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feat: add static embedding metadata to dynamic model listings for providers using OpenAIMixin (#3547)

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# What does this PR do?

- remove auto-download of ollama embedding models
- add embedding model metadata to dynamic listing w/ unit test
- add support and tests for allowed_models
- removed inference provider models.py files where dynamic listing is
enabled
- store embedding metadata in embedding_model_metadata field on
inference providers
- make model_entries optional on ModelRegistryHelper and
LiteLLMOpenAIMixin
- make OpenAIMixin a ModelRegistryHelper
- skip base64 embedding test for remote::ollama, always returns floats
- only use OpenAI client for ollama model listing
- remove unused build_model_entry function
- remove unused get_huggingface_repo function


## Test Plan

ci w/ new tests
This commit is contained in:
Matthew Farrellee 2025-09-25 17:17:00 -04:00 • committed by GitHub
parent a50b63906c
commit b67aef2fc4
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43 changed files with 368 additions and 1015 deletions
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109
llama_stack/providers/remote/inference/nvidia/models.py
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@ -1,109 +0,0 @@
# 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.
from llama_stack.apis.models import ModelType
from llama_stack.models.llama.sku_types import CoreModelId
from llama_stack.providers.utils.inference.model_registry import (
ProviderModelEntry,
build_hf_repo_model_entry,
)
SAFETY_MODELS_ENTRIES = []
# https://docs.nvidia.com/nim/large-language-models/latest/supported-llm-agnostic-architectures.html
MODEL_ENTRIES = [
build_hf_repo_model_entry(
"meta/llama3-8b-instruct",
CoreModelId.llama3_8b_instruct.value,
),
build_hf_repo_model_entry(
"meta/llama3-70b-instruct",
CoreModelId.llama3_70b_instruct.value,
),
build_hf_repo_model_entry(
"meta/llama-3.1-8b-instruct",
CoreModelId.llama3_1_8b_instruct.value,
),
build_hf_repo_model_entry(
"meta/llama-3.1-70b-instruct",
CoreModelId.llama3_1_70b_instruct.value,
),
build_hf_repo_model_entry(
"meta/llama-3.1-405b-instruct",
CoreModelId.llama3_1_405b_instruct.value,
),
build_hf_repo_model_entry(
"meta/llama-3.2-1b-instruct",
CoreModelId.llama3_2_1b_instruct.value,
),
build_hf_repo_model_entry(
"meta/llama-3.2-3b-instruct",
CoreModelId.llama3_2_3b_instruct.value,
),
build_hf_repo_model_entry(
"meta/llama-3.2-11b-vision-instruct",
CoreModelId.llama3_2_11b_vision_instruct.value,
),
build_hf_repo_model_entry(
"meta/llama-3.2-90b-vision-instruct",
CoreModelId.llama3_2_90b_vision_instruct.value,
),
build_hf_repo_model_entry(
"meta/llama-3.3-70b-instruct",
CoreModelId.llama3_3_70b_instruct.value,
),
ProviderModelEntry(
provider_model_id="nvidia/vila",
model_type=ModelType.llm,
),
# NeMo Retriever Text Embedding models -
#
# https://docs.nvidia.com/nim/nemo-retriever/text-embedding/latest/support-matrix.html
#
# +-----------------------------------+--------+-----------+-----------+------------+
# | Model ID | Max | Publisher | Embedding | Dynamic |
# | | Tokens | | Dimension | Embeddings |
# +-----------------------------------+--------+-----------+-----------+------------+
# | nvidia/llama-3.2-nv-embedqa-1b-v2 | 8192 | NVIDIA | 2048 | Yes |
# | nvidia/nv-embedqa-e5-v5 | 512 | NVIDIA | 1024 | No |
# | nvidia/nv-embedqa-mistral-7b-v2 | 512 | NVIDIA | 4096 | No |
# | snowflake/arctic-embed-l | 512 | Snowflake | 1024 | No |
# +-----------------------------------+--------+-----------+-----------+------------+
ProviderModelEntry(
provider_model_id="nvidia/llama-3.2-nv-embedqa-1b-v2",
model_type=ModelType.embedding,
metadata={
"embedding_dimension": 2048,
"context_length": 8192,
},
),
ProviderModelEntry(
provider_model_id="nvidia/nv-embedqa-e5-v5",
model_type=ModelType.embedding,
metadata={
"embedding_dimension": 1024,
"context_length": 512,
},
),
ProviderModelEntry(
provider_model_id="nvidia/nv-embedqa-mistral-7b-v2",
model_type=ModelType.embedding,
metadata={
"embedding_dimension": 4096,
"context_length": 512,
},
),
ProviderModelEntry(
provider_model_id="snowflake/arctic-embed-l",
model_type=ModelType.embedding,
metadata={
"embedding_dimension": 1024,
"context_length": 512,
},
),
# TODO(mf): how do we handle Nemotron models?
# "Llama3.1-Nemotron-51B-Instruct" -> "meta/llama-3.1-nemotron-51b-instruct",
] + SAFETY_MODELS_ENTRIES