README.md

---
tags:
- merge
- mergekit
- lazymergekit
- flemmingmiguel/NeuDist-Ro-7B
- Blizado/discolm-mfto-7b-german-v0.1
- ResplendentAI/Flora_DPO_7B
base_model:
- flemmingmiguel/NeuDist-Ro-7B
- Blizado/discolm-mfto-7b-german-v0.1
- ResplendentAI/Flora_DPO_7B
license: cc-by-sa-4.0
---

# Spaetzle-v12-7b

Spaetzle-v12-7b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [flemmingmiguel/NeuDist-Ro-7B](https://huggingface.co/flemmingmiguel/NeuDist-Ro-7B)
* [Blizado/discolm-mfto-7b-german-v0.1](https://huggingface.co/Blizado/discolm-mfto-7b-german-v0.1)
* [ResplendentAI/Flora_DPO_7B](https://huggingface.co/ResplendentAI/Flora_DPO_7B)
* on the basis of [mayflowergmbh/Wiedervereinigung-7b-dpo-laser](https://huggingface.co/mayflowergmbh/Wiedervereinigung-7b-dpo-laser) 

As expected, this is a little bit worse in general English tasks over Spaetzle-v12-7b, but a tiny little bit better on German tasks, at least some: e.g. it reaches an EQ-Bench (de)
score of 64.81, but only

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |69.36|
|AI2 Reasoning Challenge (25-Shot)|65.96|
|HellaSwag (10-Shot)              |86.16|
|MMLU (5-Shot)                    |63.48|
|TruthfulQA (0-shot)              |57.84|
|Winogrande (5-shot)              |80.03|
|GSM8k (5-shot)                   |62.70|


## 🧩 Configuration

```yaml
models:
  - model: mayflowergmbh/Wiedervereinigung-7b-dpo-laser
    # no parameters necessary for base model
  - model: flemmingmiguel/NeuDist-Ro-7B
    parameters:
      density: 0.60
      weight: 0.30
  - model: Blizado/discolm-mfto-7b-german-v0.1
    parameters:
      density: 0.65
      weight: 0.40
  - model: ResplendentAI/Flora_DPO_7B
    parameters:
      density: 0.6
      weight: 0.3
merge_method: dare_ties
base_model: mayflowergmbh/Wiedervereinigung-7b-dpo-laser
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-v12-7b"
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"])
```