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feat(providers): Groq now uses LiteLLM openai-compat (#1303)

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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.
This commit is contained in:
Ashwin Bharambe 2025-02-27 13:16:50 -08:00 • committed by GitHub
parent 564f0e5f93
commit 928a39d17b
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23 changed files with 165 additions and 1004 deletions
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14
llama_stack/templates/groq/groq.py
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@ -16,9 +16,8 @@ from llama_stack.models.llama.sku_list import all_registered_models
from llama_stack.providers.inline.inference.sentence_transformers import (
SentenceTransformersInferenceConfig,
)
from llama_stack.providers.inline.vector_io.faiss.config import FaissVectorIOConfig
from llama_stack.providers.remote.inference.groq import GroqConfig
from llama_stack.providers.remote.inference.groq.models import _MODEL_ENTRIES
from llama_stack.providers.remote.inference.groq.models import MODEL_ENTRIES
from llama_stack.templates.template import DistributionTemplate, RunConfigSettings
@ -52,11 +51,6 @@ def get_distribution_template() -> DistributionTemplate:
provider_type="inline::sentence-transformers",
config=SentenceTransformersInferenceConfig.sample_run_config(),
)
vector_io_provider = Provider(
provider_id="faiss",
provider_type="inline::faiss",
config=FaissVectorIOConfig.sample_run_config(f"distributions/{name}"),
)
embedding_model = ModelInput(
model_id="all-MiniLM-L6-v2",
provider_id="sentence-transformers",
@ -69,11 +63,13 @@ def get_distribution_template() -> DistributionTemplate:
core_model_to_hf_repo = {m.descriptor(): m.huggingface_repo for m in all_registered_models()}
default_models = [
ModelInput(
model_id=core_model_to_hf_repo[m.llama_model],
model_id=core_model_to_hf_repo[m.llama_model] if m.llama_model else m.provider_model_id,
provider_model_id=m.provider_model_id,
provider_id=name,
model_type=m.model_type,
metadata=m.metadata,
)
for m in _MODEL_ENTRIES
for m in MODEL_ENTRIES
]
default_tool_groups = [