---
tags:
- merge
- mergekit
- lazymergekit
- flemmingmiguel/NeuDist-Ro-7B
- johannhartmann/Brezn3
- ResplendentAI/Flora_DPO_7B
base_model:
- flemmingmiguel/NeuDist-Ro-7B
- johannhartmann/Brezn3
- ResplendentAI/Flora_DPO_7B
language:
- de
- en
---

# Spaetzle-v8-7b

This model is supposed to show adequate performance in German and English on a number of tasks, while mostly behaving well, that is, without rambling on, intermixing tokens from different templates in training and adapting, etc.

It 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)
* [johannhartmann/Brezn3](https://huggingface.co/johannhartmann/Brezn3)
* [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)

All credits are due to the creators of those original models and the training datasets involved.

For a suitable quantized version, try [cstr/Spaetzle-v8-7b-GGUF](https://huggingface.co/cstr/Spaetzle-v8-7b-GGUF)


## Evaluation
[Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_cstr__Spaetzle-v8-7b)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |72.27|
|AI2 Reasoning Challenge (25-Shot)|68.69|
|HellaSwag (10-Shot)              |86.68|
|MMLU (5-Shot)                    |64.60|
|TruthfulQA (0-shot)              |64.05|
|Winogrande (5-shot)              |81.45|
|GSM8k (5-shot)                   |68.16|

EQ-Bench (v2_de): 61.04 / english (v2): 78.3

|                           Model                            |AGIEval|GPT4All|TruthfulQA|Bigbench|Average|
|------------------------------------------------------------|------:|------:|---------:|-------:|------:|
|[Spaetzle-v8-7b](https://huggingface.co/cstr/Spaetzle-v8-7b)|  45.31|  75.69|     63.94|   45.57|  57.63|

### AGIEval
|             Task             |Version| Metric |Value|   |Stderr|
|------------------------------|------:|--------|----:|---|-----:|
|agieval_aqua_rat              |      0|acc     |25.59|±  |  2.74|
|                              |       |acc_norm|24.80|±  |  2.72|
|agieval_logiqa_en             |      0|acc     |39.63|±  |  1.92|
|                              |       |acc_norm|39.78|±  |  1.92|
|agieval_lsat_ar               |      0|acc     |23.48|±  |  2.80|
|                              |       |acc_norm|24.35|±  |  2.84|
|agieval_lsat_lr               |      0|acc     |50.98|±  |  2.22|
|                              |       |acc_norm|51.96|±  |  2.21|
|agieval_lsat_rc               |      0|acc     |62.08|±  |  2.96|
|                              |       |acc_norm|62.83|±  |  2.95|
|agieval_sat_en                |      0|acc     |78.64|±  |  2.86|
|                              |       |acc_norm|79.13|±  |  2.84|
|agieval_sat_en_without_passage|      0|acc     |44.66|±  |  3.47|
|                              |       |acc_norm|44.66|±  |  3.47|
|agieval_sat_math              |      0|acc     |37.27|±  |  3.27|
|                              |       |acc_norm|35.00|±  |  3.22|

Average: 45.31%

### GPT4All
|    Task     |Version| Metric |Value|   |Stderr|
|-------------|------:|--------|----:|---|-----:|
|arc_challenge|      0|acc     |63.14|±  |  1.41|
|             |       |acc_norm|64.51|±  |  1.40|
|arc_easy     |      0|acc     |85.98|±  |  0.71|
|             |       |acc_norm|82.49|±  |  0.78|
|boolq        |      1|acc     |88.10|±  |  0.57|
|hellaswag    |      0|acc     |66.31|±  |  0.47|
|             |       |acc_norm|85.17|±  |  0.35|
|openbookqa   |      0|acc     |38.00|±  |  2.17|
|             |       |acc_norm|47.20|±  |  2.23|
|piqa         |      0|acc     |83.35|±  |  0.87|
|             |       |acc_norm|84.17|±  |  0.85|
|winogrande   |      0|acc     |78.22|±  |  1.16|

Average: 75.69%

### TruthfulQA
|    Task     |Version|Metric|Value|   |Stderr|
|-------------|------:|------|----:|---|-----:|
|truthfulqa_mc|      1|mc1   |47.74|±  |  1.75|
|             |       |mc2   |63.94|±  |  1.53|

Average: 63.94%

### Bigbench
|                      Task                      |Version|       Metric        |Value|   |Stderr|
|------------------------------------------------|------:|---------------------|----:|---|-----:|
|bigbench_causal_judgement                       |      0|multiple_choice_grade|56.84|±  |  3.60|
|bigbench_date_understanding                     |      0|multiple_choice_grade|66.12|±  |  2.47|
|bigbench_disambiguation_qa                      |      0|multiple_choice_grade|41.47|±  |  3.07|
|bigbench_geometric_shapes                       |      0|multiple_choice_grade|22.01|±  |  2.19|
|                                                |       |exact_str_match      | 0.00|±  |  0.00|
|bigbench_logical_deduction_five_objects         |      0|multiple_choice_grade|31.40|±  |  2.08|
|bigbench_logical_deduction_seven_objects        |      0|multiple_choice_grade|23.14|±  |  1.60|
|bigbench_logical_deduction_three_objects        |      0|multiple_choice_grade|56.00|±  |  2.87|
|bigbench_movie_recommendation                   |      0|multiple_choice_grade|45.00|±  |  2.23|
|bigbench_navigate                               |      0|multiple_choice_grade|50.70|±  |  1.58|
|bigbench_reasoning_about_colored_objects        |      0|multiple_choice_grade|70.05|±  |  1.02|
|bigbench_ruin_names                             |      0|multiple_choice_grade|45.54|±  |  2.36|
|bigbench_salient_translation_error_detection    |      0|multiple_choice_grade|26.05|±  |  1.39|
|bigbench_snarks                                 |      0|multiple_choice_grade|71.82|±  |  3.35|
|bigbench_sports_understanding                   |      0|multiple_choice_grade|72.92|±  |  1.42|
|bigbench_temporal_sequences                     |      0|multiple_choice_grade|44.20|±  |  1.57|
|bigbench_tracking_shuffled_objects_five_objects |      0|multiple_choice_grade|22.80|±  |  1.19|
|bigbench_tracking_shuffled_objects_seven_objects|      0|multiple_choice_grade|18.23|±  |  0.92|
|bigbench_tracking_shuffled_objects_three_objects|      0|multiple_choice_grade|56.00|±  |  2.87|

Average: 45.57%

Average score: 57.63%

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "cstr/Spaetzle-v8-7b"
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"])
```


## 🧩 Configuration

The model uses ChatML and should work well with this (as it is merged from models which (mostly) saw ChatML templates in training). 

```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: johannhartmann/Brezn3
    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
```