quantized_model = "4bit_quantized-qwen2_1.5B" device = "cuda" if torch.cuda.is_available() else "cpu" model = AutoModelForCausalLM.from_pretrained(pretrained_model, quantization_config=quantization_config) tokenizer = AutoTokenizer.from_pretrained(pretrained_model) 4. 准备输入并生成响应 最后,我...
model_path = "your_model_path" quant_path = "your_quantized_model_path" quant_config = { "zero_point": True, "q_group_size": 128, "w_bit": 4, "version": "GEMM" } # Load your tokenizer and model with AutoAWQ tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoA...
# set to False can significantly speed up inference but the perplexity may slightly bad)#load un-quantized model, by default, the model will always be loaded into CPU memorymodel = AutoGPTQForCausalLM.from_pretrained(pretrained_model_dir, quantize_config, trust_remote...
bit group_size=128, # it is recommended to set the value to 128 desc_act=False, # set to False can significantly speed up inference but the perplexity may slightly bad ) #load un-quantized model, by default, the model will always be loaded into CPU memory model = AutoGPTQForCausalLM...
If you want to quantize your own model to AWQ quantized models, we advise you to use AutoAWQ. It is suggested installing the latest version of the package by installing from source code: ```bash git clone https://github.com/casper-hansen/AutoAWQ.git cd AutoAWQ pip install -e . ...
fromtransformersimportBitsAndBytesConfig,AutoModelForCausalLM bnb_config=BitsAndBytesConfig(load_in_8bit=True,)model=AutoModelForCausalLM.from_pretrained(some-model-id,quantization_config=bnb_config) 1. 2. 3. 4. 5. 6. 7. 8. 由于BnB量化不需要任何校准数据集,因此其量化速度很快,这也是为什么在Q...
from_pretrained(model_path) tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) # Quantize model.quantize(tokenizer, quant_config=quant_config) # Save quantized model model.save_quantized(quant_path) tokenizer.save_pretrained(quant_path) print(f'Model is quantized and ...
$ python3-m fastchat.serve.cli \>--model-path/data/shuzhang/models/deepseek/deepseek-coder-33B-instruct-AWQ\>--awq-wbits4\>--awq-groupsize128LoadingAWQquantized model...Special tokens have been addedinthe vocabulary,make sure the associated word embeddings are fine-tuned or trained.real...
(' ')) > 20] # 设置AWQ量化的超参数,目前AutoAWQ仅支持4bit量化,需设置w_bit为4 quant_config = { "zero_point": True, "q_group_size": 128, "w_bit": 4, "version": "GEMM" } model.quantize(tokenizer, quant_config=quant_config, calib_data=dataset) model.save_quantized(quant_path...
[用法]:使用Vllm AutoAWQ与4个GPU时，未利用GPU,尝试这个：