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---
license: apache-2.0
tags:
- moe
- frankenmoe
- merge
- mergekit
- lazymergekit
- GritLM/GritLM-7B
- argilla/notus-7b-v1
base_model:
- GritLM/GritLM-7B
- argilla/notus-7b-v1
---
# GK-inv-MoE-0.1
GK-inv-MoE-0.1 is a Mixture of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [GritLM/GritLM-7B](https://huggingface.co/GritLM/GritLM-7B)
* [argilla/notus-7b-v1](https://huggingface.co/argilla/notus-7b-v1)
## 🧩 Configuration
```yaml
base_model: GritLM/GritLM-7B
experts:
- source_model: GritLM/GritLM-7B
positive_prompts:
- "chat"
- "assistant"
- "tell me"
- "explain"
- "I want"
- "reason"
- "math"
- "mathematics"
- "solve"
- "count"
- source_model: argilla/notus-7b-v1
positive_prompts:
- "code"
- "VB.NET"
- "vb.net"
- "programming"
- "algorithm"
- "develop"
```
## 💻 Usage
```python
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "powermove72/GK-inv-MoE-0.1"
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"])
``` |