---
tags:
- merge
- mergekit
- lazymergekit
- abideen/AlphaMonarch-dora
base_model:
- abideen/AlphaMonarch-dora
---

# Spaetzle-v60-7b

This is progressive (mostly dare-ties, but also slerp) merge with the intention of suitable compromise for English and German local tasks.
The performance looks ok so far: e.g. we get in EQ-Bench: Score (v2_de): 65.08 (Parseable: 171.0).

Spaetzle-v60-7b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [abideen/AlphaMonarch-dora](https://huggingface.co/abideen/AlphaMonarch-dora)
* [cstr/Spaetzle-v58-7b](https://huggingface.co/cstr/Spaetzle-v58-7b)


## 🧩 Configuration

```yaml
models:
  - model: cstr/Spaetzle-v58-7b
    # no parameters necessary for base model
  - model: abideen/AlphaMonarch-dora
    parameters:
      density: 0.60
      weight: 0.30
merge_method: dare_ties
base_model: cstr/Spaetzle-v58-7b
parameters:
  int8_mask: true
dtype: bfloat16
random_seed: 0
tokenizer_source: base

```

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "cstr/Spaetzle-v60-7b"
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"])
```