transformers safetensors mixtral text-generation moe frankenmoe merge mergekit lazymergekit cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser LunaticPython161/CyberWitch-7B base_model:cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser base_model:LunaticPython161/CyberWitch-7B license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
---
license: apache-2.0
tags:
- moe
- frankenmoe
- merge
- mergekit
- lazymergekit
- cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser
- LunaticPython161/CyberWitch-7B
base_model:
- cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser
- LunaticPython161/CyberWitch-7B
---

# Lily-MoE-2x7b

Lily-MoE-2x7b is a Mixure of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser](https://huggingface.co/cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser)
* [LunaticPython161/CyberWitch-7B](https://huggingface.co/LunaticPython161/CyberWitch-7B)

## 🧩 Configuration

```yaml
base_model: cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser
gate_mode: hidden # one of "hidden", "cheap_embed", or "random"
dtype: bfloat16 # output dtype (float32, float16, or bfloat16)
experts:
  - source_model: cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser
    positive_prompts:
    - "chat"
    - "assistant"
    - "tell me"
    - "explain"
    - "code"
    - "programming"
  - source_model: LunaticPython161/CyberWitch-7B
    positive_prompts:
    - "solve"
    - "count"
    - "math"
    - "mathematics"
    - "algorithm"
    - "cypher"
    - "cybersecurity"
    - "penetration testing"
    - "red team"
    - "blue team"
    - "hacking"
```

## 💻 Usage

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

from transformers import AutoTokenizer
import transformers
import torch

model = "LunaticPython161/Lily-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"])
```