license: cc-by-nc-4.0
tags:
- moe
- merge
- mergekit
base_model:
- mlabonne/AlphaMonarch-7B
- beowolx/CodeNinja-1.0-OpenChat-7B
- SanjiWatsuki/Kunoichi-DPO-v2-7B
- mlabonne/NeuralDaredevil-7B
Beyonder-4x7B-v3-random-lora
The idea was very simple. If heuristic methods for determining gate parameters in mergeiit-based MoE models can work well, then perhaps we could obtain a better performing model by fine-tuning only the gate parameters.
This model is an attempt at testing that idea. Unfortunately, the performance degraded slightly, but I am sharing it as an experimental result.
Model Details
First, I created an MoE model using mergekit with gate_mode=random and the following four models:
- mlabonne/AlphaMonarch-7B
- beowolx/CodeNinja-1.0-OpenChat-7B
- SanjiWatsuki/Kunoichi-DPO-v2-7B
- mlabonne/NeuralDaredevil-7
Then, I used LoRA to fine-tune only the gate parameters by specifying "gate" in target_modules. The data used for fine-tuning is as follows. I used the Mistral prompt format.
- 5000 random samples from llm-jp/oasst1-21k-en
- 5000 random samples from databricks/databricks-dolly-15k
- 5000 random samples from hieunguyenminh/roleplay
- 5000 random samples from meta-math/MetaMathQA
- 5000 random samples from m-a-p/CodeFeedback-Filtered-Instruction
The training was conducted on runpod using 4xA6000 GPUs. The main training parameters are as follows:
- lora_r: 128
- lora_alpha: 256
- lora_dropout: 0.05
- lora_target_modules: "gate"
- learning_rate: 3e-4
- num_train_epochs: 5
- batch_size: 64
- max_seq_length: 2048
Evaluation
The evaluation results show a slight degradation in performance. Apart from the possibility that this approach may not be effective, other potential causes could be issues with the dataset, training parameters, training setup (such as prompt formatting), and so on.
Nous (LLM AutoEval)
| Model | Average | AGIEval | GPT4All | TruthfulQA | Bigbench |
|---|---|---|---|---|---|
| mlabonne/AlphaMonarch-7B 📄 | 62.74 | 45.37 | 77.01 | 78.39 | 50.2 |
| mlabonne/Beyonder-4x7B-v3 📄 | 61.91 | 45.85 | 76.67 | 74.98 | 50.12 |
| Aratako/Beyonder-4x7B-v3-random-lora 📄 | 60.29 | 45.82 | 76.69 | 69.94 | 48.72 |
| mlabonne/NeuralDaredevil-7B 📄 | 59.39 | 45.23 | 76.2 | 67.61 | 48.52 |
| SanjiWatsuki/Kunoichi-DPO-v2-7B 📄 | 58.29 | 44.79 | 75.05 | 65.68 | 47.65 |
| mlabonne/Beyonder-4x7B-v2 📄 | 57.13 | 45.29 | 75.95 | 60.86 | 46.4 |
| beowolx/CodeNinja-1.0-OpenChat-7B 📄 | 50.35 | 39.98 | 71.77 | 48.73 | 40.92 |
MT-Bench
1-turn
| Model | Coding | Extraction | Humanities | Math | Reasoning | Roleplay | STEM | Writing | avg_score |
|---|---|---|---|---|---|---|---|---|---|
| mlabonne/Beyonder-4x7B-v3 | 6.7 | 8.3 | 9.7 | 6.7 | 6.3 | 9.3 | 9.7 | 10.0 | 8.33750 |
| Aratako/Beyonder-4x7B-v3-random-lora | 6.6 | 8.2 | 9.6 | 6.3 | 6.4 | 8.7 | 9.4 | 9.5 | 8.08750 |
| mistralai/Mixtral-8x7B-Instruct-v0.1 | 5.3 | 8.5 | 9.9 | 6.8 | 6.0 | 9.1 | 9.55 | 8.9 | 8.00625 |
2-turn
| Model | Coding | Extraction | Humanities | Math | Reasoning | Roleplay | STEM | Writing | avg_score |
|---|---|---|---|---|---|---|---|---|---|
| mlabonne/Beyonder-4x7B-v3 | 5.4 | 7.6 | 10.0 | 3.5 | 5.5 | 9.0 | 9.6 | 9.1 | 7.46250 |
| Aratako/Beyonder-4x7B-v3-random-lora | 5.1 | 8.1 | 9.9 | 4.1 | 3.7 | 8.55 | 9.0 | 7.7 | 7.01875 |
| mistralai/Mixtral-8x7B-Instruct-v0.1 | 4.1 | 8.4 | 9.8 | 4.7 | 5.6 | 9.0 | 9.2 | 9.5 | 7.53750 |
