phoenix-oss/llama-stack
1
0
Fork 0
forked from phoenix-oss/llama-stack-mirror
Code Pull requests Releases Packages 2 Activity Actions

chore: remove llama_models.llama3.api imports from providers (#1107)

Browse source 
There should be a choke-point for llama3.api imports -- this is the
prompt adapter. Creating a ChatFormat() object on demand is inexpensive.
The underlying Tokenizer is a singleton anyway.
This commit is contained in:
Ashwin Bharambe 2025-02-19 19:01:29 -08:00 • committed by GitHub
parent e9b8259cf9
commit cdcbeb005b
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
13 changed files with 77 additions and 113 deletions
Download patch file Download diff file Expand all files Collapse all files

14
llama_stack/providers/remote/inference/ollama/ollama.py
View file

@ -8,8 +8,6 @@ import logging
from typing import AsyncGenerator, List, Optional, Union
import httpx
from llama_models.llama3.api.chat_format import ChatFormat
from llama_models.llama3.api.tokenizer import Tokenizer
from ollama import AsyncClient
from llama_stack.apis.common.content_types import (
@ -138,7 +136,6 @@ class OllamaInferenceAdapter(Inference, ModelsProtocolPrivate):
def __init__(self, url: str) -> None:
self.register_helper = ModelRegistryHelper(model_aliases)
self.url = url
self.formatter = ChatFormat(Tokenizer.get_instance())
@property
def client(self) -> AsyncClient:
@ -197,7 +194,7 @@ class OllamaInferenceAdapter(Inference, ModelsProtocolPrivate):
)
stream = _generate_and_convert_to_openai_compat()
async for chunk in process_completion_stream_response(stream, self.formatter):
async for chunk in process_completion_stream_response(stream):
yield chunk
async def _nonstream_completion(self, request: CompletionRequest) -> AsyncGenerator:
@ -212,7 +209,7 @@ class OllamaInferenceAdapter(Inference, ModelsProtocolPrivate):
choices=[choice],
)
return process_completion_response(response, self.formatter)
return process_completion_response(response)
async def chat_completion(
self,
@ -262,11 +259,10 @@ class OllamaInferenceAdapter(Inference, ModelsProtocolPrivate):
input_dict["prompt"] = await chat_completion_request_to_prompt(
request,
self.register_helper.get_llama_model(request.model),
self.formatter,
)
else:
assert not media_present, "Ollama does not support media for Completion requests"
input_dict["prompt"] = await completion_request_to_prompt(request, self.formatter)
input_dict["prompt"] = await completion_request_to_prompt(request)
input_dict["raw"] = True
if fmt := request.response_format:
@ -304,7 +300,7 @@ class OllamaInferenceAdapter(Inference, ModelsProtocolPrivate):
response = OpenAICompatCompletionResponse(
choices=[choice],
)
return process_chat_completion_response(response, self.formatter, request)
return process_chat_completion_response(response, request)
async def _stream_chat_completion(self, request: ChatCompletionRequest) -> AsyncGenerator:
params = await self._get_params(request)
@ -330,7 +326,7 @@ class OllamaInferenceAdapter(Inference, ModelsProtocolPrivate):
)
stream = _generate_and_convert_to_openai_compat()
async for chunk in process_chat_completion_stream_response(stream, self.formatter, request):
async for chunk in process_chat_completion_stream_response(stream, request):
yield chunk
async def embeddings(