---
license: other
license_name: microsoft-research-license
license_link: https://huggingface.co/WizardLM/WizardMath-7B-V1.1/resolve/main/LICENSE
tags:
- moe
- merge
- mergekit
- Mistral
- openchat/openchat-3.5-1210
- beowolx/CodeNinja-1.0-OpenChat-7B
- maywell/PiVoT-0.1-Starling-LM-RP
- WizardLM/WizardMath-7B-V1.1
---
# Beyonder-4x7B-v2
This model is a Mixture of Experts (MoE) made with [mergekit](https://github.com/cg123/mergekit) (mixtral branch). It uses the following base models:
* [openchat/openchat-3.5-1210](https://huggingface.co/openchat/openchat-3.5-1210)
* [beowolx/CodeNinja-1.0-OpenChat-7B](https://huggingface.co/beowolx/CodeNinja-1.0-OpenChat-7B)
* [maywell/PiVoT-0.1-Starling-LM-RP](https://huggingface.co/maywell/PiVoT-0.1-Starling-LM-RP)
* [WizardLM/WizardMath-7B-V1.1](https://huggingface.co/WizardLM/WizardMath-7B-V1.1)
The recommended context length is 8k.
## ⚡ Quantized models
Thanks to TheBloke for the quantized models:
* **GGUF**: https://huggingface.co/TheBloke/Beyonder-4x7B-v2-GGUF
* **AWQ**: https://huggingface.co/TheBloke/Beyonder-4x7B-v2-AWQ
* **GPTQ**: https://huggingface.co/TheBloke/Beyonder-4x7B-v2-GPTQ
* **EXL2**: https://huggingface.co/bartowski/Beyonder-4x7B-v2-exl2
## 🏆 Evaluation
Beyonder-4x7B-v2 is competitive with Mixtral-8x7B-Instruct-v0.1 on the Open LLM Leaderboard, while only having 4 experts instead of 8.
It also displays a significant improvement over the individual experts.
It also performs very well compared to other models on Nous benchmark suite. It's almost as good as the best Yi-34B fine-tune, which is a much bigger model: 24.2B parameters + only two experts are selected during inference (so ~12B) vs. 34B param.
| Model |AGIEval|GPT4All|TruthfulQA|Bigbench|Average|
|--------------------------------------------------------------------|------:|------:|---------:|-------:|------:|
|[**Beyonder-4x7B-v2**](https://huggingface.co/shadowml/Beyonder-4x7B-v2)| **45.29**| **75.95**| **60.86**| **46.4**| **57.13**|
|[NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B)| 43.67| 73.24| 55.37| 41.76| 53.51|
|[OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B)| 42.75| 72.99| 52.99| 40.94| 52.42|
|[Nous-Hermes-2-SOLAR-10.7B](https://huggingface.co/NousResearch/Nous-Hermes-2-SOLAR-10.7B)| 47.79| 74.69| 55.92| 44.84| 55.81|
|[Nous-Hermes-2-Yi-34B](https://huggingface.co/NousResearch/Nous-Hermes-2-SOLAR-10.7B)| 50.27| 76.00| 60.34| 46.69| 58.33|
### AGIEval
| Task |Version| Metric |Value| |Stderr|
|------------------------------|------:|--------|----:|---|-----:|
|agieval_aqua_rat | 0|acc |23.62|± | 2.67|
| | |acc_norm|23.62|± | 2.67|
|agieval_logiqa_en | 0|acc |41.47|± | 1.93|
| | |acc_norm|43.01|± | 1.94|
|agieval_lsat_ar | 0|acc |23.04|± | 2.78|
| | |acc_norm|23.48|± | 2.80|
|agieval_lsat_lr | 0|acc |51.57|± | 2.22|
| | |acc_norm|52.94|± | 2.21|
|agieval_lsat_rc | 0|acc |64.31|± | 2.93|
| | |acc_norm|64.68|± | 2.92|
|agieval_sat_en | 0|acc |79.13|± | 2.84|
| | |acc_norm|79.13|± | 2.84|
|agieval_sat_en_without_passage| 0|acc |43.20|± | 3.46|
| | |acc_norm|43.20|± | 3.46|
|agieval_sat_math | 0|acc |34.55|± | 3.21|
| | |acc_norm|32.27|± | 3.16|
### GPT4All
| Task |Version| Metric |Value| |Stderr|
|-------------|------:|--------|----:|---|-----:|
|arc_challenge| 0|acc |61.86|± | 1.42|
| | |acc_norm|64.51|± | 1.40|
|arc_easy | 0|acc |85.06|± | 0.73|
| | |acc_norm|82.45|± | 0.78|
