--- license: unknown --- ## Merging models like lego blocks using ddare and ties If you want to fine-tune, here's an example Unsloth fine tuning guide for: [Alpaca + TinyLlama + RoPE Scaling full example.ipynb](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing#scrollTo=LjY75GoYUCB8) ## How do I generate my own model merges? The code below merges the following HuggingFace TinyLlama models: - TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T - Doctor-Shotgun/TinyLlama-1.1B-32k-Instruct - Doctor-Shotgun/TinyLlama-1.1B-32k - Tensoic/TinyLlama-1.1B-3T-openhermes - Josephgflowers/TinyLlama-3T-Cinder-v1.3 ```python3 import transformers import torch import logging from ddare.merge import merge_tensors from ddare.tensor import dare_ties_sparsification, relative_norm, divide_tensor_into_sets from ddare.util import get_device import re from typing import Dict, Tuple, List logging.basicConfig(level=logging.INFO) log = logging.getLogger(__name__) def get_models( models: List[str], trust_remote_code: bool, ): config = { 'torch_dtype': torch.float16, 'low_cpu_mem_usage': False, 'trust_remote_code': trust_remote_code, } loaded_models = [] num_models = len(models) for midx, model_path in enumerate(models): log.info( f"loading model={midx}/{num_models} " f"model={model_path} " ) loaded_models.append( transformers.AutoModelForCausalLM.from_pretrained( model_path, **config ) ) return loaded_models def pm( model, ): keys = model.state_dict().keys() log.info(f"model keys={len(keys)}") for i, k in enumerate(keys): tensor = model.state_dict()[k] log.info( f"{i:3d} {k} shape={tensor.shape} " f"type={tensor.dtype} dev={tensor.device} " f"contig={tensor.is_contiguous()}") def run_text_test( model, model_path, device: str, question: str, ): base_model = model.to(device) log.info( f"loading model={model_path}" ) tokenizer = transformers.AutoTokenizer.from_pretrained( model_path, torch_dtype=torch.float16) inputs = tokenizer( question, return_tensors="pt" ).to("cuda") with torch.backends.cuda.sdp_kernel( enable_flash=True, enable_math=False, enable_mem_efficient=False ): outputs = base_model.generate(**inputs) log.info(tokenizer.decode(outputs[0], skip_special_tokens=True)) base_model = base_model.to("cpu") def get_layer_type( key: str ) -> Tuple[int, str]: matcher = re.compile(r"model.layers.(\d+).(.+)") m = matcher.match(key) if m is None: if "model.norm.weight" == key: return -1, "norm" if "model.embed_tokens.weight" == key: return -1, "embed" if "lm_head.weight" == key: return -1, "head" log.info(f"Unknown key {key}") return -1, "unknown" return int(m.group(1)), m.group(2) def merge_model_with_ties( models: List[str], model_dst: str, trust_remote_code: bool = True ): models = get_models( models=models, trust_remote_code=trust_remote_code, ) config = {} result_dict: Dict[str, torch.Tensor] = {} device = get_device() keys = models[0].state_dict().keys() num_keys = len(keys) for k in keys: block, layer_type = get_layer_type(k) m0: torch.Tensor = models[0].state_dict()[k] result = m0.clone() sets = divide_tensor_into_sets(tensor=m0, n_sets=4) # get the src layers to merge m = [ models[1].state_dict()[k], models[2].state_dict()[k], models[3].state_dict()[k], ] # build a ratio ratio = { 'to_q': 0.0, 'to_k': 0.0, 'to_v': 0.0, }.get(layer_type, .5) norm_ratio = 0.68 log.info( f"model={k} {num_keys} shape={m0.shape} " f"dtype={m0.dtype} {m0.device} " f"raio={ratio} " f"contig={m0.is_contiguous()} " f"norm={norm_ratio}") # for all tensors for i, tensor in enumerate(m): if layer_type == "to_k": # Get to_q key q_base = models[0].state_dict()[k.replace("to_k", "to_q")] q_merge = models[i].state_dict()[k.replace("to_k", "to_q")] scale = relative_norm(q_merge, q_base) tensor = tensor.to(device) / scale del scale elif layer_type == "to_q": scale = relative_norm(tensor, m0) tensor = tensor.to(device) * scale del scale slice_mask = ( sets == i ).bool() new_tensor = dare_ties_sparsification( model_a_param=m0, model_b_param=tensor, drop_rate=norm_ratio, ties="sum", rescale="off", device=device, **config) new_tensor = merge_tensors("slerp", m0, tensor, ratio) result = torch.where(slice_mask, new_tensor, result) del new_tensor, slice_mask result_dict[k] = result # end of merge log.info( f"{config} - done merge saving to file: {model_dst}" ) out_model = ( transformers.AutoModelForCausalLM.from_pretrained( model_dst, **config ) ) out_model.state_dict = lambda: result_dict out_model.save_pretrained(model_dst) def run(): log.info("start") model_src = "TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T" model_dst = "matlok/tinyllama-cinder-openhermes-32k" config = { 'torch_dtype': torch.float16, 'low_cpu_mem_usage': False, 'trust_remote_code': True, } models = [ model_src, "Doctor-Shotgun/TinyLlama-1.1B-32k-Instruct", "Doctor-Shotgun/TinyLlama-1.1B-32k", "Tensoic/TinyLlama-1.1B-3T-openhermes", "Josephgflowers/TinyLlama-3T-Cinder-v1.3", ] merge_model_with_ties( models=models, model_dst=model_dst ) log.info(f"loading newly-created file: {model_dst}") model = transformers.AutoModelForCausalLM.from_pretrained( model_dst, **config ) pm(model=model) log.info(f"done loading new model: {model} file: {model_dst}") if __name__ == "__main__": run() ``` ### Logs Here's hte logs ``` Total VRAM 12282 MB, total RAM 85434 MB Set vram state to: NORMAL_VRAM Device: cuda:0 NVIDIA GeForce RTX 4070 Ti : native VAE dtype: torch.bfloat16 INFO:__main__:start INFO:__main__:loading model=0/5 model=TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T INFO:__main__:loading model=1/5 model=Doctor-Shotgun/TinyLlama-1.1B-32k-Instruct INFO:__main__:loading model=2/5 model=Doctor-Shotgun/TinyLlama-1.1B-32k INFO:__main__:loading model=3/5 model=Tensoic/TinyLlama-1.1B-3T-openhermes INFO:__main__:loading model=4/5 model=Josephgflowers/TinyLlama-3T-Cinder-v1.3 INFO:__main__:model=model.embed_tokens.weight 201 shape=torch.Size([32000, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.0.self_attn.q_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.0.self_attn.k_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.0.self_attn.v_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.0.self_attn.o_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.0.mlp.gate_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.0.mlp.up_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.0.mlp.down_proj.weight 201 shape=torch.Size([2048, 5632]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.0.input_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.0.post_attention_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.1.self_attn.q_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.1.self_attn.k_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.1.self_attn.v_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.1.self_attn.o_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.1.mlp.gate_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.1.mlp.up_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.1.mlp.down_proj.weight 201 shape=torch.Size([2048, 5632]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.1.input_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.1.post_attention_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.2.self_attn.q_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.2.self_attn.k_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.2.self_attn.v_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.2.self_attn.o_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.2.mlp.gate_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.2.mlp.up_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.2.mlp.down_proj.weight 201 shape=torch.Size([2048, 5632]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.2.input_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.2.post_attention_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.3.self_attn.q_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.3.self_attn.k_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.3.self_attn.v_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.3.self_attn.o_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.3.mlp.gate_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.3.mlp.up_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.3.mlp.down_proj.weight 201 shape=torch.Size([2048, 5632]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.3.input_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.3.post_attention_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.4.self_attn.q_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.4.self_attn.k_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.4.self_attn.v_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.4.self_attn.o_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.4.mlp.gate_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.4.mlp.up_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.4.mlp.down_proj.weight 201 shape=torch.Size([2048, 5632]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.4.input_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.4.post_attention_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.5.self_attn.q_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.5.self_attn.k_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.5.self_attn.v_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.5.self_attn.o_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.5.mlp.gate_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.5.mlp.up_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.5.mlp.down_proj.weight 201 shape=torch.Size([2048, 5632]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.5.input_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.5.post_attention_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.6.self_attn.q_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.6.self_attn.k_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.6.self_attn.v_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.6.self_attn.o_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.6.mlp.gate_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.6.mlp.up_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.6.mlp.down_proj.weight 201 shape=torch.Size([2048, 5632]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.6.input_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.6.post_attention_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.7.self_attn.q_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.7.self_attn.k_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.7.self_attn.v_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.7.self_attn.o_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.7.mlp.gate_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.7.mlp.up_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.7.mlp.down_proj.weight 201 shape=torch.Size([2048, 5632]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.7.input_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.7.post_attention_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.8.self_attn.q_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.8.self_attn.k_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.8.self_attn.v_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.8.self_attn.o_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.8.mlp.gate_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.8.mlp.up_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.8.mlp.down_proj.weight 201 shape=torch.Size([2048, 5632]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.8.input_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.8.post_attention_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.9.self_attn.q_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.9.self_attn.k_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.9.self_attn.v_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.9.self_attn.o_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.9.mlp.gate_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.9.mlp.up_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.9.mlp.down_proj.weight 201 shape=torch.Size([2048, 5632]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.9.input_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.9.post_attention_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.10.self_attn.q_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.10.self_attn.k_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.10.self_attn.v_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.10.self_attn.o_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.10.mlp.gate_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.10.mlp.up_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.10.mlp.down_proj.weight 201 shape=torch.Size([2048, 5632]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.10.input_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.10.post_attention_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.11.self_attn.q_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.11.self_attn.k_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.11.self_attn.v_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.11.self_attn.o_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.11.mlp.gate_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.11.mlp.up_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.11.mlp.down_proj.weight 201 shape=torch.Size([2048, 5632]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.11.input_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.11.post_attention_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.12.self_attn.q_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.12.self_attn.k_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.12.self_attn.v_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.12.self_attn.o_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.12.mlp.gate_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.12.mlp.up_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.12.mlp.down_proj.weight 201 shape=torch.Size([2048, 5632]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.12.input_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.12.post_attention_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.13.self_attn.q_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.13.self_attn.k_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.13.self_attn.v_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.13.self_attn.o_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.13.mlp.gate_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.13.mlp.up_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.13.mlp.down_proj.weight 201 shape=torch.Size([2048, 