app.py

import os
import time
import random
import yaml
import subprocess

import runpod
# import gradio as gr
import pandas as pd
from jinja2 import Template
from huggingface_hub import ModelCard, ModelCardData, HfApi, repo_info
from huggingface_hub.utils import RepositoryNotFoundError

# Set environment variables
HF_TOKEN = os.environ.get("HF_TOKEN")
runpod.api_key = os.environ.get("RUNPOD_TOKEN")

# Parameters
USERNAME = 'automerger'
N_ROWS = 20
WAIT_TIME = 3600


def create_dataset() -> bool:
    """
    Use Scrape Open LLM Leaderboard to create a CSV dataset.
    """
    command = ["python3", "scrape-open-llm-leaderboard/main.py", "-csv"]

    try:
        result = subprocess.run(command, check=True, stdout=subprocess.PIPE,
                                stderr=subprocess.PIPE, text=True)
        print(f"scrape-open-llm-leaderboard: {result.stdout}")
        return True
    except subprocess.CalledProcessError as e:
        print(f"scrape-open-llm-leaderboard: {e.stderr}")
        return False


def merge_models() -> None:
    """
    Use mergekit to create a merge.
    """
    command = ["mergekit-yaml", "config.yaml", "merge", "--copy-tokenizer"]

    try:
        result = subprocess.run(command, check=True, stdout=subprocess.PIPE,
                                stderr=subprocess.PIPE, text=True)
        print(f"mergekit: {result.stdout}")
    except subprocess.CalledProcessError as e:
        print(f"mergekit: {e.stderr}")


def make_df(file_path: str, n_rows: int) -> pd.DataFrame:
    """
    Create a filtered dataset from the Open LLM Leaderboard.
    """
    columns = ["Available on the hub", "Model sha", "T", "Type", "Precision",
              "Architecture", "Weight type", "Hub ❤️", "Flagged", "MoE"]
    ds = pd.read_csv(file_path)
    df = (
          ds[
            (ds["#Params (B)"] == 7.24) &
            (ds["Available on the hub"] == True) &
            (ds["Flagged"] == False) &
            (ds["MoE"] == False) &
            (ds["Weight type"] == "Original")
          ]
          .drop(columns=columns)
          .drop_duplicates(subset=["Model"])
          .iloc[:n_rows]
      )
    return df


def repo_exists(repo_id: str) -> bool:
    try:
        repo_info(repo_id)
        return True
    except RepositoryNotFoundError:
        return False


def get_name(models: list[pd.Series], username: str, version=0) -> str:
    model_name = models[0]["Model"].split("/")[-1].split("-")[0].capitalize() \
                 + models[1]["Model"].split("/")[-1].split("-")[0].capitalize() \
                 + "-7B"
    if version > 0:
        model_name = model_name.split("-")[0] + f"-v{version}-7B"

    if repo_exists(f"{username}/{model_name}"):
        get_name(models, username, version+1)

    return model_name


def get_license(models: list[pd.Series]) -> str:
    license1 = models[0]["Hub License"]
    license2 = models[1]["Hub License"]
    license = "cc-by-nc-4.0"

    if license1 == "cc-by-nc-4.0" or license2 == "cc-by-nc-4.0":
        license = "cc-by-nc-4.0"
    elif license1 == "apache-2.0" or license2 == "apache-2.0":
        license = "apache-2.0"
    elif license1 == "MIT" and license2 == "MIT":
        license = "MIT"
    return license


def create_config(models: list[pd.Series]) -> str:
    slerp_config = f"""
slices:
  - sources:
      - model: {models[0]["Model"]}
        layer_range: [0, 32]
      - model: {models[1]["Model"]}
        layer_range: [0, 32]
merge_method: slerp
base_model: {models[0]["Model"]}
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: bfloat16
random_seed: 0
    """
    dare_config = f"""
models:
- model: {models[0]["Model"]}
  # No parameters necessary for base model
- model: {models[1]["Model"]}
  parameters:
    density: 0.53
    weight: 0.6
merge_method: dare_ties
base_model: {models[0]["Model"]}
parameters:
int8_mask: true
dtype: bfloat16
random_seed: 0
    """
    yaml_config = random.choices([slerp_config, dare_config], weights=[0.4, 0.6], k=1)[0]

    with open('config.yaml', 'w', encoding="utf-8") as f:
        f.write(yaml_config)

    return yaml_config


def create_model_card(yaml_config: str, model_name: str, username: str, license: str) -> None:
    template_text = """
---
license: {{ license }}
base_model:
{%- for model in models %}
  - {{ model }}
{%- endfor %}
tags:
- merge
- mergekit
- lazymergekit
---

# {{ model_name }}

{{ model_name }} is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration.

