forked from phoenix-oss/llama-stack-mirror
530d4bdfe1
# What does this PR do? Move around bits. This makes the copies from llama-models _much_ easier to maintain and ensures we don't entangle meta-reference specific tidbits into llama-models code even by accident. Also, kills the meta-reference-quantized-gpu distro and rolls quantization deps into meta-reference-gpu. ## Test Plan ``` LLAMA_MODELS_DEBUG=1 \ with-proxy llama stack run meta-reference-gpu \ --env INFERENCE_MODEL=meta-llama/Llama-4-Scout-17B-16E-Instruct \ --env INFERENCE_CHECKPOINT_DIR=<DIR> \ --env MODEL_PARALLEL_SIZE=4 \ --env QUANTIZATION_TYPE=fp8_mixed ``` Start a server with and without quantization. Point integration tests to it using: ``` pytest -s -v tests/integration/inference/test_text_inference.py \ --stack-config http://localhost:8321 --text-model meta-llama/Llama-4-Scout-17B-16E-Instruct ```
98 lines
3.5 KiB
Python
98 lines
3.5 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from llama_stack.apis.models import ModelType
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from llama_stack.models.llama.sku_types import CoreModelId
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from llama_stack.providers.utils.inference.model_registry import (
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ProviderModelEntry,
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build_hf_repo_model_entry,
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)
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MODEL_ENTRIES = [
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build_hf_repo_model_entry(
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"meta/llama3-8b-instruct",
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CoreModelId.llama3_8b_instruct.value,
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),
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build_hf_repo_model_entry(
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"meta/llama3-70b-instruct",
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CoreModelId.llama3_70b_instruct.value,
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),
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build_hf_repo_model_entry(
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"meta/llama-3.1-8b-instruct",
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CoreModelId.llama3_1_8b_instruct.value,
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),
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build_hf_repo_model_entry(
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"meta/llama-3.1-70b-instruct",
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CoreModelId.llama3_1_70b_instruct.value,
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),
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build_hf_repo_model_entry(
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"meta/llama-3.1-405b-instruct",
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CoreModelId.llama3_1_405b_instruct.value,
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),
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build_hf_repo_model_entry(
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"meta/llama-3.2-1b-instruct",
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CoreModelId.llama3_2_1b_instruct.value,
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),
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build_hf_repo_model_entry(
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"meta/llama-3.2-3b-instruct",
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CoreModelId.llama3_2_3b_instruct.value,
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),
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build_hf_repo_model_entry(
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"meta/llama-3.2-11b-vision-instruct",
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CoreModelId.llama3_2_11b_vision_instruct.value,
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),
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build_hf_repo_model_entry(
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"meta/llama-3.2-90b-vision-instruct",
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CoreModelId.llama3_2_90b_vision_instruct.value,
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),
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# NeMo Retriever Text Embedding models -
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#
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# https://docs.nvidia.com/nim/nemo-retriever/text-embedding/latest/support-matrix.html
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#
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# +-----------------------------------+--------+-----------+-----------+------------+
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# | Model ID | Max | Publisher | Embedding | Dynamic |
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# | | Tokens | | Dimension | Embeddings |
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# +-----------------------------------+--------+-----------+-----------+------------+
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# | nvidia/llama-3.2-nv-embedqa-1b-v2 | 8192 | NVIDIA | 2048 | Yes |
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# | nvidia/nv-embedqa-e5-v5 | 512 | NVIDIA | 1024 | No |
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# | nvidia/nv-embedqa-mistral-7b-v2 | 512 | NVIDIA | 4096 | No |
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# | snowflake/arctic-embed-l | 512 | Snowflake | 1024 | No |
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# +-----------------------------------+--------+-----------+-----------+------------+
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ProviderModelEntry(
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provider_model_id="nvidia/llama-3.2-nv-embedqa-1b-v2",
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model_type=ModelType.embedding,
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metadata={
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"embedding_dimension": 2048,
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"context_length": 8192,
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},
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),
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ProviderModelEntry(
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provider_model_id="nvidia/nv-embedqa-e5-v5",
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model_type=ModelType.embedding,
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metadata={
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"embedding_dimension": 1024,
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"context_length": 512,
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},
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),
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ProviderModelEntry(
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provider_model_id="nvidia/nv-embedqa-mistral-7b-v2",
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model_type=ModelType.embedding,
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metadata={
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"embedding_dimension": 4096,
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"context_length": 512,
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},
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),
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ProviderModelEntry(
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provider_model_id="snowflake/arctic-embed-l",
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model_type=ModelType.embedding,
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metadata={
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"embedding_dimension": 1024,
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"context_length": 512,
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},
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),
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# TODO(mf): how do we handle Nemotron models?
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# "Llama3.1-Nemotron-51B-Instruct" -> "meta/llama-3.1-nemotron-51b-instruct",
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]
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