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Add quantize_qlora support for falcon

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zhanghy-sketchzh 2023-06-08 13:37:48 +08:00
parent b357fd9d0c
commit bb9081e00f
3 changed files with 18 additions and 15 deletions
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4
.env.template
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@ -21,7 +21,7 @@ LLM_MODEL=vicuna-13b
MODEL_SERVER=http://127.0.0.1:8000
LIMIT_MODEL_CONCURRENCY=5
MAX_POSITION_EMBEDDINGS=4096
QUANTIZE_QLORA=True
## SMART_LLM_MODEL - Smart language model (Default: vicuna-13b)
## FAST_LLM_MODEL - Fast language model (Default: chatglm-6b)
# SMART_LLM_MODEL=vicuna-13b
@ -112,4 +112,4 @@ PROXY_SERVER_URL=http://127.0.0.1:3000/proxy_address
#*******************************************************************#
# ** SUMMARY_CONFIG
#*******************************************************************#
SUMMARY_CONFIG=FAST
SUMMARY_CONFIG=FAST

2
pilot/configs/model_config.py
View File

@ -42,7 +42,7 @@ LLM_MODEL_CONFIG = {
# Load model config
ISLOAD_8BIT = True
ISDEBUG = False
QLORA = os.getenv("QUANTIZE_QLORA") == "True"
VECTOR_SEARCH_TOP_K = 10
VS_ROOT_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "vs_store")

27
pilot/model/adapter.py
View File

@ -7,6 +7,7 @@ from functools import cache
from transformers import AutoModel, AutoModelForCausalLM, AutoTokenizer, LlamaTokenizer, BitsAndBytesConfig
from pilot.configs.model_config import DEVICE
bnb_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype="bfloat16", bnb_4bit_use_double_quant=False)
class BaseLLMAdaper:
"""The Base class for multi model, in our project.
@ -105,19 +106,21 @@ class FalconAdapater(BaseLLMAdaper):
def loader(self, model_path: str, from_pretrained_kwagrs: dict):
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype="bfloat16",
bnb_4bit_use_double_quant=False,
if QLORA == True:
model = AutoModelForCausalLM.from_pretrained(
model_path,
load_in_4bit=True, #quantize
quantization_config=bnb_config,
device_map={"": 0},
trust_remote_code=True,
**from_pretrained_kwagrs
)
model = AutoModelForCausalLM.from_pretrained(
model_path,
#load_in_4bit=True, #quantize
quantization_config=bnb_config,
device_map={"": 0},
trust_remote_code=True,
**from_pretrained_kwagrs
else:
model = AutoModelForCausalLM.from_pretrained(
model_path,
trust_remote_code=True,
device_map={"": 0},
**from_pretrained_kwagrs
)
return model, tokenizer