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from dotenv import load_dotenv
import logging
import torch
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
BitsAndBytesConfig,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from langchain_huggingface import HuggingFacePipeline
from langchain.globals import set_debug
from langchain.globals import set_verbose
from config import HF_MODEL_ID
from config import LLM_VERBOSE
set_verbose(LLM_VERBOSE)
set_debug(LLM_VERBOSE)
logger = logging.getLogger(__name__)
load_dotenv()
cuda_check = torch.cuda.is_available()
logger.info(f"torch.cuda.is_available : {cuda_check}")
print(f"> torch.cuda.is_available : {cuda_check}")
# Load Llama3 model and tokenizer
model_id = HF_MODEL_ID
tokenizer = AutoTokenizer.from_pretrained(model_id)
# BitsAndBytesConfig int-4 config
# device_map = {"": 0}
device_map = "auto"
compute_dtype = getattr(torch, "float16")
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=False,
bnb_4bit_quant_type="nf4",
# bnb_4bit_compute_dtype=torch.bfloat16
bnb_4bit_compute_dtype=compute_dtype,
# bnb_4bit_use_double_quant=False,
)
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map=device_map,
# attn_implementation="flash_attention_2",
quantization_config=bnb_config,
)
model.generation_config.pad_token_id = tokenizer.eos_token_id
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=50,
return_full_text=False,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
temperature=0.0001,
do_sample=True,
)
llm = HuggingFacePipeline(pipeline=pipe)
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