mirror of
https://github.com/RYDE-WORK/MiniCPM.git
synced 2026-01-19 21:03:39 +08:00
44 lines
2.0 KiB
Python
44 lines
2.0 KiB
Python
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from datasets import load_dataset
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from awq import AutoAWQForCausalLM
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from transformers import AutoTokenizer
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import os
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model_path = '/root/ld/ld_model_pretrained/MiniCPM-1B-sft-bf16' # model_path or model_id
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quant_path = '/root/ld/ld_project/pull_request/MiniCPM/quantize/awq_cpm_1b_4bit' # quant_save_path
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quant_data_path='/root/ld/ld_project/pull_request/MiniCPM/quantize/quantize_data/wikitext'# 写入自带
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quant_config = { "zero_point": True, "q_group_size": 128, "w_bit": 4, "version": "GEMM" } # "w_bit":4 or 8
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quant_samples=512 # how many samples to use for calibration
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custom_data=[{'question':'你叫什么名字。','answer':'我是openmbmb开源的小钢炮minicpm。'},
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{'question':'你有什么特色。','answer':'我很小，但是我很强。'}]
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# Load model
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model = AutoAWQForCausalLM.from_pretrained(model_path)
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True,device_map={"": "cuda:0"})
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# Define data loading methods
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def load_alpaca(quant_data_path):
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data = load_dataset(quant_data_path, split="train") # Set the absolute path to alpaca or huggingface id
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# concatenate data
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def concatenate_data(x):
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return {"text": '<s><用户>'+x['instruction'] + x['input'] + '<AI>' + '\n' + x['output']}
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concatenated = data.map(concatenate_data)[:quant_samples]
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return [text for text in concatenated["text"]]
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def load_wikitext(quant_data_path):
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data = load_dataset(quant_data_path, split="train")
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return [text for text in data["text"] if text.strip() != '' and len(text.split(' ')) > 20][:quant_samples]
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def load_cust_data(custom_data):
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quant_data=['<s><用户>'+i['question'] + '<AI>' + i['answer'] + '<s>' for i in custom_data]
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return quant_data[:quant_samples]
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# Quantize
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model.quantize(tokenizer, quant_config=quant_config, calib_data=load_wikitext(quant_data_path=quant_data_path))
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# Save quantized model
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model.save_quantized(quant_path)
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tokenizer.save_pretrained(quant_path)
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print(f'Model is quantized and saved at "{quant_path}"') |