|boolq | 1|acc |88.35|± | 0.56|
|hellaswag | 0|acc |68.04|± | 0.47|
| | |acc_norm|85.12|± | 0.36|
|openbookqa | 0|acc |37.80|± | 2.17|
| | |acc_norm|48.60|± | 2.24|
|piqa | 0|acc |83.08|± | 0.87|
| | |acc_norm|83.95|± | 0.86|
|winogrande | 0|acc |78.69|± | 1.15|
### TruthfulQA
| Task |Version|Metric|Value| |Stderr|
|-------------|------:|------|----:|---|-----:|
|truthfulqa_mc| 1|mc1 |44.55|± | 1.74|
| | |mc2 |60.86|± | 1.57|
### Bigbench
| Task |Version| Metric |Value| |Stderr|
|------------------------------------------------|------:|---------------------|----:|---|-----:|
|bigbench_causal_judgement | 0|multiple_choice_grade|58.95|± | 3.58|
|bigbench_date_understanding | 0|multiple_choice_grade|66.40|± | 2.46|
|bigbench_disambiguation_qa | 0|multiple_choice_grade|48.84|± | 3.12|
|bigbench_geometric_shapes | 0|multiple_choice_grade|22.56|± | 2.21|
| | |exact_str_match |13.37|± | 1.80|
|bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|30.40|± | 2.06|
|bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|20.57|± | 1.53|
|bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|52.00|± | 2.89|
|bigbench_movie_recommendation | 0|multiple_choice_grade|44.40|± | 2.22|
|bigbench_navigate | 0|multiple_choice_grade|52.10|± | 1.58|
|bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|69.75|± | 1.03|
|bigbench_ruin_names | 0|multiple_choice_grade|55.36|± | 2.35|
|bigbench_salient_translation_error_detection | 0|multiple_choice_grade|23.65|± | 1.35|
|bigbench_snarks | 0|multiple_choice_grade|77.35|± | 3.12|
|bigbench_sports_understanding | 0|multiple_choice_grade|73.02|± | 1.41|
|bigbench_temporal_sequences | 0|multiple_choice_grade|46.80|± | 1.58|
|bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|22.08|± | 1.17|
|bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|19.03|± | 0.94|
|bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|52.00|± | 2.89|
## 🧩 Configuration
```yaml
base_model: mlabonne/Marcoro14-7B-slerp
experts:
- source_model: openchat/openchat-3.5-1210
positive_prompts:
- "chat"
- "assistant"
- "tell me"
- "explain"
- source_model: beowolx/CodeNinja-1.0-OpenChat-7B
positive_prompts:
- "code"
- "python"
- "javascript"
- "programming"
- "algorithm"
- source_model: maywell/PiVoT-0.1-Starling-LM-RP
positive_prompts:
- "storywriting"
- "write"
- "scene"
- "story"
- "character"
- source_model: WizardLM/WizardMath-7B-V1.1
positive_prompts:
- "reason"
- "math"
- "mathematics"
- "solve"
- "count"
```
## 💻 Usage
Here's a [notebook](https://colab.research.google.com/drive/1ypy8fEAJe9RkNmNQR1BduOzy2Qn6CnMl#scrollTo=myLRfwjZcIyP) to run this model in 4-bit precision using a free T4 GPU on Google Colab.
```python
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "mlabonne/Beyonder-4x7B-v2"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)
messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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"])
```
Output:
> A Mixture of Experts (ME) is a machine learning technique that combines multiple expert models to make predictions or decisions. Each expert model is specialized in a different aspect of the problem, and their outputs are combined to produce a more accurate and robust solution. This approach allows the model to leverage the strengths of individual experts and compensate for their weaknesses, improving overall performance.