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---
license: apache-2.0
tags:
- moe
- frankenmoe
- merge
- mergekit
- lazymergekit
- mlabonne/AlphaMonarch-7B
- bardsai/jaskier-7b-dpo-v5.6
base_model:
- mlabonne/AlphaMonarch-7B
- bardsai/jaskier-7b-dpo-v5.6
---
# ExpertRamonda-7Bx2_MoE
ExpertRamonda-7Bx2_MoE is a Mixure of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [mlabonne/AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B)
* [bardsai/jaskier-7b-dpo-v5.6](https://huggingface.co/bardsai/jaskier-7b-dpo-v5.6)
# 🏆 Benchmarks
### Open LLM Leaderboard
| Model | Average | ARC_easy | HellaSwag | MMLU | TruthfulQA_mc2 | Winogrande | GSM8K |
|------------------------|--------:|-----:|----------:|-----:|-----------:|-----------:|------:|
| mayacinka/ExpertRamonda-7Bx2_MoE | 78.10 | 86.87 | 87.51| 61.63 | 78.02 | 81.85 | 72.71|
### MMLU
| Groups |Version|Filter|n-shot|Metric|Value | |Stderr|
|------------------|-------|------|------|------|-----:|---|-----:|
|mmlu |N/A |none | 0|acc |0.6163|± |0.0039|
| - humanities |N/A |none |None |acc |0.5719|± |0.0067|
| - other |N/A |none |None |acc |0.6936|± |0.0079|
| - social_sciences|N/A |none |None |acc |0.7121|± |0.0080|
| - stem |N/A |none |None |acc |0.5128|± |0.0085|
## 🧩 Configuration
```yaml
base_model: mlabonne/AlphaMonarch-7B
gate_mode: hidden
dtype: bfloat16
experts_per_token: 2
experts:
- source_model: mlabonne/AlphaMonarch-7B
positive_prompts:
- "You excel at reasoning skills. For every prompt you think of an answer from 3 different angles"
## (optional)
# negative_prompts:
# - "This is a prompt expert_model_1 should not be used for"
- source_model: bardsai/jaskier-7b-dpo-v5.6
positive_prompts:
- "You excel at logic and reasoning skills. Reply in a straightforward and concise way"
```
## 💻 Usage
```python
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "mayacinka/ExpertRamonda-7Bx2_MoE"
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"])
``` |