Combining the Best LLM Models - Quick Guide
Combining the Best Models: A Quick Guide to Model Merging
Hey everyone! Recently, I conducted an experiment where I went to the [Hugging Face LLM leaderboard](https://huggingface.co/leaderboard) and combined the top three models. These models used a technique called **model merging**, which allows you to create a better model without any additional training. Since I had already combined the top three models, it became the **Top Model**. The idea for this experiment came from my coworker Malone, so a big thank you to him for the inspiration!
In this blog post, I will guide you through the process of merging models and submitting your final model to the Hugging Face LLM leaderboard. We'll be using the **Merge Kit** library, which does an excellent job of consolidating various merging methods into a single, easy-to-use package. However, I do feel that a bit more documentation would be helpful. For a deeper dive into the merging methods, I recommend checking out Julian's video on the topic. I'll include the link below.
Getting Started
Step 1: Install Required Libraries
First, we need to install the necessary libraries. We'll use `bitsandbytes` and `accelerate` for this process.
```bash
!pip install bitsandbytes accelerate
```
Step 2: Install Merge Kit
Next, we need to install the Merge Kit itself. You can clone the repository and install it using the following commands:
```bash
!git clone https://github.com/your-repo/merge-kit.git
!cd merge-kit && pip install .
```
Step 3: Prepare the Configuration File
We need a `.yml` file to tell Merge Kit which models to merge and how to merge them. For this example, we'll use a basic linear merging method. Here's a sample configuration file:
```yaml
models:
- path: "path/to/first/model"
weight: 1.0
- path: "path/to/second/model"
weight: 1.0
- path: "path/to/third/model"
weight: 1.0
merge_method: "linear"
tokenizer_source: "union"
dtype: "float16"
```
In this configuration, we specify the paths to the three models we want to merge, their weights (which determine their importance), the merging method, the tokenizer source, and the data type. The tokenizer source is set to "union" to ensure that the tokenizers are merged correctly, which is crucial to avoid errors.
You can create this file and name it `linear_merge.yml`, or you can download it directly from the Merge Kit GitHub page.
Step 4: Perform the Model Merging
Now that we have our configuration file, we can perform the model merging. We'll do this in a Python script for ease of use.
```python
import torch
import yaml
from merge_kit import MergeConfiguration, MergeOptions, run_merge
# Define the output path for the merged model
output_path = "models/llama-3s-merged"
# Load the configuration file
merge_config = MergeConfiguration.from_yaml("linear_merge.yml")
# Set up the merge options
options = MergeOptions(
output_path=output_path,
cuda=True,
copy_tokenizer=True,
lazy_load=False,
low_cpu_memory_usage=False
)
# Run the merge
run_merge(merge_config, options)
print("Model merging is complete!")
```
This script will download the models, load them, and merge them using the specified method. It may take some time, so be patient.
Step 5: Test the Merged Model
Once the model is merged, let's test it to ensure it works as expected. We'll use the `Transformers` library to load the model and generate some text.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(output_path)
model = AutoModelForCausalLM.from_pretrained(output_path, torch_dtype=torch.float16).to("cuda")
# Define a prompt
prompt = "Calculate a recursive function that calculates the Fibonacci sequence in Python."
input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to("cuda")
# Generate text
output = model.generate(input_ids, max_length=100)
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)
```
You may encounter a `CUDA out of memory` error. If so, you can use 4-bit quantization to reduce memory usage:
```python
from transformers import BitsAndBytesConfig
# Define a 4-bit quantization configuration
quant_config = BitsAndBytesConfig(load_in_4bit=True)
# Load the model with quantization
model = AutoModelForCausalLM.from_pretrained(output_path, torch_dtype=torch.float16, quantization_config=quant_config).to("cuda")
# Generate text
output = model.generate(input_ids, max_length=100)
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)
```
### Step 6: Submit the Model to the Hugging Face LLM Leaderboard
Finally, we'll submit our merged model to the Hugging Face LLM leaderboard. First, we need to log in to our Hugging Face account:
```python
from huggingface_hub import notebook_login
notebook_login()
```
Follow the prompts to log in and copy your token.
Next, we push the model and tokenizer to our Hugging Face account:
```python
model.push_to_hub("username/llama-3s-merged")
tokenizer.push_to_hub("username/llama-3s-merged")
```
Replace `username` with your Hugging Face username.
Before submitting, ensure that the model is in a submittable format:
```python
from transformers import AutoConfig, AutoModel, AutoTokenizer
config = AutoConfig.from_pretrained("username/llama-3s-merged")
model = AutoModel.from_pretrained("username/llama-3s-merged")
tokenizer = AutoTokenizer.from_pretrained("username/llama-3s-merged")
```
If these commands load without any issues, your model is ready to go. Now, go to the [Hugging Face LLM leaderboard](https://huggingface.co/leaderboard) and submit your model:
1. **Model Name:** `username/llama-3s-merged`
2. **Precision:** `float16`
3. **Model Type:** `base`
Don't forget to set the license to MIT in your model's repository settings before submitting.
Conclusion
That's it! You've successfully merged multiple models and submitted the result to the Hugging Face LLM leaderboard. This process is powerful because it allows you to create better models without additional training. I hope this guide was helpful. If you want to explore more merging methods and configurations, check out the [Merge Kit GitHub repository](https://github.com/your-repo/merge-kit).
Feel free to leave a comment if you have any questions or if you'd like to see more content on this topic. Happy merging! 🚀
---
**Links:**
- [Merge Kit GitHub Repository](https://github.com/your-repo/merge-kit
- [Julian's Video on Model Merging](https://www.youtube.com/watch?v=example)
AND a link to Lazy MergKit hosted by Google's Colab
**Note:** Make sure to replace `your-repo` and `username` with the actual repository and username.
PS....FINALLY
Hyperlink function in Blogger App is working again. I really do WISH that they
woukd add a button to link YouTube videos.
An
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