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from langchain_openai import ChatOpenAI | |
from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline | |
from transformers import AutoTokenizer, TextStreamer | |
from langchain_community.llms import LlamaCpp | |
import torch | |
import transformers | |
import os | |
from langchain.callbacks.manager import CallbackManager | |
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler | |
from modules.config.constants import LLAMA_PATH | |
class ChatModelLoader: | |
def __init__(self, config): | |
self.config = config | |
self.huggingface_token = os.getenv("HUGGINGFACEHUB_API_TOKEN") | |
def load_chat_model(self): | |
if self.config["llm_params"]["llm_loader"] in ["gpt-3.5-turbo-1106", "gpt-4"]: | |
llm = ChatOpenAI(model_name=self.config["llm_params"]["llm_loader"]) | |
elif self.config["llm_params"]["llm_loader"] == "local_llm": | |
n_batch = 512 # Should be between 1 and n_ctx, consider the amount of VRAM in your GPU. | |
llm = LlamaCpp( | |
model_path=LLAMA_PATH, | |
n_batch=n_batch, | |
n_ctx=2048, | |
f16_kv=True, | |
verbose=True, | |
n_threads=2, | |
temperature=self.config["llm_params"]["local_llm_params"][ | |
"temperature" | |
], | |
) | |
else: | |
raise ValueError( | |
f"Invalid LLM Loader: {self.config['llm_params']['llm_loader']}" | |
) | |
return llm | |