README.md

---
license: apache-2.0
tags:
- moe
- frankenmoe
- merge
- mergekit
- lazymergekit
- NeuralNovel/Valor-7B-v0.1
- Toten5/Marcoroni-neural-chat-7B-v1
base_model:
- NeuralNovel/Valor-7B-v0.1
- Toten5/Marcoroni-neural-chat-7B-v1
---

# Valor_Macaroni_moe

Valor_Macaroni_moe is a Mixure of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [NeuralNovel/Valor-7B-v0.1](https://huggingface.co/NeuralNovel/Valor-7B-v0.1)
* [Toten5/Marcoroni-neural-chat-7B-v1](https://huggingface.co/Toten5/Marcoroni-neural-chat-7B-v1)

## 🧩 Configuration

```yaml
base_model: NeuralNovel/Valor-7B-v0.1
gate_mode: cheap_embed
experts:
  - source_model: NeuralNovel/Valor-7B-v0.1
    positive_prompts: ["What should I do if lost my mobile phone"]
  - source_model: Toten5/Marcoroni-neural-chat-7B-v1
    positive_prompts: ["I have 3 apples. I lost 2 out of it. After that my father gave me another 3. How many do I have now?"]
```

## 💻 Usage

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

from transformers import AutoTokenizer
import transformers
import torch

model = "Vasanth/Valor_Macaroni_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"])
```