---
tags:
- merge
- mergekit
- lazymergekit
language:
- de
- en
base_model:
- abideen/AlphaMonarch-dora
- mayflowergmbh/Wiedervereinigung-7b-dpo
- flemmingmiguel/NeuDist-Ro-7B
- ResplendentAI/Flora_DPO_7B
- yleo/EmertonMonarch-7B
- occiglot/occiglot-7b-de-en-instruct
- OpenPipe/mistral-ft-optimized-1227
- DiscoResearch/DiscoLM_German_7b_v1
- LeoLM/leo-mistral-hessianai-7b
- DRXD1000/Phoenix
- VAGOsolutions/SauerkrautLM-7b-v1-mistral
- malteos/hermeo-7b
- FelixChao/WestSeverus-7B-DPO-v2
- cognitivecomputations/openchat-3.5-0106-laser
license: cc-by-nc-4.0
---

# Spaetzle-v69-7b
This is a progressive (mostly dare-ties, but also slerp) merge with the intention of a suitable compromise for English and German local tasks.

There is also a 4q_k_m quantized [GGUF](https://huggingface.co/cstr/Spaetzle-v69-7b-GGUF).

It should work sufficiently well with ChatML prompt template (for all merged models should have seen ChatML prompts at least in DPO stage).

## Evaluation

Benchmark scores are not the possible optimum, as the model attempts a compromise with a number of parameters, like German language performance, instruction following, reasoning capabilities, robustness (so far, i did not encounter inserted tokens, e.g.), model licensing, and other criteria.
Nevertheless, they are not too bad:

It achieves (running quantized) in 
- German EQ Bench: Score (v2_de): 62.59 (Parseable: 171.0).
- English EQ Bench: Score (v2): 76.43 (Parseable: 171.0).

[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-v69-7b)

 |             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |72.87|
|AI2 Reasoning Challenge (25-Shot)|69.54|
|HellaSwag (10-Shot)              |86.77|
|MMLU (5-Shot)                    |64.63|
|TruthfulQA (0-shot)              |65.61|
|Winogrande (5-shot)              |81.93|
|GSM8k (5-shot)                   |68.76|

Nous benchmark results:

|                            Model                             |AGIEval|GPT4All|TruthfulQA|Bigbench|Average|
|--------------------------------------------------------------|------:|------:|---------:|-------:|------:|
|[Spaetzle-v69-7b](https://huggingface.co/cstr/Spaetzle-v69-7b)|  44.48|  75.84|     66.15|   46.59|  58.27|

### AGIEval
|             Task             |Version| Metric |Value|   |Stderr|
|------------------------------|------:|--------|----:|---|-----:|
|agieval_aqua_rat              |      0|acc     |25.98|±  |  2.76|
|                              |       |acc_norm|23.62|±  |  2.67|
|agieval_logiqa_en             |      0|acc     |39.78|±  |  1.92|
|                              |       |acc_norm|39.48|±  |  1.92|
|agieval_lsat_ar               |      0|acc     |23.48|±  |  2.80|
|                              |       |acc_norm|23.91|±  |  2.82|
|agieval_lsat_lr               |      0|acc     |50.00|±  |  2.22|
|                              |       |acc_norm|51.76|±  |  2.21|
|agieval_lsat_rc               |      0|acc     |63.94|±  |  2.93|
|                              |       |acc_norm|64.31|±  |  2.93|
|agieval_sat_en                |      0|acc     |76.70|±  |  2.95|
|                              |       |acc_norm|77.67|±  |  2.91|
|agieval_sat_en_without_passage|      0|acc     |46.12|±  |  3.48|
|                              |       |acc_norm|44.17|±  |  3.47|
|agieval_sat_math              |      0|acc     |34.09|±  |  3.20|
|                              |       |acc_norm|30.91|±  |  3.12|

Average: 44.48%

### GPT4All
|    Task     |Version| Metric |Value|   |Stderr|
|-------------|------:|--------|----:|---|-----:|
|arc_challenge|      0|acc     |63.23|±  |  1.41|
|             |       |acc_norm|64.16|±  |  1.40|
|arc_easy     |      0|acc     |85.90|±  |  0.71|
|             |       |acc_norm|82.49|±  |  0.78|
|boolq        |      1|acc     |87.80|±  |  0.57|
|hellaswag    |      0|acc     |67.05|±  |  0.47|
|             |       |acc_norm|85.19|±  |  0.35|
|openbookqa   |      0|acc     |38.40|±  |  2.18|
|             |       |acc_norm|48.40|±  |  2.24|
|piqa         |      0|acc     |82.75|±  |  0.88|
|             |       |acc_norm|84.28|±  |  0.85|
|winogrande   |      0|acc     |78.53|±  |  1.15|

