Commit graph

321 commits

Author SHA1 Message Date
Xi Yan
2c23a66300
Merge branch 'main' into evals_new 2024-10-15 10:20:03 -07:00
Xi Yan
be4f395032 full evals / full scoring flow 2024-10-15 10:17:45 -07:00
Xi Yan
cccd5be090 move eval_task_config to client 2024-10-15 10:14:35 -07:00
Xi Yan
d2b62157a3 openapi gen 2024-10-15 00:44:54 -07:00
Xi Yan
9cc0a54f0b rag correctness scorer w/ custom dataset 2024-10-15 00:42:03 -07:00
Xi Yan
ec6c63ba57 dataset accept file uploads 2024-10-14 23:36:15 -07:00
Xi Yan
3c29108b6e input query optional input for braintrust scorer 2024-10-14 21:17:16 -07:00
Xi Yan
7b5895003a braintrust scorer 2024-10-14 21:09:59 -07:00
Xi Yan
c8f6849291 full accuracy 2024-10-14 20:42:22 -07:00
Xi Yan
fcb8dea1ef scorer only api 2024-10-14 17:46:29 -07:00
Xi Yan
a22c31b8a4 processor registry 2024-10-14 16:25:06 -07:00
Xi Yan
95fd53d292 registry refactor 2024-10-14 16:09:55 -07:00
Xi Yan
c50686b6fe scorer registry 2024-10-14 15:41:31 -07:00
Xi Yan
9c501d042b cleanup hardcoded dataset registry 2024-10-14 14:19:15 -07:00
Xi Yan
a9210cd416 datasets api crud 2024-10-14 13:54:16 -07:00
Xi Yan
f046899a1c datasets api 2024-10-14 13:16:39 -07:00
Xi Yan
209cd3d35e Bump version to 0.0.42 2024-10-14 11:13:04 -07:00
Xi Yan
18fe966e96 registry refactor 2024-10-14 00:12:46 -07:00
Xi Yan
78cb88c3c4 RunEvalTask / InferenceGenerator 2024-10-13 23:48:15 -07:00
Xi Yan
8890de7322 cleanup original BaseTask 2024-10-13 23:30:21 -07:00
Xi Yan
a25aff290e generator + scorer Api for MMLU 2024-10-13 23:27:02 -07:00
Yuan Tang
a2b87ed0cb
Switch to pre-commit/action (#239) 2024-10-11 11:09:11 -07:00
Yuan Tang
05282d1234
Enable pre-commit on main branch (#237) 2024-10-11 10:03:59 -07:00
Xi Yan
fb565dfb06 eleuther eval fix 2024-10-11 09:30:10 -07:00
Yuan Tang
2128e61da2
Fix incorrect completion() signature for Databricks provider (#236) 2024-10-11 08:47:57 -07:00
Xi Yan
ad18dc94ac add data structure to tasks 2024-10-10 21:33:13 -07:00
Dalton Flanagan
9fbe8852aa
Add Swift Package Index badge 2024-10-10 23:39:25 -04:00
Xi Yan
ca29980c6b fix agents context retriever 2024-10-10 20:17:29 -07:00
Xi Yan
9816c9aae6 wip add datatypes 2024-10-10 19:56:19 -07:00
Xi Yan
99ed1425fc add dataset datatypes 2024-10-10 17:19:18 -07:00
Ashwin Bharambe
1ff0476002 Split off meta-reference-quantized provider 2024-10-10 16:03:19 -07:00
Xi Yan
7ff5800dea generate openapi 2024-10-10 15:30:34 -07:00
Dalton Flanagan
a3e65d58a9
Add logo 2024-10-10 15:04:21 -04:00
Xi Yan
c8de439d9f clean 2024-10-10 11:38:37 -07:00
Xi Yan
31c046dcdf evals new rebase 2024-10-10 11:35:26 -07:00
Russell Bryant
eba9d1ea14
ci: Run pre-commit checks in CI (#176)
Run the pre-commit checks in a github workflow to validate that a PR
or a direct push to the repo does not introduce new errors.
2024-10-10 11:21:59 -07:00
Ashwin Bharambe
89d24a07f0 Bump version to 0.0.41 2024-10-10 10:27:03 -07:00
Ashwin Bharambe
6bb57e72a7
Remove "routing_table" and "routing_key" concepts for the user (#201)
This PR makes several core changes to the developer experience surrounding Llama Stack.

