README.md

---
tags:
- merge
- mergekit
- lazymergekit
- paulml/OGNO-7B
- nlpguy/AlloyIngot
- mlabonne/Monarch-7B
base_model:
- paulml/OGNO-7B
- nlpguy/AlloyIngot
- mlabonne/Monarch-7B
---

# RandomMergeSparsifyWEIGHTED-7B-DARETIES

RandomMergeSparsifyWEIGHTED-7B-DARETIES is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [paulml/OGNO-7B](https://huggingface.co./paulml/OGNO-7B)
* [nlpguy/AlloyIngot](https://huggingface.co./nlpguy/AlloyIngot)
* [mlabonne/Monarch-7B](https://huggingface.co./mlabonne/Monarch-7B)

## 🧩 Configuration

```yaml
models:
  - model: paulml/OGNO-7B
    parameters:
      density: [1, 0.7, 0.3]
      weight: [0, 0.3, 0.7, 1]
  - model: nlpguy/AlloyIngot
    parameters:
      density: [1, 0.7, 0.1]
      weight: [0, 0.25, 0.5, 1]
  - model: mlabonne/Monarch-7B
    parameters:
      weight: 0.33
      density: 0.33
merge_method: dare_ties
base_model: mlabonne/Monarch-7B
parameters:
  int8_mask: true
  normalize: true
dtype: bfloat16
```

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "jsfs11/RandomMergeSparsifyWEIGHTED-7B-DARETIES"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

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"])
```