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Merge branch 'main' into TamiTakamiya/tool-param-definition-update

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Kai Wu 2025-09-16 15:04:32 -07:00 • committed by GitHub
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24 changed files with 9251 additions and 202 deletions
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120
llama_stack/providers/remote/inference/together/models.py
View file

@ -4,7 +4,6 @@
# 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,
@ -21,57 +20,84 @@ SAFETY_MODELS_ENTRIES = [
CoreModelId.llama_guard_3_11b_vision.value,
),
]
MODEL_ENTRIES = [
build_hf_repo_model_entry(
"meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo",
CoreModelId.llama3_1_8b_instruct.value,
),
build_hf_repo_model_entry(
"meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo",
CoreModelId.llama3_1_70b_instruct.value,
),
build_hf_repo_model_entry(
"meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo",
CoreModelId.llama3_1_405b_instruct.value,
),
build_hf_repo_model_entry(
"meta-llama/Llama-3.2-3B-Instruct-Turbo",
CoreModelId.llama3_2_3b_instruct.value,
),
build_hf_repo_model_entry(
"meta-llama/Llama-3.2-11B-Vision-Instruct-Turbo",
CoreModelId.llama3_2_11b_vision_instruct.value,
),
build_hf_repo_model_entry(
"meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo",
CoreModelId.llama3_2_90b_vision_instruct.value,
),
build_hf_repo_model_entry(
"meta-llama/Llama-3.3-70B-Instruct-Turbo",
CoreModelId.llama3_3_70b_instruct.value,
),
ProviderModelEntry(
provider_model_id="togethercomputer/m2-bert-80M-8k-retrieval",
model_type=ModelType.embedding,
metadata={
"embedding_dimension": 768,
"context_length": 8192,
},
),
ProviderModelEntry(
# source: https://docs.together.ai/docs/serverless-models#embedding-models
EMBEDDING_MODEL_ENTRIES = {
"togethercomputer/m2-bert-80M-32k-retrieval": ProviderModelEntry(
provider_model_id="togethercomputer/m2-bert-80M-32k-retrieval",
model_type=ModelType.embedding,
metadata={
"embedding_dimension": 768,
"context_length": 32768,
},
),
build_hf_repo_model_entry(
"meta-llama/Llama-4-Scout-17B-16E-Instruct",
CoreModelId.llama4_scout_17b_16e_instruct.value,
"BAAI/bge-large-en-v1.5": ProviderModelEntry(
provider_model_id="BAAI/bge-large-en-v1.5",
metadata={
"embedding_dimension": 1024,
"context_length": 512,
},
),
build_hf_repo_model_entry(
"meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
CoreModelId.llama4_maverick_17b_128e_instruct.value,
"BAAI/bge-base-en-v1.5": ProviderModelEntry(
provider_model_id="BAAI/bge-base-en-v1.5",
metadata={
"embedding_dimension": 768,
"context_length": 512,
},
),
] + SAFETY_MODELS_ENTRIES
"Alibaba-NLP/gte-modernbert-base": ProviderModelEntry(
provider_model_id="Alibaba-NLP/gte-modernbert-base",
metadata={
"embedding_dimension": 768,
"context_length": 8192,
},
),
"intfloat/multilingual-e5-large-instruct": ProviderModelEntry(
provider_model_id="intfloat/multilingual-e5-large-instruct",
metadata={
"embedding_dimension": 1024,
"context_length": 512,
},
),
}
MODEL_ENTRIES = (
[
build_hf_repo_model_entry(
"meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo",
CoreModelId.llama3_1_8b_instruct.value,
),
build_hf_repo_model_entry(
"meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo",
CoreModelId.llama3_1_70b_instruct.value,
),
build_hf_repo_model_entry(
"meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo",
CoreModelId.llama3_1_405b_instruct.value,
),
build_hf_repo_model_entry(
"meta-llama/Llama-3.2-3B-Instruct-Turbo",
CoreModelId.llama3_2_3b_instruct.value,
),
build_hf_repo_model_entry(
"meta-llama/Llama-3.2-11B-Vision-Instruct-Turbo",
CoreModelId.llama3_2_11b_vision_instruct.value,
),
build_hf_repo_model_entry(
"meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo",
CoreModelId.llama3_2_90b_vision_instruct.value,
),
build_hf_repo_model_entry(
"meta-llama/Llama-3.3-70B-Instruct-Turbo",
CoreModelId.llama3_3_70b_instruct.value,
),
build_hf_repo_model_entry(
"meta-llama/Llama-4-Scout-17B-16E-Instruct",
CoreModelId.llama4_scout_17b_16e_instruct.value,
),
build_hf_repo_model_entry(
"meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
CoreModelId.llama4_maverick_17b_128e_instruct.value,
),
]
+ SAFETY_MODELS_ENTRIES
+ list(EMBEDDING_MODEL_ENTRIES.values())
)

205
llama_stack/providers/remote/inference/together/together.py
View file

@ -4,11 +4,11 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from collections.abc import AsyncGenerator, AsyncIterator
from typing import Any
from collections.abc import AsyncGenerator
from openai import AsyncOpenAI
from openai import NOT_GIVEN, AsyncOpenAI
from together import AsyncTogether
from together.constants import BASE_URL
from llama_stack.apis.common.content_types import (
InterleavedContent,
@ -23,12 +23,7 @@ from llama_stack.apis.inference import (
Inference,
LogProbConfig,
Message,
OpenAIChatCompletion,
OpenAIChatCompletionChunk,
OpenAICompletion,
OpenAIEmbeddingsResponse,
OpenAIMessageParam,
OpenAIResponseFormatParam,
ResponseFormat,
ResponseFormatType,
SamplingParams,
@ -38,18 +33,20 @@ from llama_stack.apis.inference import (
ToolDefinition,
ToolPromptFormat,
)
from llama_stack.apis.inference.inference import OpenAIEmbeddingUsage
from llama_stack.apis.models import Model, ModelType
from llama_stack.core.request_headers import NeedsRequestProviderData
from llama_stack.log import get_logger
from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper
from llama_stack.providers.utils.inference.openai_compat import (
convert_message_to_openai_dict,
get_sampling_options,
prepare_openai_completion_params,
process_chat_completion_response,
process_chat_completion_stream_response,
process_completion_response,
process_completion_stream_response,
)
from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
from llama_stack.providers.utils.inference.prompt_adapter import (
chat_completion_request_to_prompt,
completion_request_to_prompt,
@ -59,15 +56,22 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
)
from .config import TogetherImplConfig
from .models import MODEL_ENTRIES
from .models import EMBEDDING_MODEL_ENTRIES, MODEL_ENTRIES
logger = get_logger(name=__name__, category="inference::together")
class TogetherInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProviderData):
class TogetherInferenceAdapter(OpenAIMixin, ModelRegistryHelper, Inference, NeedsRequestProviderData):
def __init__(self, config: TogetherImplConfig) -> None:
ModelRegistryHelper.__init__(self, MODEL_ENTRIES, config.allowed_models)
self.config = config
self._model_cache: dict[str, Model] = {}
def get_api_key(self):
return self.config.api_key.get_secret_value()
def get_base_url(self):
return BASE_URL
async def initialize(self) -> None:
pass
@ -255,6 +259,37 @@ class TogetherInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProvi
embeddings = [item.embedding for item in r.data]
return EmbeddingsResponse(embeddings=embeddings)
async def list_models(self) -> list[Model] | None:
self._model_cache = {}
# Together's /v1/models is not compatible with OpenAI's /v1/models. Together support ticket #13355 -> will not fix, use Together's own client
for m in await self._get_client().models.list():
if m.type == "embedding":
if m.id not in EMBEDDING_MODEL_ENTRIES:
logger.warning(f"Unknown embedding dimension for model {m.id}, skipping.")
continue
self._model_cache[m.id] = Model(
provider_id=self.__provider_id__,
provider_resource_id=EMBEDDING_MODEL_ENTRIES[m.id].provider_model_id,
identifier=m.id,
model_type=ModelType.embedding,
metadata=EMBEDDING_MODEL_ENTRIES[m.id].metadata,
)
else:
self._model_cache[m.id] = Model(
provider_id=self.__provider_id__,
provider_resource_id=m.id,
identifier=m.id,
model_type=ModelType.llm,
)
return self._model_cache.values()
async def should_refresh_models(self) -> bool:
return True
async def check_model_availability(self, model):
return model in self._model_cache
async def openai_embeddings(
self,
model: str,
@ -263,125 +298,39 @@ class TogetherInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProvi
dimensions: int | None = None,
user: str | None = None,
) -> OpenAIEmbeddingsResponse:
raise NotImplementedError()
"""
Together's OpenAI-compatible embeddings endpoint is not compatible with
the standard OpenAI embeddings endpoint.
async def openai_completion(
self,
model: str,
prompt: str | list[str] | list[int] | list[list[int]],
best_of: int | None = None,
echo: bool | None = None,
frequency_penalty: float | None = None,
logit_bias: dict[str, float] | None = None,
logprobs: bool | None = None,
max_tokens: int | None = None,
n: int | None = None,
presence_penalty: float | None = None,
seed: int | None = None,
stop: str | list[str] | None = None,
stream: bool | None = None,
stream_options: dict[str, Any] | None = None,
temperature: float | None = None,
top_p: float | None = None,
user: str | None = None,
guided_choice: list[str] | None = None,
