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import pathlib
import tempfile
from typing import Generator
import gradio as gr
import torch
import yaml
from gradio_logsview import LogsView
has_gpu = torch.cuda.is_available()
cli = "mergekit-yaml config.yaml merge --copy-tokenizer" + (
" --cuda --low-cpu-memory"
if has_gpu
else " --allow-crimes --out-shard-size 1B --lazy-unpickle"
)
print(cli)
## This Space is heavily inspired by LazyMergeKit by Maxime Labonne
## https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb
MARKDOWN_DESCRIPTION = """
# mergekit-gui
The fastest way to perform a model merge 🔥
Specify a YAML configuration file (see examples below) and a HF token and this app will perform the merge and upload the merged model to your user profile.
"""
MARKDOWN_ARTICLE = """
___
## Merge Configuration
[Mergekit](https://github.com/arcee-ai/mergekit) configurations are YAML documents specifying the operations to perform in order to produce your merged model.
Below are the primary elements of a configuration file:
- `merge_method`: Specifies the method to use for merging models. See [Merge Methods](https://github.com/arcee-ai/mergekit#merge-methods) for a list.
- `slices`: Defines slices of layers from different models to be used. This field is mutually exclusive with `models`.
- `models`: Defines entire models to be used for merging. This field is mutually exclusive with `slices`.
- `base_model`: Specifies the base model used in some merging methods.
- `parameters`: Holds various parameters such as weights and densities, which can also be specified at different levels of the configuration.
- `dtype`: Specifies the data type used for the merging operation.
- `tokenizer_source`: Determines how to construct a tokenizer for the merged model.
## Merge Methods
A quick overview of the currently supported merge methods:
| Method | `merge_method` value | Multi-Model | Uses base model |
| -------------------------------------------------------------------------------------------- | -------------------- | ----------- | --------------- |
| Linear ([Model Soups](https://arxiv.org/abs/2203.05482)) | `linear` | ✅ | ❌ |
| SLERP | `slerp` | ❌ | ✅ |
| [Task Arithmetic](https://arxiv.org/abs/2212.04089) | `task_arithmetic` | ✅ | ✅ |
| [TIES](https://arxiv.org/abs/2306.01708) | `ties` | ✅ | ✅ |
| [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) | `dare_ties` | ✅ | ✅ |
| [DARE](https://arxiv.org/abs/2311.03099) [Task Arithmetic](https://arxiv.org/abs/2212.04089) | `dare_linear` | ✅ | ✅ |
| Passthrough | `passthrough` | ❌ | ❌ |
| [Model Stock](https://arxiv.org/abs/2403.19522) | `model_stock` | ✅ | ✅ |
"""
examples = [[f.name, f.read_text()] for f in pathlib.Path("examples").glob("*.yml")]
def merge(
example_filename: str, yaml_config: str, hf_token: str, repo_name: str
) -> Generator[str, None, None]:
if not yaml_config:
raise gr.Error("Empty yaml, pick an example below")
try:
_ = yaml.safe_load(yaml_config)
except Exception as e:
raise gr.Error(f"Invalid yaml {e}")
with tempfile.TemporaryDirectory() as tmpdirname:
tmpdir = pathlib.Path(tmpdirname)
config_path = tmpdir / "config.yaml"
config_path.write_text(yaml_config)
cmd = f"cd {tmpdir} && {cli}"
print(cmd)
print(tmpdir.is_dir())
yield from LogsView.run_process(cmd.split())
## TODO(implement upload at the end of the merge, and display the repo URL)
with gr.Blocks() as demo:
gr.Markdown(MARKDOWN_DESCRIPTION)
with gr.Row():
filename = gr.Textbox(visible=False, label="filename")
config = gr.Code(
language="yaml",
lines=10,
label="config.yaml",
)
with gr.Column():
token = gr.Textbox(
lines=1,
label="HF Write Token",
info="https://hf.co/settings/token",
type="password",
placeholder="optional, will not upload merge if empty (dry-run)",
)
repo_name = gr.Textbox(
lines=1,
label="Repo name",
placeholder="optional, will create a random name if empty",
)
button = gr.Button("Merge", variant="primary")
logs = LogsView()
gr.Examples(examples, label="Examples", inputs=[filename, config], outputs=[logs])
gr.Markdown(MARKDOWN_ARTICLE)
button.click(fn=merge, inputs=[filename, config, token, repo_name], outputs=[logs])
demo.queue(default_concurrency_limit=1).launch()
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