5632]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.13.input_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.13.post_attention_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.14.self_attn.q_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.14.self_attn.k_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.14.self_attn.v_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.14.self_attn.o_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.14.mlp.gate_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.14.mlp.up_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.14.mlp.down_proj.weight 201 shape=torch.Size([2048, 5632]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.14.input_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.14.post_attention_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.15.self_attn.q_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.15.self_attn.k_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.15.self_attn.v_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.15.self_attn.o_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.15.mlp.gate_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.15.mlp.up_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.15.mlp.down_proj.weight 201 shape=torch.Size([2048, 5632]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.15.input_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.15.post_attention_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.16.self_attn.q_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.16.self_attn.k_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.16.self_attn.v_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.16.self_attn.o_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.16.mlp.gate_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.16.mlp.up_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.16.mlp.down_proj.weight 201 shape=torch.Size([2048, 5632]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.16.input_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.16.post_attention_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.17.self_attn.q_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.17.self_attn.k_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.17.self_attn.v_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.17.self_attn.o_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.17.mlp.gate_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.17.mlp.up_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.17.mlp.down_proj.weight 201 shape=torch.Size([2048, 5632]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.17.input_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.17.post_attention_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.18.self_attn.q_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.18.self_attn.k_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.18.self_attn.v_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.18.self_attn.o_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.18.mlp.gate_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.18.mlp.up_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.18.mlp.down_proj.weight 201 shape=torch.Size([2048, 5632]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.18.input_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.18.post_attention_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.19.self_attn.q_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.19.self_attn.k_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.19.self_attn.v_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.19.self_attn.o_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.19.mlp.gate_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.19.mlp.up_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.19.mlp.down_proj.weight 201 shape=torch.Size([2048, 5632]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.19.input_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.19.post_attention_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.20.self_attn.q_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.20.self_attn.k_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.20.self_attn.v_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.20.self_attn.o_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.20.mlp.gate_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.20.mlp.up_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.20.mlp.down_proj.weight 201 shape=torch.Size([2048, 5632]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.20.input_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.20.post_attention_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.21.self_attn.q_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.21.self_attn.k_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.21.self_attn.v_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.21.self_attn.o_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.21.mlp.gate_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 INFO:__main__:model=model.layers.21.mlp.up_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68 