prompt_logprobs: int | None = None,
suffix: str | None = None,
) -> OpenAICompletion:
model_obj = await self.model_store.get_model(model)
params = await prepare_openai_completion_params(
model=model_obj.provider_resource_id,
prompt=prompt,
best_of=best_of,
echo=echo,
frequency_penalty=frequency_penalty,
logit_bias=logit_bias,
logprobs=logprobs,
max_tokens=max_tokens,
n=n,
presence_penalty=presence_penalty,
seed=seed,
stop=stop,
stream=stream,
stream_options=stream_options,
temperature=temperature,
top_p=top_p,
user=user,
The endpoint -
- does not return usage information
- does not support user param, returns 400 Unrecognized request arguments supplied: user
- does not support dimensions param, returns 400 Unrecognized request arguments supplied: dimensions
- does not support encoding_format param, always returns floats, never base64
"""
# Together support ticket #13332 -> will not fix
if user is not None:
raise ValueError("Together's embeddings endpoint does not support user param.")
# Together support ticket #13333 -> escalated
if dimensions is not None:
raise ValueError("Together's embeddings endpoint does not support dimensions param.")
# Together support ticket #13331 -> will not fix, compute client side
if encoding_format not in (None, NOT_GIVEN, "float"):
raise ValueError("Together's embeddings endpoint only supports encoding_format='float'.")
response = await self.client.embeddings.create(
model=await self._get_provider_model_id(model),
input=input,
)
return await self._get_openai_client().completions.create(**params) # type: ignore
async def openai_chat_completion(
self,
model: str,
messages: list[OpenAIMessageParam],
frequency_penalty: float | None = None,
function_call: str | dict[str, Any] | None = None,
functions: list[dict[str, Any]] | None = None,
logit_bias: dict[str, float] | None = None,
logprobs: bool | None = None,
max_completion_tokens: int | None = None,
max_tokens: int | None = None,
n: int | None = None,
parallel_tool_calls: bool | None = None,
presence_penalty: float | None = None,
response_format: OpenAIResponseFormatParam | None = None,
seed: int | None = None,
stop: str | list[str] | None = None,
stream: bool | None = None,
stream_options: dict[str, Any] | None = None,
temperature: float | None = None,
tool_choice: str | dict[str, Any] | None = None,
tools: list[dict[str, Any]] | None = None,
top_logprobs: int | None = None,
top_p: float | None = None,
user: str | None = None,
) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]:
model_obj = await self.model_store.get_model(model)
params = await prepare_openai_completion_params(
model=model_obj.provider_resource_id,
messages=messages,
frequency_penalty=frequency_penalty,
function_call=function_call,
functions=functions,
logit_bias=logit_bias,
logprobs=logprobs,
max_completion_tokens=max_completion_tokens,
max_tokens=max_tokens,
n=n,
parallel_tool_calls=parallel_tool_calls,
presence_penalty=presence_penalty,
response_format=response_format,
seed=seed,
stop=stop,
stream=stream,
stream_options=stream_options,
temperature=temperature,
tool_choice=tool_choice,
tools=tools,
top_logprobs=top_logprobs,
top_p=top_p,
user=user,
)
if params.get("stream", False):
return self._stream_openai_chat_completion(params)
return await self._get_openai_client().chat.completions.create(**params) # type: ignore
response.model = model # return the user the same model id they provided, avoid exposing the provider model id
async def _stream_openai_chat_completion(self, params: dict) -> AsyncGenerator:
# together.ai sometimes adds usage data to the stream, even if include_usage is False
# This causes an unexpected final chunk with empty choices array to be sent
# to clients that may not handle it gracefully.
include_usage = False
if params.get("stream_options", None):
include_usage = params["stream_options"].get("include_usage", False)
stream = await self._get_openai_client().chat.completions.create(**params)
# Together support ticket #13330 -> escalated
# - togethercomputer/m2-bert-80M-32k-retrieval *does not* return usage information
if not hasattr(response, "usage") or response.usage is None:
logger.warning(
f"Together's embedding endpoint for {model} did not return usage information, substituting -1s."
)
response.usage = OpenAIEmbeddingUsage(prompt_tokens=-1, total_tokens=-1)
seen_finish_reason = False
async for chunk in stream:
# Final usage chunk with no choices that the user didn't request, so discard
if not include_usage and seen_finish_reason and len(chunk.choices) == 0:
break
yield chunk
for choice in chunk.choices:
if choice.finish_reason:
seen_finish_reason = True
break
return response

8
llama_stack/ui/package-lock.json generated
View file

@ -18,7 +18,7 @@
"class-variance-authority": "^0.7.1",
"clsx": "^2.1.1",
"framer-motion": "^12.23.12",
"llama-stack-client": "^0.2.21",
"llama-stack-client": "^0.2.22",
"lucide-react": "^0.542.0",
"next": "15.5.3",
"next-auth": "^4.24.11",
@ -10314,9 +10314,9 @@
"license": "MIT"
},
"node_modules/llama-stack-client": {
"version": "0.2.21",
"resolved": "https://registry.npmjs.org/llama-stack-client/-/llama-stack-client-0.2.21.tgz",
"integrity": "sha512-rjU2Vx5xStxDYavU8K1An/SYXiQQjroLcK98B+p0Paz/a7OgRao2S0YwvThJjPUyChY4fO03UIXP9LpmHqlXWQ==",
"version": "0.2.22",
"resolved": "https://registry.npmjs.org/llama-stack-client/-/llama-stack-client-0.2.22.tgz",
"integrity": "sha512-7aW3UQj5MwjV73Brd+yQ1e4W1W33nhozyeHM5tzOgbsVZ88tL78JNiNvyFqDR5w6V9XO4/uSGGiQVG6v83yR4w==",
"license": "MIT",
"dependencies": {
"@types/node": "^18.11.18",

2
llama_stack/ui/package.json
View file

@ -23,7 +23,7 @@
"class-variance-authority": "^0.7.1",
"clsx": "^2.1.1",
"framer-motion": "^12.23.12",
"llama-stack-client": "^0.2.21",
"llama-stack-client": "^0.2.22",
"lucide-react": "^0.542.0",
"next": "15.5.3",
"next-auth": "^4.24.11",