Mirrors/text-generation-webui
1
0
Fork 0
mirror of https://github.com/oobabooga/text-generation-webui.git synced 2024-11-25 17:29:22 +01:00
Code Issues Packages Projects Releases Wiki Activity

use new quant loader

Browse Source
This commit is contained in:
Ayanami Rei 2023-03-13 20:00:38 +03:00
parent 345b6dee8c
commit edbc61139f
1 changed files with 6 additions and 6 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

12
modules/models.py
View File

@ -1,6 +1,5 @@
import json
import os
import sys
import time
import zipfile
from pathlib import Path
@ -35,6 +34,7 @@ if shared.args.deepspeed:
ds_config = generate_ds_config(shared.args.bf16, 1 * world_size, shared.args.nvme_offload_dir)
dschf = HfDeepSpeedConfig(ds_config) # Keep this object alive for the Transformers integration
def load_model(model_name):
print(f"Loading {model_name}...")
t0 = time.time()
@ -42,7 +42,7 @@ def load_model(model_name):
shared.is_RWKV = model_name.lower().startswith('rwkv-')
# Default settings
if not any([shared.args.cpu, shared.args.load_in_8bit, shared.args.load_in_4bit, shared.args.gptq_bits > 0, shared.args.auto_devices, shared.args.disk, shared.args.gpu_memory is not None, shared.args.cpu_memory is not None, shared.args.deepspeed, shared.args.flexgen, shared.is_RWKV]):
if not any([shared.args.cpu, shared.args.load_in_8bit, shared.args.gptq_bits, shared.args.auto_devices, shared.args.disk, shared.args.gpu_memory is not None, shared.args.cpu_memory is not None, shared.args.deepspeed, shared.args.flexgen, shared.is_RWKV]):
if any(size in shared.model_name.lower() for size in ('13b', '20b', '30b')):
model = AutoModelForCausalLM.from_pretrained(Path(f"models/{shared.model_name}"), device_map='auto', load_in_8bit=True)
else:
@ -87,11 +87,11 @@ def load_model(model_name):
return model, tokenizer
# 4-bit LLaMA
elif shared.args.gptq_bits > 0 or shared.args.load_in_4bit:
from modules.quantized_LLaMA import load_quantized_LLaMA
# Quantized model
elif shared.args.gptq_bits > 0:
from modules.quant_loader import load_quant
model = load_quantized_LLaMA(model_name)
model = load_quant(model_name, shared.args.gptq_model_type)
# Custom
else: