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use new quant loader

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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
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12
modules/models.py
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@ -1,6 +1,5 @@
import json import json
import os import os
import sys
import time import time
import zipfile import zipfile
from pathlib import Path 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) 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 dschf = HfDeepSpeedConfig(ds_config) # Keep this object alive for the Transformers integration
def load_model(model_name): def load_model(model_name):
print(f"Loading {model_name}...") print(f"Loading {model_name}...")
t0 = time.time() t0 = time.time()
@ -42,7 +42,7 @@ def load_model(model_name):
shared.is_RWKV = model_name.lower().startswith('rwkv-') shared.is_RWKV = model_name.lower().startswith('rwkv-')
# Default settings # 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')): 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) model = AutoModelForCausalLM.from_pretrained(Path(f"models/{shared.model_name}"), device_map='auto', load_in_8bit=True)
else: else:
@ -87,11 +87,11 @@ def load_model(model_name):
return model, tokenizer return model, tokenizer
# 4-bit LLaMA # Quantized model
elif shared.args.gptq_bits > 0 or shared.args.load_in_4bit: elif shared.args.gptq_bits > 0:
from modules.quantized_LLaMA import load_quantized_LLaMA from modules.quant_loader import load_quant
model = load_quantized_LLaMA(model_name) model = load_quant(model_name, shared.args.gptq_model_type)
# Custom # Custom
else: else: