metadata
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
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
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
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INFO:__main__:{} - done merge saving to file: matlok/tinyllama-cinder-openhermes-32k
config.json: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 724/724 [00:00<00:00, 6.15MB/s]
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generation_config.json: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 133/133 [00:00<00:00, 1.82MB/s]
INFO:__main__:loading newly-created file: matlok/tinyllama-cinder-openhermes-32k
INFO:__main__:model keys=201
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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