selfhostyourtech/reflector
2
0
Fork 0
mirror of https://github.com/Monadical-SAS/reflector.git synced 2025-12-20 20:29:06 +00:00
Code Issues Packages Projects Releases Wiki Activity

Delete server/reflector/llm/llm_params_cod.py

Browse Source
This commit is contained in:
projects-g
2023-09-25 09:46:00 +05:30
committed by GitHub
parent 117acfacae
commit 3a374ea593
1 changed files with 0 additions and 150 deletions
Download Patch File Download Diff File Expand all files Collapse all files

150
server/reflector/llm/llm_params_cod.py
View File

@@ -1,150 +0,0 @@
from typing import Optional, TypeVar
from pydantic import BaseModel
from transformers import GenerationConfig
class TaskParams(BaseModel, arbitrary_types_allowed=True):
instruct: str
gen_cfg: Optional[GenerationConfig] = None
gen_schema: Optional[dict] = None
T = TypeVar("T", bound="LLMTaskParams")
class LLMTaskParams:
_registry = {}
@classmethod
def register(cls, task, klass) -> None:
cls._registry[task] = klass
@classmethod
def get_instance(cls, task: str) -> T:
return cls._registry[task]()
@property
def task_params(self) -> TaskParams | None:
"""
Fetch the task related parameters
"""
return self._get_task_params()
def _get_task_params(self) -> None:
pass
class FinalLongSummaryParams(LLMTaskParams):
def __init__(self, **kwargs):
super().__init__(**kwargs)
self._gen_cfg = GenerationConfig(
max_new_tokens=800, num_beams=3, do_sample=True, temperature=0.3
)
self._instruct = """
Take the key ideas and takeaways from the text and create a short
summary. Be sure to keep the length of the response to a minimum.
Do not include trivial information in the summary.
"""
self._schema = {
"type": "object",
"properties": {"long_summary": {"type": "string"}},
}
self._task_params = TaskParams(
instruct=self._instruct, gen_schema=self._schema, gen_cfg=self._gen_cfg
)
def _get_task_params(self) -> TaskParams:
"""gen_schema
Return the parameters associated with a specific LLM task
"""
return self._task_params
class FinalShortSummaryParams(LLMTaskParams):
def __init__(self, **kwargs):
super().__init__(**kwargs)
self._gen_cfg = GenerationConfig(
max_new_tokens=1300, num_beams=3, do_sample=True, temperature=0.3
)
self._instruct = """
Take the key ideas and takeaways from the text and create a short
summary. Be sure to keep the length of the response to a minimum.
Do not include trivial information in the summary.
"""
self._schema = {
"type": "object",
"properties": {"short_summary": {"type": "string"}},
}
self._task_params = TaskParams(
instruct=self._instruct, gen_schema=self._schema, gen_cfg=self._gen_cfg
)
def _get_task_params(self) -> TaskParams:
"""
Return the parameters associated with a specific LLM task
"""
return self._task_params
class FinalTitleParams(LLMTaskParams):
def __init__(self, **kwargs):
super().__init__(**kwargs)
self._gen_cfg = GenerationConfig(
max_new_tokens=200, num_beams=5, do_sample=True, temperature=0.5
)
self._instruct = """
Combine the following individual titles into one single short title that
condenses the essence of all titles.
"""
self._schema = {
"type": "object",
"properties": {"title": {"type": "string"}},
}
self._task_params = TaskParams(
instruct=self._instruct, gen_schema=self._schema, gen_cfg=self._gen_cfg
)
def _get_task_params(self) -> TaskParams:
"""
Return the parameters associated with a specific LLM task
"""
return self._task_params
class TopicParams(LLMTaskParams):
def __init__(self, **kwargs):
super().__init__(**kwargs)
self._gen_cfg = GenerationConfig(
max_new_tokens=550, num_beams=6, do_sample=True, temperature=0.9
)
self._instruct = """
Create a JSON object as response.The JSON object must have 2 fields:
i) title and ii) summary.
For the title field, generate a very detailed and self-explanatory
title for the given text. Let the title be as descriptive as possible.
For the summary field, summarize the given text in a maximum of
three sentences.
"""
self._schema = {
"type": "object",
"properties": {
"title": {"type": "string"},
"summary": {"type": "string"},
},
}
self._task_params = TaskParams(
instruct=self._instruct, gen_schema=self._schema, gen_cfg=self._gen_cfg
)
def _get_task_params(self) -> TaskParams:
"""
Return the parameters associated with a specific LLM task
"""
return self._task_params
LLMTaskParams.register("topic", TopicParams)
LLMTaskParams.register("final_title", FinalTitleParams)
LLMTaskParams.register("final_short_summary", FinalShortSummaryParams)
LLMTaskParams.register("final_long_summary", FinalLongSummaryParams)