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Merge pull request #39 from wacunn:updates
PiperOrigin-RevId: 621196556 Change-Id: I24679b6abc732686a893b63592cdd37ed5ba9f94
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# Copyright 2024 DeepMind Technologies Limited. | ||
# | ||
# Licensed under the Apache License, Version 2.0 (the "License"); | ||
# you may not use this file except in compliance with the License. | ||
# You may obtain a copy of the License at | ||
# | ||
# https://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, software | ||
# distributed under the License is distributed on an "AS IS" BASIS, | ||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
# See the License for the specific language governing permissions and | ||
# limitations under the License. | ||
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"""Ollama Language Model.""" | ||
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from collections.abc import Collection, Sequence | ||
import re | ||
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from concordia.language_model import language_model | ||
from concordia.utils import measurements as measurements_lib | ||
from langchain import llms | ||
from typing_extensions import override | ||
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def _extract_choices(text): | ||
match = re.search(r"\(?(\w)\)", text) | ||
if match: | ||
return match.group(1) | ||
return None | ||
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class OllamaLanguageModel(language_model.LanguageModel): | ||
"""Language Model that uses Ollama LLM models.""" | ||
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def __init__( | ||
self, | ||
model_name: str, | ||
*, | ||
system_message: str = "", | ||
measurements: measurements_lib.Measurements | None = None, | ||
channel: str = language_model.DEFAULT_STATS_CHANNEL, | ||
) -> None: | ||
"""Initializes the instance. | ||
Args: | ||
model_name: The language model to use. For more details, see | ||
https://github.com/ollama/ollama. | ||
system_message: System message to prefix to requests when prompting the | ||
model. | ||
measurements: The measurements object to log usage statistics to. | ||
channel: The channel to write the statistics to. | ||
""" | ||
self._model_name = model_name | ||
self._system_message = system_message | ||
self._measurements = measurements | ||
self._channel = channel | ||
self._client = llms.Ollama(model=model_name) | ||
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@override | ||
def sample_text( | ||
self, | ||
prompt: str, | ||
*, | ||
max_tokens: int = language_model.DEFAULT_MAX_TOKENS, | ||
max_characters: int = language_model.DEFAULT_MAX_CHARACTERS, | ||
terminators: Collection[str] = language_model.DEFAULT_TERMINATORS, | ||
temperature: float = language_model.DEFAULT_TEMPERATURE, | ||
timeout: float = language_model.DEFAULT_TIMEOUT_SECONDS, | ||
seed: int | None = None, | ||
) -> str: | ||
prompt_with_system_message = f"{self._system_message}\n\n{prompt}" | ||
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response = self._client( | ||
prompt_with_system_message, | ||
stop=terminators, | ||
temperature=temperature, | ||
) | ||
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if self._measurements is not None: | ||
self._measurements.publish_datum( | ||
self._channel, {"raw_text_length": len(response)} | ||
) | ||
return response | ||
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@override | ||
def sample_choice( | ||
self, | ||
prompt: str, | ||
responses: Sequence[str], | ||
*, | ||
seed: int | None = None, | ||
) -> tuple[int, str, dict[str, float]]: | ||
max_characters = len(max(responses, key=len)) | ||
prompt_with_system_message = f"{self._system_message}\n\n{prompt}" | ||
sample = self.sample_text( | ||
prompt_with_system_message, | ||
max_characters=max_characters, | ||
temperature=0.0, | ||
seed=seed, | ||
) | ||
answer = _extract_choices(sample) | ||
try: | ||
idx = responses.index(answer) | ||
except ValueError: | ||
raise language_model.InvalidResponseError( | ||
f"Invalid response: {answer}. " | ||
f"LLM Input: {prompt}\nLLM Output: {sample}" | ||
) from None | ||
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if self._measurements is not None: | ||
self._measurements.publish_datum(self._channel, {"choices_calls": 1}) | ||
debug = {} | ||
return idx, responses[idx], debug |
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