forked from phoenix-oss/llama-stack-mirror
928a39d17b
Groq has never supported raw completions anyhow. So this makes it easier to switch it to LiteLLM. All our test suite passes. I also updated all the openai-compat providers so they work with api keys passed from headers. `provider_data` ## Test Plan ```bash LLAMA_STACK_CONFIG=groq \ pytest -s -v tests/client-sdk/inference/test_text_inference.py \ --inference-model=groq/llama-3.3-70b-versatile --vision-inference-model="" ``` Also tested (openai, anthropic, gemini) providers. No regressions.
37 lines
842 B
YAML
37 lines
842 B
YAML
version: '2'
|
|
distribution_spec:
|
|
description: Distribution for running e2e tests in CI
|
|
providers:
|
|
inference:
|
|
- remote::openai
|
|
- remote::fireworks
|
|
- remote::anthropic
|
|
- remote::gemini
|
|
- remote::groq
|
|
- inline::sentence-transformers
|
|
vector_io:
|
|
- inline::sqlite-vec
|
|
- remote::chromadb
|
|
- remote::pgvector
|
|
safety:
|
|
- inline::llama-guard
|
|
agents:
|
|
- inline::meta-reference
|
|
telemetry:
|
|
- inline::meta-reference
|
|
eval:
|
|
- inline::meta-reference
|
|
datasetio:
|
|
- remote::huggingface
|
|
- inline::localfs
|
|
scoring:
|
|
- inline::basic
|
|
- inline::llm-as-judge
|
|
- inline::braintrust
|
|
tool_runtime:
|
|
- remote::brave-search
|
|
- remote::tavily-search
|
|
- inline::code-interpreter
|
|
- inline::rag-runtime
|
|
- remote::model-context-protocol
|
|
image_type: conda
|