{%- for model in models %}
* [{{ model }}](https://huggingface.co/{{ model }})
{%- endfor %}

## 🧩 Configuration

```yaml
{{- yaml_config -}}
```

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "{{ username }}/{{ model_name }}"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
    """

    # Create a Jinja template object
    jinja_template = Template(template_text.strip())

    # Get list of models from config
    data = yaml.safe_load(yaml_config)
    if "models" in data:
        models = [data["models"][i]["model"] for i in range(len(data["models"])) if "parameters" in data["models"][i]]
    elif "parameters" in data:
        models = [data["slices"][0]["sources"][i]["model"] for i in range(len(data["slices"][0]["sources"]))]
    elif "slices" in data:
        models = [data["slices"][i]["sources"][0]["model"] for i in range(len(data["slices"]))]
    else:
        raise Exception("No models or slices found in yaml config")

    # Fill the template
    content = jinja_template.render(
        model_name=model_name,
        models=models,
        yaml_config=yaml_config,
        username=username,
        license=license
    )

    # Save the model card
    card = ModelCard(content)
    card.save('merge/README.md')


def upload_model(api: HfApi, username: str, model_name: str) -> None:
    api.create_repo(
        repo_id=f"{username}/{model_name}",
        repo_type="model",
        exist_ok=True,
    )
    api.upload_folder(
        repo_id=f"{username}/{model_name}",
        folder_path="merge",
    )


def create_pod(model_name: str, username: str, n=10, wait_seconds=10):
    for attempt in range(n):
        try:
            pod = runpod.create_pod(
                name=f"Automerge {model_name} on Nous",
                image_name="runpod/pytorch:2.0.1-py3.10-cuda11.8.0-devel-ubuntu22.04",
                gpu_type_id="NVIDIA GeForce RTX 3090",
                cloud_type="COMMUNITY",
                gpu_count=1,
                volume_in_gb=0,
                container_disk_in_gb=50,
                template_id="au6nz6emhk",
                env={
                    "BENCHMARK": "nous",
                    "MODEL_ID": f"{username}/{model_name}",
                    "REPO": "https://github.com/mlabonne/llm-autoeval.git",
                    "TRUST_REMOTE_CODE": False,
                    "DEBUG": False,
                    "GITHUB_API_TOKEN": os.environ["GITHUB_TOKEN"],
                }
            )
            print("Pod creation succeeded.")
            return pod
        except Exception as e:
            print(f"Attempt {attempt + 1} failed with error: {e}")
            if attempt < n - 1:
                print(f"Waiting {wait_seconds} seconds before retrying...")
                time.sleep(wait_seconds)
            else:
                print("All attempts failed. Giving up.")
                raise

def merge_loop():
    # Start HF API
    api = HfApi(token=HF_TOKEN)

    # Create dataset (proceed only if successful)
    if not create_dataset():
        print("Failed to create dataset. Skipping merge loop.")
        return

    df = make_df("open-llm-leaderboard.csv", N_ROWS)

    # Sample two models
    sample = df.sample(n=2)
    models = [sample.iloc[i] for i in range(2)]

    # Get model name
    model_name = get_name(models, USERNAME, version=0)
    print(model_name)

    # Get model license
    license = get_license(models)
    print(license)

    # Merge configs
    yaml_config = create_config(models)
    print(yaml_config)

    # Merge models
    merge_models()

    # Create model card
    create_model_card(yaml_config, model_name, USERNAME, license)

    # Upload model
    upload_model(api, USERNAME, model_name)

    # Evaluate model on Runpod
    create_pod(model_name, USERNAME)

command = ["git", "clone", "-q", "https://github.com/Weyaxi/scrape-open-llm-leaderboard"]
subprocess.run(command, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)

command = ["pip", "install", "-r", "scrape-open-llm-leaderboard/requirements.txt"]
subprocess.run(command, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
  
command = ["git", "clone", "https://github.com/arcee-ai/mergekit.git"]
subprocess.run(command, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)

command = ["pip", "install", "-e", "mergekit"]
subprocess.run(command, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)

# Gradio interface
title = """
<div align="center">
  <p style="font-size: 36px;">♾️ AutoMerger</p>
  <p style="font-size: 20px;">📝 <a href="https://medium.com/towards-data-science/merge-large-language-models-with-mergekit-2118fb392b54">Model merging</a> • 💻 <a href="https://github.com/arcee-ai/mergekit">Mergekit</a> • 🐦 <a href="https://twitter.com/maximelabonne">Follow me on X</a></p>
  <p><em>AutoMerger selects two 7B models on top of the Open LLM Leaderboard, combine them with a merge technique, and evaluate the resulting model.</em></p>
</div>
"""
# with gr.Blocks() as demo:
#     gr.Markdown(title)
# demo.launch().launch(server_name="0.0.0.0")

print("Start AutoMerger...")

# Main loop
while True:
    merge_loop()
    time.sleep(WAIT_TIME)