Commit graph

5 commits

Author SHA1 Message Date
varunfb
f5c36c47ed
Added support for llama 3.3 model (#601)
# What does this PR do?

Llama-Stack does not support the 3.3 model. So added the support so
llama-stack can do inferencing with 3.3 model.
2024-12-10 20:03:31 -08:00
Ashwin Bharambe
e84d4436b5
Since we are pushing for HF repos, we should accept them in inference configs (#497)
# What does this PR do?

As the title says. 

## Test Plan

This needs
8752149f58
to also land. So the next package (0.0.54) will make this work properly.

The test is:

```bash
pytest -v -s -m "llama_3b and meta_reference" test_model_registration.py
```
2024-11-20 16:14:37 -08:00
Dinesh Yeduguru
57a9b4d57f
Allow models to be registered as long as llama model is provided (#472)
This PR allows models to be registered with provider as long as the user
specifies a llama model, even though the model does not match our
prebuilt provider specific mapping.
Test:
pytest -v -s
llama_stack/providers/tests/inference/test_model_registration.py -m
"together" --env TOGETHER_API_KEY=<KEY>

---------

Co-authored-by: Dinesh Yeduguru <dineshyv@fb.com>
2024-11-18 15:05:29 -08:00
Ashwin Bharambe
0a3999a9a4
Use inference APIs for executing Llama Guard (#121)
We should use Inference APIs to execute Llama Guard instead of directly needing to use HuggingFace modeling related code. The actual inference consideration is handled by Inference.
2024-09-28 15:40:06 -07:00
Ashwin Bharambe
9487ad8294
API Updates (#73)
* API Keys passed from Client instead of distro configuration

* delete distribution registry

* Rename the "package" word away

* Introduce a "Router" layer for providers

Some providers need to be factorized and considered as thin routing
layers on top of other providers. Consider two examples:

- The inference API should be a routing layer over inference providers,
  routed using the "model" key
- The memory banks API is another instance where various memory bank
  types will be provided by independent providers (e.g., a vector store
  is served by Chroma while a keyvalue memory can be served by Redis or
  PGVector)

This commit introduces a generalized routing layer for this purpose.

* update `apis_to_serve`

* llama_toolchain -> llama_stack

* Codemod from llama_toolchain -> llama_stack

- added providers/registry
- cleaned up api/ subdirectories and moved impls away
- restructured api/api.py
- from llama_stack.apis.<api> import foo should work now
- update imports to do llama_stack.apis.<api>
- update many other imports
- added __init__, fixed some registry imports
- updated registry imports
- create_agentic_system -> create_agent
- AgenticSystem -> Agent

* Moved some stuff out of common/; re-generated OpenAPI spec

* llama-toolchain -> llama-stack (hyphens)

* add control plane API

* add redis adapter + sqlite provider

* move core -> distribution

* Some more toolchain -> stack changes

* small naming shenanigans

* Removing custom tool and agent utilities and moving them client side

* Move control plane to distribution server for now

* Remove control plane from API list

* no codeshield dependency randomly plzzzzz

* Add "fire" as a dependency

* add back event loggers

* stack configure fixes

* use brave instead of bing in the example client

* add init file so it gets packaged

* add init files so it gets packaged

* Update MANIFEST

* bug fix

---------

Co-authored-by: Hardik Shah <hjshah@fb.com>
Co-authored-by: Xi Yan <xiyan@meta.com>
Co-authored-by: Ashwin Bharambe <ashwin@meta.com>
2024-09-17 19:51:35 -07:00