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:recycle: [Refactor] Move STOP_SEQUENCES_MAP and TOKEN_LIMIT_MAP to constants
Browse files- constants/models.py +19 -0
- networks/message_streamer.py +10 -22
constants/models.py
CHANGED
@@ -6,3 +6,22 @@ MODEL_MAP = {
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"gemma-7b": "google/gemma-7b-it",
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"default": "mistralai/Mixtral-8x7B-Instruct-v0.1",
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}
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"gemma-7b": "google/gemma-7b-it",
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"default": "mistralai/Mixtral-8x7B-Instruct-v0.1",
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}
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+
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+
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STOP_SEQUENCES_MAP = {
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"mixtral-8x7b": "</s>",
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"nous-mixtral-8x7b": "<|im_end|>",
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"mistral-7b": "</s>",
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"openchat-3.5": "<|end_of_turn|>",
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"gemma-7b": "<eos>",
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}
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TOKEN_LIMIT_MAP = {
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"mixtral-8x7b": 32768,
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"nous-mixtral-8x7b": 32768,
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"mistral-7b": 32768,
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"openchat-3.5": 8192,
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"gemma-7b": 8192,
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}
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TOKEN_RESERVED = 20
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networks/message_streamer.py
CHANGED
@@ -5,28 +5,18 @@ import requests
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from tiktoken import get_encoding as tiktoken_get_encoding
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from transformers import AutoTokenizer
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from messagers.message_outputer import OpenaiStreamOutputer
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from constants.models import MODEL_MAP
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from utils.logger import logger
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from utils.enver import enver
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class MessageStreamer:
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-
STOP_SEQUENCES_MAP = {
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-
"mixtral-8x7b": "</s>",
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"mistral-7b": "</s>",
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"nous-mixtral-8x7b": "<|im_end|>",
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"openchat-3.5": "<|end_of_turn|>",
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"gemma-7b": "<eos>",
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}
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TOKEN_LIMIT_MAP = {
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"mixtral-8x7b": 32768,
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"mistral-7b": 32768,
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"nous-mixtral-8x7b": 32768,
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"openchat-3.5": 8192,
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"gemma-7b": 8192,
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}
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TOKEN_RESERVED = 20
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def __init__(self, model: str):
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if model in MODEL_MAP.keys():
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@@ -92,9 +82,7 @@ class MessageStreamer:
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top_p = min(top_p, 0.99)
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token_limit = int(
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-
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- self.TOKEN_RESERVED
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- self.count_tokens(prompt)
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)
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if token_limit <= 0:
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raise ValueError("Prompt exceeded token limit!")
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@@ -125,8 +113,8 @@ class MessageStreamer:
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"stream": True,
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}
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if self.model in
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self.stop_sequences =
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# self.request_body["parameters"]["stop_sequences"] = [
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# self.STOP_SEQUENCES[self.model]
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# ]
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@@ -176,7 +164,7 @@ class MessageStreamer:
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logger.back(content, end="")
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final_content += content
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if self.model in
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final_content = final_content.replace(self.stop_sequences, "")
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final_content = final_content.strip()
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from tiktoken import get_encoding as tiktoken_get_encoding
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from transformers import AutoTokenizer
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from constants.models import (
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MODEL_MAP,
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STOP_SEQUENCES_MAP,
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TOKEN_LIMIT_MAP,
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TOKEN_RESERVED,
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)
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from messagers.message_outputer import OpenaiStreamOutputer
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from utils.logger import logger
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from utils.enver import enver
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class MessageStreamer:
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def __init__(self, model: str):
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if model in MODEL_MAP.keys():
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top_p = min(top_p, 0.99)
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token_limit = int(
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TOKEN_LIMIT_MAP[self.model] - TOKEN_RESERVED - self.count_tokens(prompt)
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)
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if token_limit <= 0:
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raise ValueError("Prompt exceeded token limit!")
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"stream": True,
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}
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if self.model in STOP_SEQUENCES_MAP.keys():
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self.stop_sequences = STOP_SEQUENCES_MAP[self.model]
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# self.request_body["parameters"]["stop_sequences"] = [
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# self.STOP_SEQUENCES[self.model]
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# ]
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logger.back(content, end="")
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final_content += content
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if self.model in STOP_SEQUENCES_MAP.keys():
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final_content = final_content.replace(self.stop_sequences, "")
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final_content = final_content.strip()
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