README.md

---
tags:
- merge
- mergekit
- lazymergekit
- allknowingroger/LimyQstar-7B-slerp
- allknowingroger/JaskierMistral-7B-slerp
- allknowingroger/LimmyAutomerge-7B-slerp
base_model:
- allknowingroger/LimyQstar-7B-slerp
- allknowingroger/JaskierMistral-7B-slerp
- allknowingroger/LimmyAutomerge-7B-slerp
license: apache-2.0
---

# TripleMerge2-7B-Ties

TripleMerge2-7B-Ties is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [allknowingroger/LimyQstar-7B-slerp](https://huggingface.co/allknowingroger/LimyQstar-7B-slerp)
* [allknowingroger/JaskierMistral-7B-slerp](https://huggingface.co/allknowingroger/JaskierMistral-7B-slerp)
* [allknowingroger/LimmyAutomerge-7B-slerp](https://huggingface.co/allknowingroger/LimmyAutomerge-7B-slerp)

## 🧩 Configuration

```yaml
models:
  - model: allknowingroger/LimyQstar-7B-slerp
    parameters:
      density: [1, 0.7, 0.1] # density gradient
      weight: 1.0
  - model: allknowingroger/JaskierMistral-7B-slerp
    parameters:
      density: 0.5
      weight: [0, 0.3, 0.7, 1] # weight gradient
  - model: allknowingroger/LimmyAutomerge-7B-slerp
    parameters:
      density: 0.33
      weight:
        - filter: mlp
          value: 0.5
        - value: 0
merge_method: ties
base_model: allknowingroger/LimyQstar-7B-slerp
parameters:
  normalize: true
  int8_mask: true
dtype: float16
```

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "allknowingroger/TripleMerge2-7B-Ties"
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"])
```