I tryed to modify your example code to run this model on lowvram card by BNB 4bit or 8bit quantization config. While use bnb 4bit config like below: qnt_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.float16, bnb_4bit_...
bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16 ) model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", quantization_config=bnb_config) Expected behavior when i am running the above snipped it is...
警告: warnings.warn(f'Input type into Linear4bit is torch.float16, but bnb_4bit_compute_type=torch.float32 (default). This will lead to slow inferenceortraining speed.') Run Code Online (Sandbox Code Playgroud) 硬件: DellPrecision T7920 Tower server/WorkstationIntelxeon gold processor @ 1...
"bnb_4bit_quant_storage": "uint8", "bnb_4bit_quant_type": "nf4", "bnb_4bit_use_double_quant": true, "llm_int8_enable_fp32_cpu_offload": false, "llm_int8_has_fp16_weight": false, "llm_int8_skip_modules": null, "llm_int8_threshold": 6.0, "load_in_4bit": ...
code = quant_state.code.clone().contiguous() dq=bnb.functional.dequantize_blockwise(v_quant,bnb.functional.QuantState(absmax=absmax,code=code, blocksize=16, dtype=torch.float32)) 上面的代码为非线性量化,将param除以最大值归一化到-1到1,然后bnb在quantize_blockwise生成的code是一个长度为256的tenso...
"bnb_4bit_quant_storage":"uint8", "bnb_4bit_quant_type":"nf4", "bnb_4bit_use_double_quant":true, "llm_int8_enable_fp32_cpu_offload":false, "llm_int8_has_fp16_weight":false, "llm_int8_skip_modules":null, "llm_int8_threshold":6.0, ...
return (f"BitsAndBytesConfig(load_in_8bit={self.load_in_8bit}, " f"load_in_4bit={self.load_in_4bit}, " f"bnb_4bit_compute_dtype={self.bnb_4bit_compute_dtype}, " f"bnb_4bit_quant_type={self.bnb_4bit_quant_type}, " ...
bnb_4bit_quant_type="nf4", # 量化格式,这里用的是正太分布的int4 bnb_4bit_use_double_quant=True, # 是否采用双量化,即对zeropoint和scaling参数进行量化 llm_int8_enable_fp32_cpu_offload=False, # 是否llm使用int8,cpu上保存的参数使用fp32 llm_int8_has_fp16_weight=False, # 是否启用混合精度...
"bnb_4bit_quant_type":"nf4", "bnb_4bit_use_double_quant":true, "llm_int8_enable_fp32_cpu_offload":false, "llm_int8_has_fp16_weight":false, "llm_int8_skip_modules":null, "llm_int8_threshold":6.0, "load_in_4bit":true, ...
"bnb_4bit_quant_type":"nf4", "bnb_4bit_use_double_quant":true, "llm_int8_enable_fp32_cpu_offload":true, "llm_int8_has_fp16_weight":false, "llm_int8_skip_modules":null, "llm_int8_threshold":6.0, "load_in_4bit":true, ...