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model.layers.0.self_attn.o_proj.weight shape=torch.Size([2048, 2048]) type=torch.float16 dev=cpu contig=True INFO:__main__: 5 model.layers.0.mlp.gate_proj.weight shape=torch.Size([5632, 2048]) type=torch.float16 dev=cpu contig=True INFO:__main__: 6 model.layers.0.mlp.up_proj.weight shape=torch.Size([5632, 2048]) type=torch.float16 dev=cpu contig=True INFO:__main__: 7 model.layers.0.mlp.down_proj.weight shape=torch.Size([2048, 5632]) type=torch.float16 dev=cpu contig=True INFO:__main__: 8 model.layers.0.input_layernorm.weight shape=torch.Size([2048]) type=torch.float16 dev=cpu contig=True INFO:__main__: 9 model.layers.0.post_attention_layernorm.weight shape=torch.Size([2048]) type=torch.float16 dev=cpu contig=True INFO:__main__: 10 model.layers.1.self_attn.q_proj.weight shape=torch.Size([2048, 2048]) type=torch.float16 dev=cpu contig=True INFO:__main__: 11 model.layers.1.self_attn.k_proj.weight shape=torch.Size([256, 2048]) type=torch.float16 dev=cpu contig=True INFO:__main__: 12 model.layers.1.self_attn.v_proj.weight shape=torch.Size([256, 2048]) type=torch.float16 dev=cpu contig=True INFO:__main__: 13 model.layers.1.self_attn.o_proj.weight shape=torch.Size([2048, 2048]) type=torch.float16 dev=cpu contig=True INFO:__main__: 14 model.layers.1.mlp.gate_proj.weight shape=torch.Size([5632, 2048]) type=torch.float16 dev=cpu contig=True INFO:__main__: 15 model.layers.1.mlp.up_proj.weight shape=torch.Size([5632, 2048]) type=torch.float16 dev=cpu contig=True INFO:__main__: 16 model.layers.1.mlp.down_proj.weight shape=torch.Size([2048, 5632]) type=torch.float16 dev=cpu contig=True INFO:__main__: 17 model.layers.1.input_layernorm.weight shape=torch.Size([2048]) type=torch.float16 dev=cpu contig=True INFO:__main__: 18 model.layers.1.post_attention_layernorm.weight shape=torch.Size([2048]) type=torch.float16 dev=cpu contig=True INFO:__main__: 19 model.layers.2.self_attn.q_proj.weight shape=torch.Size([2048, 2048]) type=torch.float16 dev=cpu contig=True INFO:__main__: 20 model.layers.2.self_attn.k_proj.weight shape=torch.Size([256, 2048]) type=torch.float16 dev=cpu contig=True INFO:__main__: 21 model.layers.2.self_attn.v_proj.weight shape=torch.Size([256, 2048]) type=torch.float16 dev=cpu contig=True INFO:__main__: 22 model.layers.2.self_attn.o_proj.weight shape=torch.Size([2048, 2048]) type=torch.float16 dev=cpu contig=True INFO:__main__: 23 model.layers.2.mlp.gate_proj.weight shape=torch.Size([5632, 2048]) type=torch.float16 dev=cpu contig=True INFO:__main__: 24 model.layers.2.mlp.up_proj.weight shape=torch.Size([5632, 2048]) type=torch.float16 dev=cpu contig=True INFO:__main__: 25 model.layers.2.mlp.down_proj.weight shape=torch.Size([2048, 5632]) type=torch.float16 dev=cpu contig=True INFO:__main__: 26 model.layers.2.input_layernorm.weight shape=torch.Size([2048]) type=torch.float16 dev=cpu contig=True INFO:__main__: 27 model.layers.2.post_attention_layernorm.weight shape=torch.Size([2048]) type=torch.float16 dev=cpu contig=True INFO:__main__: 28 model.layers.3.self_attn.q_proj.weight shape=torch.Size([2048, 2048]) type=torch.float16 dev=cpu contig=True INFO:__main__: 29 model.layers.3.self_attn.k_proj.weight shape=torch.Size([256, 2048]) type=torch.float16 dev=cpu contig=True INFO:__main__: 30 model.layers.3.self_attn.v_proj.weight shape=torch.Size([256, 2048]) type=torch.float16 dev=cpu contig=True INFO:__main__: 31 model.layers.3.self_attn.o_proj.weight shape=torch.Size([2048, 2048]) type=torch.float16 dev=cpu contig=True INFO:__main__: 32 model.layers.3.mlp.gate_proj.weight shape=torch.Size([5632, 2048]) type=torch.float16 dev=cpu contig=True INFO:__main__: 33 model.layers.3.mlp.up_proj.weight shape=torch.Size([5632, 2048]) type=torch.float16 dev=cpu contig=True INFO:__main__: 34 model.layers.3.mlp.down_proj.weight shape=torch.Size([2048, 5632]) type=torch.float16 dev=cpu contig=True INFO:__main__: 35 model.layers.3.input_layernorm.weight shape=torch.Size([2048]) type=torch.float16 dev=cpu contig=True INFO:__main__: 36 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shape=torch.Size([2048]) type=torch.float16 dev=cpu contig=True INFO:__main__:163 model.layers.18.self_attn.q_proj.weight shape=torch.Size([2048, 2048]) type=torch.float16 dev=cpu contig=True INFO:__main__:164 model.layers.18.self_attn.k_proj.weight shape=torch.Size([256, 2048]) type=torch.float16 dev=cpu contig=True INFO:__main__:165 model.layers.18.self_attn.v_proj.weight shape=torch.Size([256, 2048]) type=torch.float16 dev=cpu contig=True INFO:__main__:166 model.layers.18.self_attn.o_proj.weight shape=torch.Size([2048, 2048]) type=torch.float16 dev=cpu contig=True INFO:__main__:167 model.layers.18.mlp.gate_proj.weight shape=torch.Size([5632, 2048]) type=torch.float16 dev=cpu contig=True INFO:__main__:168 model.layers.18.mlp.up_proj.weight shape=torch.Size([5632, 2048]) type=torch.float16 dev=cpu contig=True INFO:__main__:169 model.layers.18.mlp.down_proj.weight shape=torch.Size([2048, 5632]) type=torch.float16 dev=cpu contig=True