---
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 [cstr/spaetzle-v8-7b](https://huggingface.co/cstr/spaetzle-v8-7b), 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|


|                            Model                             |AGIEval|GPT4All|TruthfulQA|Bigbench|Average|
|--------------------------------------------------------------|------:|------:|---------:|-------:|------:|
|[Spaetzle-v12-7b](https://huggingface.co/cstr/Spaetzle-v12-7b)|  42.64|   74.3|     58.44|   44.44|  54.95|

### AGIEval
|             Task             |Version| Metric |Value|   |Stderr|
|------------------------------|------:|--------|----:|---|-----:|
|agieval_aqua_rat              |      0|acc     |24.02|±  |  2.69|
|                              |       |acc_norm|21.65|±  |  2.59|
|agieval_logiqa_en             |      0|acc     |36.10|±  |  1.88|
|                              |       |acc_norm|37.63|±  |  1.90|
|agieval_lsat_ar               |      0|acc     |24.35|±  |  2.84|
|                              |       |acc_norm|23.04|±  |  2.78|
|agieval_lsat_lr               |      0|acc     |48.82|±  |  2.22|
|                              |       |acc_norm|47.25|±  |  2.21|
|agieval_lsat_rc               |      0|acc     |60.59|±  |  2.98|
|                              |       |acc_norm|57.99|±  |  3.01|
|agieval_sat_en                |      0|acc     |76.21|±  |  2.97|
|                              |       |acc_norm|74.76|±  |  3.03|
|agieval_sat_en_without_passage|      0|acc     |46.60|±  |  3.48|
|                              |       |acc_norm|45.63|±  |  3.48|
|agieval_sat_math              |      0|acc     |37.27|±  |  3.27|
|                              |       |acc_norm|33.18|±  |  3.18|

Average: 42.64%

### GPT4All
|    Task     |Version| Metric |Value|   |Stderr|
|-------------|------:|--------|----:|---|-----:|
|arc_challenge|      0|acc     |59.13|±  |  1.44|
|             |       |acc_norm|61.26|±  |  1.42|
|arc_easy     |      0|acc     |83.67|±  |  0.76|
|             |       |acc_norm|80.89|±  |  0.81|
|boolq        |      1|acc     |87.83|±  |  0.57|
|hellaswag    |      0|acc     |66.45|±  |  0.47|
|             |       |acc_norm|84.63|±  |  0.36|
|openbookqa   |      0|acc     |37.40|±  |  2.17|
|             |       |acc_norm|45.80|±  |  2.23|
|piqa         |      0|acc     |82.15|±  |  0.89|
|             |       |acc_norm|83.13|±  |  0.87|
|winogrande   |      0|acc     |76.56|±  |  1.19|

Average: 74.3%

### TruthfulQA
|    Task     |Version|Metric|Value|   |Stderr|
|-------------|------:|------|----:|---|-----:|
|truthfulqa_mc|      1|mc1   |42.59|±  |  1.73|
|             |       |mc2   |58.44|±  |  1.58|

Average: 58.44%

### Bigbench
|                      Task                      |Version|       Metric        |Value|   |Stderr|
|------------------------------------------------|------:|---------------------|----:|---|-----:|
|bigbench_causal_judgement                       |      0|multiple_choice_grade|55.26|±  |  3.62|
|bigbench_date_understanding                     |      0|multiple_choice_grade|64.77|±  |  2.49|
|bigbench_disambiguation_qa                      |      0|multiple_choice_grade|37.60|±  |  3.02|
|bigbench_geometric_shapes                       |      0|multiple_choice_grade|32.31|±  |  2.47|
|                                                |       |exact_str_match      |21.45|±  |  2.17|
|bigbench_logical_deduction_five_objects         |      0|multiple_choice_grade|31.00|±  |  2.07|
|bigbench_logical_deduction_seven_objects        |      0|multiple_choice_grade|22.43|±  |  1.58|
|bigbench_logical_deduction_three_objects        |      0|multiple_choice_grade|53.00|±  |  2.89|
|bigbench_movie_recommendation                   |      0|multiple_choice_grade|40.40|±  |  2.20|
|bigbench_navigate                               |      0|multiple_choice_grade|51.30|±  |  1.58|
|bigbench_reasoning_about_colored_objects        |      0|multiple_choice_grade|68.50|±  |  1.04|
|bigbench_ruin_names                             |      0|multiple_choice_grade|48.66|±  |  2.36|
|bigbench_salient_translation_error_detection    |      0|multiple_choice_grade|30.36|±  |  1.46|
|bigbench_snarks                                 |      0|multiple_choice_grade|70.17|±  |  3.41|
|bigbench_sports_understanding                   |      0|multiple_choice_grade|70.39|±  |  1.45|
|bigbench_temporal_sequences                     |      0|multiple_choice_grade|31.00|±  |  1.46|
|bigbench_tracking_shuffled_objects_five_objects |      0|multiple_choice_grade|21.44|±  |  1.16|
|bigbench_tracking_shuffled_objects_seven_objects|      0|multiple_choice_grade|18.29|±  |  0.92|
|bigbench_tracking_shuffled_objects_three_objects|      0|multiple_choice_grade|53.00|±  |  2.89|

Average: 44.44%

Average score: 54.95%

Elapsed time: 02:50:51

## 🧩 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"])
```