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---
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
model-index:
- name: Beyonder-4x7B-random-lora
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 71.25
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Aratako/Beyonder-4x7B-random-lora
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 87.4
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Aratako/Beyonder-4x7B-random-lora
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 64.78
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Aratako/Beyonder-4x7B-random-lora
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 70.49
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Aratako/Beyonder-4x7B-random-lora
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 82.16
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Aratako/Beyonder-4x7B-random-lora
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 67.4
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Aratako/Beyonder-4x7B-random-lora
name: Open LLM Leaderboard
---
# Beyonder-4x7B-v3-random-lora
The idea was very simple. If heuristic methods for determining gate parameters in mergekit-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 (same as [mlabonne/Beyonder-4x7B-v3](https://huggingface.co/mlabonne/Beyonder-4x7B-v3)):
- [mlabonne/AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B)
- [beowolx/CodeNinja-1.0-OpenChat-7B](https://huggingface.co/beowolx/CodeNinja-1.0-OpenChat-7B)
- [SanjiWatsuki/Kunoichi-DPO-v2-7B](https://huggingface.co/SanjiWatsuki/Kunoichi-DPO-v2-7B)
- [mlabonne/NeuralDaredevil-7](https://huggingface.co/mlabonne/NeuralDaredevil-7B)
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](https://huggingface.co/datasets/llm-jp/oasst1-21k-en)
- 5000 random samples from [databricks/databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k)
- 5000 random samples from [hieunguyenminh/roleplay](https://huggingface.co/datasets/hieunguyenminh/roleplay)
- 5000 random samples from [meta-math/MetaMathQA](https://huggingface.co/datasets/meta-math/MetaMathQA)
- 5000 random samples from [m-a-p/CodeFeedback-Filtered-Instruction](https://huggingface.co/datasets/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](https://github.com/mlabonne/llm-autoeval))
| Model | Average | AGIEval | GPT4All | TruthfulQA | Bigbench |
|---|---:|---:|---:|---:|---:|
| [mlabonne/AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B) [📄](https://gist.github.com/mlabonne/1d33c86824b3a11d2308e36db1ba41c1) | 62.74 | 45.37 | 77.01 | 78.39 | 50.2 |
| [mlabonne/Beyonder-4x7B-v3](https://huggingface.co/mlabonne/Beyonder-4x7B-v3) [📄](https://gist.github.com/mlabonne/3740020807e559f7057c32e85ce42d92) | 61.91 | 45.85 | 76.67 | 74.98 | 50.12 |
| [**Aratako/Beyonder-4x7B-v3-random-lora**](https://huggingface.co/Aratako/Beyonder-4x7B-v3-random-lora) [📄](https://gist.github.com/Aratako/f86144312989d69f92c64ea4f25a8bb6) | **60.29** | **45.82** | **76.69** | **69.94** | **48.72** |
| [mlabonne/NeuralDaredevil-7B](https://huggingface.co/mlabonne/NeuralDaredevil-7B) [📄](https://gist.github.com/mlabonne/cbeb077d1df71cb81c78f742f19f4155) | 59.39 | 45.23 | 76.2 | 67.61 | 48.52 |
| [SanjiWatsuki/Kunoichi-DPO-v2-7B](https://huggingface.co/SanjiWatsuki/Kunoichi-DPO-v2-7B) [📄](https://gist.github.com/mlabonne/895ff5171e998abfdf2a41a4f9c84450) | 58.29 | 44.79 | 75.05 | 65.68 | 47.65 |
| [mlabonne/Beyonder-4x7B-v2](https://huggingface.co/mlabonne/Beyonder-4x7B-v2) [📄](https://gist.github.com/mlabonne/f73baa140a510a676242f8a4496d05ca) | 57.13 | 45.29 | 75.95 | 60.86 | 46.4 |
| [beowolx/CodeNinja-1.0-OpenChat-7B](https://huggingface.co/beowolx/CodeNinja-1.0-OpenChat-7B) [📄](https://gist.github.com/mlabonne/08b5280c221fbd7f98eb27561ae902a3) | 50.35 | 39.98 | 71.77 | 48.73 | 40.92 |
### [MT-Bench](https://github.com/lm-sys/FastChat/tree/main/fastchat/llm_judge)
**1-turn**
|Model|Coding|Extraction|Humanities|Math|Reasoning|Roleplay|STEM|Writing|avg_score|
|---|---|---|---|---|---|---|---|---|---|
| [mlabonne/Beyonder-4x7B-v3](https://huggingface.co/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**](https://huggingface.co/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](https://huggingface.co/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](https://huggingface.co/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**](https://huggingface.co/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](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) | 4.1 | 8.4 | 9.8 | 4.7 | 5.6 | 9.0 | 9.2 | 9.5 | 7.53750 |
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Aratako__Beyonder-4x7B-random-lora)
| Metric |Value|
|---------------------------------|----:|
|Avg. |73.91|
|AI2 Reasoning Challenge (25-Shot)|71.25|
|HellaSwag (10-Shot) |87.40|
|MMLU (5-Shot) |64.78|
|TruthfulQA (0-shot) |70.49|
|Winogrande (5-shot) |82.16|
|GSM8k (5-shot) |67.40|
|