---
license: apache-2.0
tags:
- moe
- merge
- mergekit
- lazymergekit
- mlabonne/NeuralBeagle14-7B
- mlabonne/NeuralDaredevil-7B
- text-generation-inference
- Text Generation
---

---
**This is a repository of GGUF Quants for DareBeagel-2x7B**
---

Original Model Available Here: https://huggingface.co/shadowml/DareBeagel-2x7B

**Available Quants**

* F16
* Q8_0
* Q6_K
* Q5_0
* Q5_K_M
* Q5_K_S
* Q4_0
* Q4_K_M
* Q4_K_S
* Q3_K_M
* Q3_K_S
* Q2_K

# Beyonder-2x7B-v2

Beyonder-2x7B-v2 is a Mixure of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [mlabonne/NeuralBeagle14-7B](https://huggingface.co/mlabonne/NeuralBeagle14-7B)
* [mlabonne/NeuralDaredevil-7B](https://huggingface.co/mlabonne/NeuralDaredevil-7B)

## 🧩 Configuration

```yaml
base_model: mlabonne/NeuralBeagle14-7B
gate_mode: random
experts:
  - source_model: mlabonne/NeuralBeagle14-7B
    positive_prompts: [""]
  - source_model: mlabonne/NeuralDaredevil-7B
    positive_prompts: [""]
```

## 💻 Usage

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

from transformers import AutoTokenizer
import transformers
import torch

model = "shadowml/Beyonder-2x7B-v2"

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"])
```