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 ```
116 lines
2.9 KiB
Python
116 lines
2.9 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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# 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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# top-level folder for each specific model found within the models/ directory at
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# the top-level of this source tree.
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from ..datatypes import BuiltinTool, StopReason, ToolCall
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from .prompt_templates import (
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BuiltinToolGenerator,
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JsonCustomToolGenerator,
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ToolResponseGenerator,
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)
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INSTRUCTION = "You are a helpful assistant."
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def system_message_builtin_tools_only():
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return {
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"builtin_tools": BuiltinToolGenerator().data_examples()[0],
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"custom_tools": [],
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"instruction": INSTRUCTION,
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}
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def system_message_builtin_code_only():
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return {
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"builtin_tools": BuiltinToolGenerator().data_examples()[1],
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"custom_tools": [],
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"instruction": "",
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}
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def system_message_custom_tools_only():
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return {
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"builtin_tools": [],
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"custom_tools": JsonCustomToolGenerator().data_examples()[0],
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"instruction": INSTRUCTION,
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}
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def system_message_builtin_and_custom_tools():
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return {
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"builtin_tools": BuiltinToolGenerator().data_examples()[0],
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"custom_tools": JsonCustomToolGenerator().data_examples()[0],
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"instruction": INSTRUCTION,
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}
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def system_default():
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return {
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"builtin_tools": [],
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"custom_tools": [],
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"instruction": INSTRUCTION,
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}
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def tool_success():
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return ToolResponseGenerator().data_examples()[0]
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def tool_failure():
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return ToolResponseGenerator().data_examples()[1]
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def assistant_builtin_tool_call():
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return {
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"content": "",
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"tool_call": ToolCall(
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call_id="uuid",
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tool_name=BuiltinTool.brave_search,
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arguments={
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"query": "Who won NBA in 2024?",
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},
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),
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"stop_reason": StopReason.end_of_message,
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}
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def assistant_custom_tool_call():
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return {
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"content": "",
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"tool_call": ToolCall(
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call_id="uuid",
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tool_name="trending_songs",
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arguments={"country": "US", "n": 10},
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),
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"stop_reason": StopReason.end_of_turn,
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}
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def assistant_default():
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return {
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"content": "Hi, I am a helpful assistant. What can I help you with today?",
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"tool_call": None,
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"stop_reason": StopReason.end_of_turn,
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}
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def user_default():
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return {"content": "Please tell me how to plan a trip to New York"}
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def user_images():
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return {"content": "<|image|><|image|>What do these images depict?"}
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def user_interleaved_images():
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return {"content": "<|image|>Describe the image in one sentence.<|image|>Write a haiku about these images"}
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