INFO:__main__:170 model.layers.18.input_layernorm.weight shape=torch.Size([2048]) type=torch.float16 dev=cpu contig=True INFO:__main__:171 model.layers.18.post_attention_layernorm.weight shape=torch.Size([2048]) type=torch.float16 dev=cpu contig=True INFO:__main__:172 model.layers.19.self_attn.q_proj.weight shape=torch.Size([2048, 2048]) type=torch.float16 dev=cpu contig=True INFO:__main__:173 model.layers.19.self_attn.k_proj.weight shape=torch.Size([256, 2048]) type=torch.float16 dev=cpu contig=True INFO:__main__:174 model.layers.19.self_attn.v_proj.weight shape=torch.Size([256, 2048]) type=torch.float16 dev=cpu contig=True INFO:__main__:175 model.layers.19.self_attn.o_proj.weight shape=torch.Size([2048, 2048]) type=torch.float16 dev=cpu contig=True INFO:__main__:176 model.layers.19.mlp.gate_proj.weight shape=torch.Size([5632, 2048]) type=torch.float16 dev=cpu contig=True INFO:__main__:177 model.layers.19.mlp.up_proj.weight shape=torch.Size([5632, 2048]) type=torch.float16 dev=cpu contig=True INFO:__main__:178 model.layers.19.mlp.down_proj.weight shape=torch.Size([2048, 5632]) type=torch.float16 dev=cpu contig=True INFO:__main__:179 model.layers.19.input_layernorm.weight shape=torch.Size([2048]) type=torch.float16 dev=cpu contig=True INFO:__main__:180 model.layers.19.post_attention_layernorm.weight shape=torch.Size([2048]) type=torch.float16 dev=cpu contig=True INFO:__main__:181 model.layers.20.self_attn.q_proj.weight shape=torch.Size([2048, 2048]) type=torch.float16 dev=cpu contig=True INFO:__main__:182 model.layers.20.self_attn.k_proj.weight shape=torch.Size([256, 2048]) type=torch.float16 dev=cpu contig=True INFO:__main__:183 model.layers.20.self_attn.v_proj.weight shape=torch.Size([256, 2048]) type=torch.float16 dev=cpu contig=True INFO:__main__:184 model.layers.20.self_attn.o_proj.weight shape=torch.Size([2048, 2048]) type=torch.float16 dev=cpu contig=True INFO:__main__:185 model.layers.20.mlp.gate_proj.weight shape=torch.Size([5632, 2048]) type=torch.float16 dev=cpu contig=True INFO:__main__:186 model.layers.20.mlp.up_proj.weight shape=torch.Size([5632, 2048]) type=torch.float16 dev=cpu contig=True INFO:__main__:187 model.layers.20.mlp.down_proj.weight shape=torch.Size([2048, 5632]) type=torch.float16 dev=cpu contig=True INFO:__main__:188 model.layers.20.input_layernorm.weight shape=torch.Size([2048]) type=torch.float16 dev=cpu contig=True INFO:__main__:189 model.layers.20.post_attention_layernorm.weight shape=torch.Size([2048]) type=torch.float16 dev=cpu contig=True INFO:__main__:190 model.layers.21.self_attn.q_proj.weight shape=torch.Size([2048, 2048]) type=torch.float16 dev=cpu contig=True INFO:__main__:191 model.layers.21.self_attn.k_proj.weight shape=torch.Size([256, 2048]) type=torch.float16 dev=cpu contig=True INFO:__main__:192 model.layers.21.self_attn.v_proj.weight shape=torch.Size([256, 2048]) type=torch.float16 dev=cpu contig=True INFO:__main__:193 model.layers.21.self_attn.o_proj.weight shape=torch.Size([2048, 2048]) type=torch.float16 dev=cpu contig=True INFO:__main__:194 model.layers.21.mlp.gate_proj.weight shape=torch.Size([5632, 2048]) type=torch.float16 dev=cpu contig=True INFO:__main__:195 model.layers.21.mlp.up_proj.weight shape=torch.Size([5632, 2048]) type=torch.float16 dev=cpu contig=True INFO:__main__:196 model.layers.21.mlp.down_proj.weight shape=torch.Size([2048, 5632]) type=torch.float16 dev=cpu contig=True INFO:__main__:197 model.layers.21.input_layernorm.weight shape=torch.Size([2048]) type=torch.float16 dev=cpu contig=True INFO:__main__:198 model.layers.21.post_attention_layernorm.weight shape=torch.Size([2048]) type=torch.float16 dev=cpu contig=True INFO:__main__:199 model.norm.weight shape=torch.Size([2048]) type=torch.float16 dev=cpu contig=True INFO:__main__:200 lm_head.weight shape=torch.Size([32000, 2048]) type=torch.float16 dev=cpu contig=True INFO:__main__:done loading new model: LlamaForCausalLM( (model): LlamaModel( (embed_tokens): Embedding(32000, 2048) (layers): ModuleList( (0-21): 22 x LlamaDecoderLayer( (self_attn): LlamaSdpaAttention( (q_proj): Linear(in_features=2048, out_features=2048, bias=False) (k_proj): Linear(in_features=2048, out_features=256, bias=False) (v_proj): Linear(in_features=2048, out_features=256, bias=False) (o_proj): Linear(in_features=2048, out_features=2048, bias=False) (rotary_emb): LlamaRotaryEmbedding() ) (mlp): LlamaMLP( (gate_proj): Linear(in_features=2048, out_features=5632, bias=False) (up_proj): Linear(in_features=2048, out_features=5632, bias=False) (down_proj): Linear(in_features=5632, out_features=2048, bias=False) (act_fn): SiLU() ) (input_layernorm): LlamaRMSNorm() (post_attention_layernorm): LlamaRMSNorm() ) ) (norm): LlamaRMSNorm() ) (lm_head): Linear(in_features=2048, out_features=32000, bias=False) ) file: matlok/tinyllama-cinder-openhermes-32k real 1m18.070s user 2m10.228s sys 0m14.040s ``` Note: code sample above was modified from [this very helpful GitHub gist](https://gist.github.com/maldevide/08829eada04ad9bd78e46c1a3787d42b)