import pathlib import random import string import tempfile from typing import Iterable, List import gradio as gr import huggingface_hub import torch import yaml from gradio_logsview.logsview import Log, LogsView, LogsViewRunner from mergekit.config import MergeConfiguration has_gpu = torch.cuda.is_available() # Running directly from Python doesn't work well with Gradio+run_process because of: # Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method # Let's use the CLI instead. # # import mergekit.merge # from mergekit.common import parse_kmb # from mergekit.options import MergeOptions # # merge_options = ( # MergeOptions( # copy_tokenizer=True, # cuda=True, # low_cpu_memory=True, # write_model_card=True, # ) # if has_gpu # else MergeOptions( # allow_crimes=True, # out_shard_size=parse_kmb("1B"), # lazy_unpickle=True, # write_model_card=True, # ) # ) cli = "mergekit-yaml config.yaml merge --copy-tokenizer" + ( " --cuda --low-cpu-memory" if has_gpu else " --allow-crimes --out-shard-size 1B --lazy-unpickle" ) ## 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 = [[str(f)] for f in pathlib.Path("examples").glob("*.yml")] def merge(yaml_config: str, hf_token: str, repo_name: str) -> Iterable[List[Log]]: runner = LogsViewRunner() if not yaml_config: yield runner.log("Empty yaml, pick an example below", level="ERROR") return try: merge_config = MergeConfiguration.model_validate(yaml.safe_load(yaml_config)) except Exception as e: yield runner.log(f"Invalid yaml {e}", level="ERROR") return if not hf_token: yield runner.log("You must provide a write-access token.", level="ERROR") return api = huggingface_hub.HfApi(token=hf_token) with tempfile.TemporaryDirectory() as tmpdirname: tmpdir = pathlib.Path(tmpdirname) merged_path = tmpdir / "merged" merged_path.mkdir(parents=True, exist_ok=True) config_path = merged_path / "config.yaml" config_path.write_text(yaml_config) yield runner.log(f"Merge configuration saved in {config_path}") if not repo_name: yield runner.log("No repo name provided. Generating a random one.") repo_name = f"mergekit-{merge_config.merge_method}" # Make repo_name "unique" (no need to be extra careful on uniqueness) repo_name += "-" + "".join(random.choices(string.ascii_lowercase, k=7)) repo_name = repo_name.replace("/", "-").strip("-") try: yield runner.log(f"Creating repo {repo_name}") repo_url = api.create_repo(repo_name, exist_ok=True) yield runner.log(f"Repo created: {repo_url}") except Exception as e: yield runner.log(f"Error creating repo {e}", level="ERROR") return yield from runner.run_command(cli.split(), cwd=merged_path) if runner.exit_code != 0: yield runner.log("Merge failed. Deleting repo as no model is uploaded.", level="ERROR") api.delete_repo(repo_url.repo_id) return yield runner.log("Model merged successfully. Uploading to HF.") yield from runner.run_python( api.upload_folder, repo_id=repo_url.repo_id, folder_path=merged_path / "merge", ) yield runner.log("Model successfully uploaded to HF.") 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="Mandatory. Used to upload the merged model.", ) 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, fn=lambda s: (s,), run_on_click=True, label="Examples", inputs=[filename], outputs=[config], ) gr.Markdown(MARKDOWN_ARTICLE) button.click(fn=merge, inputs=[config, token, repo_name], outputs=[logs]) demo.queue(default_concurrency_limit=1).launch()