Average: 75.84%

### TruthfulQA
|    Task     |Version|Metric|Value|   |Stderr|
|-------------|------:|------|----:|---|-----:|
|truthfulqa_mc|      1|mc1   |50.67|±  |  1.75|
|             |       |mc2   |66.15|±  |  1.48|

Average: 66.15%

### Bigbench
|                      Task                      |Version|       Metric        |Value|   |Stderr|
|------------------------------------------------|------:|---------------------|----:|---|-----:|
|bigbench_causal_judgement                       |      0|multiple_choice_grade|56.84|±  |  3.60|
|bigbench_date_understanding                     |      0|multiple_choice_grade|66.67|±  |  2.46|
|bigbench_disambiguation_qa                      |      0|multiple_choice_grade|40.70|±  |  3.06|
|bigbench_geometric_shapes                       |      0|multiple_choice_grade|24.79|±  |  2.28|
|                                                |       |exact_str_match      |10.58|±  |  1.63|
|bigbench_logical_deduction_five_objects         |      0|multiple_choice_grade|31.00|±  |  2.07|
|bigbench_logical_deduction_seven_objects        |      0|multiple_choice_grade|23.00|±  |  1.59|
|bigbench_logical_deduction_three_objects        |      0|multiple_choice_grade|58.00|±  |  2.85|
|bigbench_movie_recommendation                   |      0|multiple_choice_grade|45.80|±  |  2.23|
|bigbench_navigate                               |      0|multiple_choice_grade|52.10|±  |  1.58|
|bigbench_reasoning_about_colored_objects        |      0|multiple_choice_grade|69.55|±  |  1.03|
|bigbench_ruin_names                             |      0|multiple_choice_grade|48.88|±  |  2.36|
|bigbench_salient_translation_error_detection    |      0|multiple_choice_grade|30.96|±  |  1.46|
|bigbench_snarks                                 |      0|multiple_choice_grade|73.48|±  |  3.29|
|bigbench_sports_understanding                   |      0|multiple_choice_grade|74.14|±  |  1.40|
|bigbench_temporal_sequences                     |      0|multiple_choice_grade|42.70|±  |  1.56|
|bigbench_tracking_shuffled_objects_five_objects |      0|multiple_choice_grade|23.60|±  |  1.20|
|bigbench_tracking_shuffled_objects_seven_objects|      0|multiple_choice_grade|18.40|±  |  0.93|
|bigbench_tracking_shuffled_objects_three_objects|      0|multiple_choice_grade|58.00|±  |  2.85|

Average: 46.59%

Average score: 58.27%

## 🧩 Merge Configuration

Spaetzle-v69-7b 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-v68-7b](https://huggingface.co/cstr/Spaetzle-v68-7b)

The merge tree in total involves the following original models:
  - [abideen/AlphaMonarch-dora](https://huggingface.co/abideen/AlphaMonarch-dora)
  - [mayflowergmbh/Wiedervereinigung-7b-dpo](https://huggingface.co/mayflowergmbh/Wiedervereinigung-7b-dpo)
  - [flemmingmiguel/NeuDist-Ro-7B](https://huggingface.co/flemmingmiguel/NeuDist-Ro-7B)
  - [ResplendentAI/Flora_DPO_7B](https://huggingface.co/ResplendentAI/Flora_DPO_7B)
  - [yleo/EmertonMonarch-7B](https://huggingface.co/yleo/EmertonMonarch-7B)
  - [occiglot/occiglot-7b-de-en-instruct](https://huggingface.co/occiglot/occiglot-7b-de-en-instruct)     
  - [OpenPipe/mistral-ft-optimized-1227](https://huggingface.co/OpenPipe/mistral-ft-optimized-1227)
  - [DiscoResearch/DiscoLM_German_7b_v1](https://huggingface.co/DiscoResearch/DiscoLM_German_7b_v1)
  - [LeoLM/leo-mistral-hessianai-7b](https://huggingface.co/LeoLM/leo-mistral-hessianai-7b)
  - [DRXD1000/Phoenix](https://huggingface.co/DRXD1000/Phoenix)
  - [VAGOsolutions/SauerkrautLM-7b-v1-mistral](https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-v1-mistral)
  - [malteos/hermeo-7b](https://huggingface.co/malteos/hermeo-7b)
  - [FelixChao/WestSeverus-7B-DPO-v2](https://huggingface.co/FelixChao/WestSeverus-7B-DPO-v2)
  - [cognitivecomputations/openchat-3.5-0106-laser](https://huggingface.co/cognitivecomputations/openchat-3.5-0106-laser)

For this last merge:

```yaml
models:
  - model: cstr/Spaetzle-v68-7b
    # 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-v68-7b
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-v69-7b"
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"])
```