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# custom text generation llm classes
import warnings
import logging
import os
import openai
# supress warnings
warnings.filterwarnings("ignore")
class llm_boiler:
def __init__(self, model_id, openai_key):
self.model_id = model_id
self.openai_key = openai_key
for f_idx, run_function in enumerate(MODEL_FUNCTIONS):
if run_function.__name__.lower() in self.model_id:
print(
f"Load function recognized for {self.model_id}: {LOAD_MODEL_FUNCTIONS[f_idx].__name__}"
)
self.load_fn = LOAD_MODEL_FUNCTIONS[f_idx]
for run_function in MODEL_FUNCTIONS:
if run_function.__name__.lower() in self.model_id:
print(
f"Run function recognized for {self.model_id}: {run_function.__name__.lower()}"
)
self.run_fn = run_function
self.model = self.load_fn(self.model_id, self.openai_key)
self.name = self.run_fn.__name__.lower()
def run(
self,
prompt,
temperature,
):
return self.run_fn(
model=self.model,
prompt=prompt,
temperature=temperature,
)
LOAD_MODEL_FUNCTIONS = []
MODEL_FUNCTIONS = []
# gpt models
def gpt_loader(model_id: str, openai_key: str):
# Load your API key from an environment variable or secret management service
openai.api_key = openai_key # os.getenv("OPENAI_API_KEY")
logging.warning(f"model id: {model_id}")
return model_id
LOAD_MODEL_FUNCTIONS.append(gpt_loader)
def gpt(
model: str,
prompt: str,
temperature: int,
) -> str:
"""
Initialize the pipeline
Uses Hugging Face GenerationConfig defaults
https://huggingface.co./docs/transformers/v4.29.1/en/main_classes/text_generation#transformers.GenerationConfig
Args:
model (str): openai model key
tokenizer (str): openai model key
prompt (str): Prompt for text generation
max_new_tokens (int, optional): Max new tokens after the prompt to generate. Defaults to 128.
temperature (float, optional): The value used to modulate the next token probabilities.
Defaults to 1.0
"""
conversation = prompt.split("<|im_start|>")
messages = []
for turn in conversation:
first_word = turn.split("\n")[0]
if first_word == "system":
messages.append(
{
"role": "system",
"content": turn.replace("system\n", "").replace("<|im_end|>\n", ""),
}
)
elif first_word == "user":
messages.append(
{
"role": "user",
"content": turn.replace("user\n", "").replace("<|im_end|>\n", ""),
}
)
elif first_word == "assistant":
messages.append(
{
"role": "assistant",
"content": turn.replace("assistant\n", "").replace(
"<|im_end|>\n", ""
),
}
)
logging.warning(f"Input to openai api call: {messages}")
chat_completion = openai.ChatCompletion.create(
model=model,
messages=messages,
temperature=temperature,
stream=True,
)
return chat_completion
MODEL_FUNCTIONS.append(gpt)
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