File size: 65,025 Bytes

---
license: unknown
---

## Merging models like lego blocks using ddare and ties

This model was merged with the following HuggingFace TinyLlama models using ties:

- 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

## How do I fine-tune this model?

Please refer to the Unsloth fine-tuning guide for:

- [Alpaca + TinyLlama + RoPE Scaling full example.ipynb](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing)

## How do I generate my own model merges?

Here's the standalone python script we used with logs below:

```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 the logs from the code above:

```
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
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INFO:__main__:model=model.norm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68
INFO:__main__:model=lm_head.weight 201 shape=torch.Size([32000, 2048]) dtype=torch.float16 cpu raio=0.5 contig=True norm=0.68
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__: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)