--------+-------------------+------------------------+--------------------------+
|
||||
```
|
||||
|
||||
## Shield Management
|
||||
|
||||
### `llama-stack-client shields list`
|
||||
```bash
|
||||
$ llama-stack-client shields list
|
||||
```
|
||||
|
||||
```
|
||||
+--------------+----------+----------------+-------------+
|
||||
| identifier | params | provider_id | type |
|
||||
+==============+==========+================+=============+
|
||||
| llama_guard | {} | meta-reference | llama_guard |
|
||||
+--------------+----------+----------------+-------------+
|
||||
```
|
||||
|
||||
## Evaluation Tasks
|
||||
|
||||
### `llama-stack-client eval_tasks list`
|
||||
```bash
|
||||
$ llama-stack-client eval run_benchmark <task_id1> <task_id2> --num-examples 10 --output-dir ./ --eval-task-config ~/eval_task_config.json
|
||||
```
|
||||
|
||||
where `eval_task_config.json` is the path to the eval task config file in JSON format. An example eval_task_config
|
||||
```
|
||||
$ cat ~/eval_task_config.json
|
||||
{
|
||||
"type": "benchmark",
|
||||
"eval_candidate": {
|
||||
"type": "model",
|
||||
"model": "Llama3.1-405B-Instruct",
|
||||
"sampling_params": {
|
||||
"strategy": "greedy",
|
||||
"temperature": 0,
|
||||
"top_p": 0.95,
|
||||
"top_k": 0,
|
||||
"max_tokens": 0,
|
||||
"repetition_penalty": 1.0
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
Loading…
Add table
Add a link
Reference in a new issue