mirror of https://github.com/THUDM/ChatGLM-6B
Change quantization instruction
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4371f7a572
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ca43864f39
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@ -136,7 +136,7 @@ curl -X POST "http://127.0.0.1:8000" \
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```python
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# 按需修改,目前只支持 4/8 bit 量化
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model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).half().quantize(4).cuda()
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model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).quantize(4).half().cuda()
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```
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进行 2 至 3 轮对话后,8-bit 量化下 GPU 显存占用约为 10GB,4-bit 量化下仅需 6GB 占用。随着对话轮数的增多,对应消耗显存也随之增长,由于采用了相对位置编码,理论上 ChatGLM-6B 支持无限长的 context-length,但总长度超过 2048(训练长度)后性能会逐渐下降。
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@ -112,10 +112,10 @@ def main():
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model = AutoModel.from_pretrained(model_args.model_name_or_path, config=config, trust_remote_code=True)
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model = model.half()
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if model_args.quantization_bit is not None:
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print(f"Quantized to {model_args.quantization_bit} bit")
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model = model.quantize(model_args.quantization_bit)
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model = model.half()
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model.transformer.prefix_encoder.float()
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prefix = data_args.source_prefix if data_args.source_prefix is not None else ""
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