Background: PR #92 introduced the notion of "routing" to the Llama Stack. It introduces three object types: (1) models, (2) shields and (3) memory banks. Each of these objects can be associated with a distinct provider. So you can get model A to be inferenced locally while model B, C can be inference remotely (e.g.)

However, this had a few drawbacks:

you could not address the provider instances -- i.e., if you configured "meta-reference" with a given model, you could not assign an identifier to this instance which you could re-use later.
the above meant that you could not register a "routing_key" (e.g. model) dynamically and say "please use this existing provider I have already configured" for a new model.
the terms "routing_table" and "routing_key" were exposed directly to the user. in my view, this is way too much overhead for a new user (which almost everyone is.) people come to the stack wanting to do ML and encounter a completely unexpected term.
What this PR does: This PR structures the run config with only a single prominent key:

- providers
Providers are instances of configured provider types. Here's an example which shows two instances of the remote::tgi provider which are serving two different models.

providers:
  inference:
  - provider_id: foo
    provider_type: remote::tgi
    config: { ... }
  - provider_id: bar
    provider_type: remote::tgi
    config: { ... }
Secondly, the PR adds dynamic registration of { models | shields | memory_banks } to the API surface. The distribution still acts like a "routing table" (as previously) except that it asks the backing providers for a listing of these objects. For example it asks a TGI or Ollama inference adapter what models it is serving. Only the models that are being actually served can be requested by the user for inference. Otherwise, the Stack server will throw an error.

When dynamically registering these objects, you can use the provider IDs shown above. Info about providers can be obtained using the Api.inspect set of endpoints (/providers, /routes, etc.)

The above examples shows the correspondence between inference providers and models registry items. Things work similarly for the safety <=> shields and memory <=> memory_banks pairs.

Registry: This PR also makes it so that Providers need to implement additional methods for registering and listing objects. For example, each Inference provider is now expected to implement the ModelsProtocolPrivate protocol (naming is not great!) which consists of two methods

register_model
list_models
The goal is to inform the provider that a certain model needs to be supported so the provider can make any relevant backend changes if needed (or throw an error if the model cannot be supported.)

There are many other cleanups included some of which are detailed in a follow-up comment.
2024-10-10 10:24:13 -07:00
Dalton Flanagan
8c3010553f
Fix agents path in generate.py 2024-10-10 11:41:03 -04:00
Dalton Flanagan
7a8aa775e5
JSON serialization for parallel processing queue (#232)
* send/recv pydantic json over socket

* fixup

* address feedback

* bidirectional wrapper

* second round of feedback
2024-10-09 17:24:12 -04:00
kebbbnnn
0f66ae0f61
Add function for stopping inference (#224) 2024-10-09 10:50:19 -04:00
Xi Yan
6b094b72d3
Update cli_reference.md 2024-10-08 15:32:06 -07:00
Xi Yan
ce70d21f65
Add files via upload 2024-10-08 15:29:19 -07:00
Dalton Flanagan
2d4f7d8acf
Create SECURITY.md 2024-10-08 13:30:40 -04:00
Yuan Tang
48d0d2001e
Add classifiers in setup.py (#217)
* Add classifiers in setup.py

* Update setup.py

* Update setup.py
2024-10-08 06:55:16 -07:00
Xi Yan
4d5f7459aa
[bugfix] Fix logprobs on meta-reference impl (#213)
* fix log probs

* add back LogProbsConfig

* error handling

* bugfix
2024-10-07 19:42:39 -07:00
Yuan Tang
e4ae09d090
Add .idea to .gitignore (#216)
Signed-off-by: Yuan Tang <terrytangyuan@gmail.com>
2024-10-07 19:38:43 -07:00
Xi Yan
16ba0fa06f
Update README.md 2024-10-07 11:24:27 -07:00
Russell Bryant
996efa9b42
README.md: Add vLLM to providers table (#207)
Signed-off-by: Russell Bryant <russell.bryant@gmail.com>
2024-10-07 10:26:52 -07:00
Xi Yan
2366e18873
refactor docs (#209) 2024-10-07 10:21:26 -07:00