import os import pathlib import random import string import tempfile import time import threading 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 from spaces import spaces has_gpu = torch.cuda.is_available() cli = "mergekit-yaml config.yaml merge --copy-tokenizer" + ( " --cuda --low-cpu-memory --allow-crimes" if has_gpu else " --allow-crimes --out-shard-size 1B --lazy-unpickle" ) 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` | ✅ | ✅ | ## Citation This GUI is powered by [Arcee's MergeKit](https://arxiv.org/abs/2403.13257). If you use it in your research, please cite the following paper: @article{goddard2024arcee, title={Arcee's MergeKit: A Toolkit for Merging Large Language Models}, author={Goddard, Charles and Siriwardhana, Shamane and Ehghaghi, Malikeh and Meyers, Luke and Karpukhin, Vlad and Benedict, Brian and McQuade, Mark and Solawetz, Jacob}, journal={arXiv preprint arXiv:2403.13257}, year={2024} } This Space is heavily inspired by LazyMergeKit by Maxime Labonne (see [Colab](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb)). """ examples = [[str(f)] for f in pathlib.Path("examples").glob("*.yaml")] COMMUNITY_HF_TOKEN = os.getenv("COMMUNITY_HF_TOKEN") def merge_process(yaml_config, hf_token, repo_name, profile_name, logs_queue): runner = LogsViewRunner(logs_queue) if not yaml_config: 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: runner.log(f"Invalid yaml {e}", level="ERROR") return is_community_model = False if not hf_token: if "/" in repo_name and not repo_name.startswith("mergekit-community/"): runner.log(f"Cannot upload merge model to namespace {repo_name.split('/')[0]}: you must provide a valid token.", level="ERROR") return runner.log("No HF token provided. Your merged model will be uploaded to the https://huggingface.co/mergekit-community organization.") is_community_model = True if not COMMUNITY_HF_TOKEN: raise gr.Error("Cannot upload to community org: community token not set by Space owner.") hf_token = COMMUNITY_HF_TOKEN api = huggingface_hub.HfApi(token=hf_token) with tempfile.TemporaryDirectory(ignore_cleanup_errors=True) 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) current_time = time.strftime("%Y-%m-%d %H:%M:%S") runner.log(f"[{current_time}] Merge configuration saved in {config_path}") if not repo_name: runner.log("No repo name provided. Generating a random one.") repo_name = f"{profile_name}/mergekit-{merge_config.merge_method}" if profile_name else f"mergekit-{merge_config.merge_method}" repo_name += "-" + "".join(random.choices(string.ascii_lowercase, k=7)) repo_name = repo_name.replace("/", "-").strip("-") if is_community_model and not repo_name.startswith("mergekit-community/"): repo_name = f"mergekit-community/{repo_name}" try: runner.log(f"Creating repo {repo_name}") repo_url = api.create_repo(repo_name, exist_ok=True) runner.log(f"Repo created: {repo_url}") except Exception as e: runner.log(f"Error creating repo {e}", level="ERROR") return tmp_env = os.environ.copy() tmp_env["HF_HOME"] = f"{tmpdirname}/.cache" full_cli = cli + f" --lora-merge-cache {tmpdirname}/.lora_cache" runner.run_command(full_cli.split(), cwd=merged_path, env=tmp_env) if runner.exit_code != 0: runner.log("Merge failed. Deleting repo as no model is uploaded.", level="ERROR") api.delete_repo(repo_url.repo_id) return runner.log("Model merged successfully. Uploading to HF.") runner.run_python(api.upload_folder, repo_id=repo_url.repo_id, folder_path=merged_path / "merge") current_time = time.strftime("%Y-%m-%d %H:%M:%S") runner.log(f"[{current_time}] Model successfully uploaded to HF: {repo_url.repo_id}") def merge(yaml_config, hf_token, repo_name, profile_name): logs_queue = [] thread = threading.Thread(target=merge_process, args=(yaml_config, hf_token, repo_name, profile_name, logs_queue)) thread.start() while thread.is_alive(): if logs_queue: yield logs_queue[:] logs_queue.clear() time.sleep(0.1) if logs_queue: yield logs_queue 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") repo_name = gr.Textbox(lines=1, label="Repo name", placeholder="Optional") profile_name = gr.Textbox(lines=1, label="Hugging Face Profile Name", placeholder="Enter your HF profile name") button = gr.Button("Merge", variant="primary") logs = LogsView(label="Terminal output") 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, profile_name], outputs=[logs]) @spaces(duration=0) def launch(): demo.launch(share=True) if __name__ == "__main__": launch()