README.md

---
license: apache-2.0
tags:
- moe
- frankenmoe
- merge
- mergekit
- lazymergekit
- fblgit/UNA-TheBeagle-7b-v1
- berkeley-nest/Starling-LM-7B-alpha
base_model:
- fblgit/UNA-TheBeagle-7b-v1
- berkeley-nest/Starling-LM-7B-alpha
---

# megatron_1.1_MoE_2x7B

megatron_1.1_MoE_2x7B is a Mixure of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [fblgit/UNA-TheBeagle-7b-v1](https://huggingface.co/fblgit/UNA-TheBeagle-7b-v1)
* [berkeley-nest/Starling-LM-7B-alpha](https://huggingface.co/berkeley-nest/Starling-LM-7B-alpha)

## 🧩 Configuration

```yaml
base_model: openchat/openchat-3.5-0106
gate_mode: hidden
dtype: bfloat16
experts:
  - source_model: fblgit/UNA-TheBeagle-7b-v1
    positive_prompts:
    - "Mathematics"
    - "Physics"
    negative_prompts:
    - "History"
    - "Philosophy"
  - source_model: berkeley-nest/Starling-LM-7B-alpha
    positive_prompts:
    - "Earth Sciences (Geology, Meteorology, Oceanography)"
    - "Environmental Science"
    negative_prompts:
    - "Education"
    - "Law"

```

## 💻 Usage

```python
!pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Eurdem/megatron_1.1_MoE_2x7B"

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"])
```