low_cpu_mem_usage=True, return_dict=True, torch_dtype=torch.float16, device_map=device_map, )# Merge LoRA and base modelmerged_model = model.merge_and_unload()# Save the merged modelmerged_model.save_pretrained("merged_model",safe_serialization=True)tokenizer...
And here is my code for merging the weights: model = AutoPeftModelForCausalLM.from_pretrained(qlora_path, device_map="auto", torch_dtype=torch.bfloat16) model = model.merge_and_unload() output_merged_dir = "/dbfs/$folder" os.makedirs(output_merged_dir, exist_ok=True) model.save_pret...
from_pretrained( base_path, device_map="cuda:0", max_memory=max_memory, trust_remote_code=True, #use_safetensors=True, bf16=True ).eval() merge_model = PeftModel.from_pretrained(model, adapter_path) merge_model.generation_config.eos_token_id=[2512,19357,151643] Expected behavior I ...
- peft_config.base_model_name_or_path, load_in_8bit=True, device_map="auto" - ) - model = PeftModel.from_pretrained(model, peft_model_id) + model = AutoPeftModel.from_pretrained(peft_model_id) ``` ## Next steps 1 change: 1 addition & 0 deletions 1 src/peft/__init__.py ...
()), id2label=id2label, label2id=label2id, device_map='auto', quantization_config=quant_config, pad_token_id=2) if model_type=='AutoModelForCausalLM': model=AutoModelForCausalLM.from_pretrained(model_name,cache_dir=cache_dir, output_hidden_states=True, torch_dtype=torch.float16, #...
model:ISTA-DASLab/Mixtral-8x7B-Instruct-v0_1-AQLM-2Bit-1x16-hf peft:0.10.0 model = AutoModelForCausalLM.from_pretrained(base_model, trust_remote_code=True, attn_implementation="flash_attention_2", torch_dtype=torch.bfloat16, device_map="...
Distributed Training: Supports distributed data parallel (DDP), device_map simple model parallelism, DeepSpeed ZeRO2/ZeRO3, FSDP, and other distributed training techniques. Quantization Training: Supports training quantized models like BNB, AWQ, GPTQ, AQLM, HQQ, EETQ. ...
map_location=torch.device("cuda" if torch.cuda.is_available() else "cpu") ) # load the weights into the model set_peft_model_state_dict(self, adapters_weights, adapter_name=adapter_name) if ( (getattr(self, "hf_device_map", None) is not None) and (len(set(self.hf_device_...
from_pretrained( model_name, num_labels=2, load_in_8bit=True, device_map="auto" ) model_loaded = PeftModel.from_pretrained( model_origin, "./tmp/issue-876.pt/", is_trainable=False, ).to(device) print(model_loaded(X).logits) # tensor([[ 3.0352, -5.0742], # [-5.7578, 5.8828],...
│ 357 │ │ │ (getattr(self, "hf_device_map", None) is not None) │ │ 358 │ │ │ and (len(set(self.hf_device_map.values()).intersection({"cpu", "disk"})) > 0 │ │ │ │ /usr/local/lib/python3.7/site-packages/peft/utils/save_and_load.py:120 in │ ...