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48 lines
1.9 KiB
Python
48 lines
1.9 KiB
Python
# coding=utf-8
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# Quantizes fine-tuned models with AutoGPTQ (https://github.com/PanQiWei/AutoGPTQ).
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# Usage: python auto_gptq.py --input_dir path_to_llama_model --output_dir path_to_quant_model --data_file alpaca.json
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# dataset format: question (string), A (string), B (string), C (string), D (string), answer (Literal["A", "B", "C", "D"])
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import fire
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from datasets import load_dataset
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from transformers import AutoTokenizer
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from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
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def quantize(input_dir: str, output_dir: str, data_file: str):
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tokenizer = AutoTokenizer.from_pretrained(input_dir, use_fast=False, padding_side="left")
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def format_example(examples):
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prefix=("A chat between a curious user and an artificial intelligence assistant. "
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"The assistant gives helpful, detailed, and polite answers to the user's questions.")
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texts = []
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for i in range(len(examples["instruction"])):
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prompt = prefix + "\n"
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if "history" in examples:
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for user_query, bot_resp in examples["history"][i]:
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prompt += "Human: {}\nAssistant: {}\n".format(user_query, bot_resp)
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prompt += "Human: {}\nAssistant: {}".format(examples["instruction"][i], examples["output"][i])
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texts.append(prompt)
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return tokenizer(texts, truncation=True, max_length=1024)
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dataset = load_dataset("json", data_files=data_file)["train"]
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column_names = list(dataset.column_names)
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dataset = dataset.select(range(1024))
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dataset = dataset.map(format_example, batched=True, remove_columns=column_names)
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dataset = dataset.shuffle()
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quantize_config = BaseQuantizeConfig(
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bits=4,
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group_size=128,
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desc_act=False
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)
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model = AutoGPTQForCausalLM.from_pretrained(input_dir, quantize_config)
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model.quantize(dataset)
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model.save_quantized(output_dir)
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if __name__ == "__main__":
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fire.Fire(quantize)
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