mirror of
https://github.com/meta-llama/llama-stack.git
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f754e1b65b
vllm - - requires max_tokens be set, use config value - set tool_choice to none if no tools provided
86 lines
2.7 KiB
Python
86 lines
2.7 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 urllib.parse import urljoin
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from cerebras.cloud.sdk import AsyncCerebras
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from llama_stack.apis.inference import (
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ChatCompletionRequest,
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CompletionRequest,
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Inference,
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OpenAIEmbeddingsResponse,
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TopKSamplingStrategy,
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)
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from llama_stack.providers.utils.inference.model_registry import (
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ModelRegistryHelper,
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)
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from llama_stack.providers.utils.inference.openai_compat import (
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get_sampling_options,
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)
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from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
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from llama_stack.providers.utils.inference.prompt_adapter import (
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chat_completion_request_to_prompt,
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completion_request_to_prompt,
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)
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from .config import CerebrasImplConfig
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class CerebrasInferenceAdapter(
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OpenAIMixin,
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ModelRegistryHelper,
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Inference,
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):
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def __init__(self, config: CerebrasImplConfig) -> None:
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self.config = config
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# TODO: make this use provider data, etc. like other providers
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self._cerebras_client = AsyncCerebras(
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base_url=self.config.base_url,
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api_key=self.config.api_key.get_secret_value(),
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)
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def get_api_key(self) -> str:
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return self.config.api_key.get_secret_value()
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def get_base_url(self) -> str:
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return urljoin(self.config.base_url, "v1")
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async def initialize(self) -> None:
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return
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async def shutdown(self) -> None:
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pass
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async def _get_params(self, request: ChatCompletionRequest | CompletionRequest) -> dict:
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if request.sampling_params and isinstance(request.sampling_params.strategy, TopKSamplingStrategy):
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raise ValueError("`top_k` not supported by Cerebras")
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prompt = ""
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if isinstance(request, ChatCompletionRequest):
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prompt = await chat_completion_request_to_prompt(request, self.get_llama_model(request.model))
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elif isinstance(request, CompletionRequest):
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prompt = await completion_request_to_prompt(request)
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else:
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raise ValueError(f"Unknown request type {type(request)}")
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return {
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"model": request.model,
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"prompt": prompt,
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"stream": request.stream,
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**get_sampling_options(request.sampling_params),
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}
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async def openai_embeddings(
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self,
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model: str,
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input: str | list[str],
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encoding_format: str | None = "float",
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dimensions: int | None = None,
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user: str | None = None,
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) -> OpenAIEmbeddingsResponse:
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raise NotImplementedError()
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