---
tags:
- merge
- mergekit
- cstr/Spaetzle-v80-7b
- cstr/Spaetzle-v79-7b
- cstr/Spaetzle-v81-7b
- cstr/Spaetzle-v71-7b
base_model:
- cstr/Spaetzle-v80-7b
- cstr/Spaetzle-v79-7b
- cstr/Spaetzle-v81-7b
- cstr/Spaetzle-v71-7b
license: cc-by-nc-4.0
language:
- de
- en
---

# Spaetzle-v85-7b

Spaetzle-v85-7b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [cstr/Spaetzle-v84-7b](https://huggingface.co/cstr/Spaetzle-v84-7b)
* [cstr/Spaetzle-v81-7b](https://huggingface.co/cstr/Spaetzle-v81-7b)
* [cstr/Spaetzle-v80-7b](https://huggingface.co/cstr/Spaetzle-v80-7b)
* [cstr/Spaetzle-v79-7b](https://huggingface.co/cstr/Spaetzle-v79-7b)
* [cstr/Spaetzle-v71-7b](https://huggingface.co/cstr/Spaetzle-v71-7b)

## Evaluation

EQ-Bench (v2_de): 65.32, Parseable: 171.0

|                            Model                             |AGIEval|GPT4All|TruthfulQA|Bigbench|Average|
|--------------------------------------------------------------|------:|------:|---------:|-------:|------:|
|[Spaetzle-v85-7b](https://huggingface.co/cstr/Spaetzle-v85-7b)|  44.35|  75.99|     67.23|   46.55|  58.53|


From [Intel/low_bit_open_llm_leaderboard](https://huggingface.co/datasets/Intel/ld_results/blob/main/cstr/Spaetzle-v85-7b-int4-inc/results_2024-06-12-21-00-34.json):

| Metric       | Value   |
|--------------|---------|
| ARC-c        | 62.63   |
| ARC-e        | 85.56   |
| Boolq        | 87.77   |
| HellaSwag    | 66.66   |
| Lambada      | 70.35   |
| MMLU         | 61.61   |
| Openbookqa   | 37.2    |
| Piqa         | 82.48   |
| Truthfulqa   | 50.43   |
| Winogrande   | 78.3    |
| Average      | 68.3    |

From [Occiglot Euro LLM Leaderboard](https://huggingface.co/spaces/occiglot/euro-llm-leaderboard)
| Model                                        | 🇪🇺 Average ⬆️ | 🇩🇪 DE | 🇬🇧 EN | 🇬🇧ARC EN | 🇬🇧TruthfulQA EN | 🇬🇧Belebele EN | 🇬🇧HellaSwag EN | 🇬🇧MMLU EN | 🇩🇪ARC DE | 🇩🇪TruthfulQA DE | 🇩🇪Belebele DE | 🇩🇪HellaSwag DE | 🇩🇪MMLU DE |
|----------------------------------------------|----------------|--------|--------|-------------|------------------|----------------|----------------|------------|-------------|------------------|----------------|----------------|------------|
| mistral-community/Mixtral-8x22B-v0.1         | 68.3           | 66.81  | 72.87  | 70.56       | 52.29            | 93.89          | 70.41          | 77.17      | 63.9        | 29.31            | 92.44          | 77.9           | 70.49      |
| **cstr/Spaetzle-v85-7b**                        | 63.26          | 61.11  | 71.94  | 70.48       | 67.16            | 90.33          | 68.54          | 63.17      | 58.43       | 36.93            | 84.22          | 70.62          | 55.36      |
| cstr/Spaetzle-v60-7b                        | 63.32          | 60.95  | 71.65  | 69.88       | 66.24            | 90.11          | 68.43          | 63.59      | 58          | 37.31            | 84.22          | 70.09          | 55.11      |
| VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct| 64.49          | 60.07  | 74.71  | 74.49       | 66.19            | 91.67          | 74.55          | 66.65      | 59.37       | 29.57            | 88.56          | 66.43          | 56.44      |
| seedboxai/Llama-3-KafkaLM-8B-v0.1            | 62.27          | 59.67  | 69.75  | 69.03       | 58.14            | 90.78          | 64.35          | 66.43      | 57.66       | 30.33            | 85.89          | 66.88          | 57.58      |
| cstr/llama3-8b-spaetzle-v33                  | 62.75          | 59.56  | 70.68  | 69.54       | 59.31            | 91.44          | 66.04          | 67.06      | 57.06       | 28.55            | 87.56          | 66.7           | 57.92      |

### AGIEval
|             Task             |Version| Metric |Value|   |Stderr|
|------------------------------|------:|--------|----:|---|-----:|
|agieval_aqua_rat              |      0|acc     |23.23|±  |  2.65|
|                              |       |acc_norm|22.44|±  |  2.62|
|agieval_logiqa_en             |      0|acc     |37.33|±  |  1.90|
|                              |       |acc_norm|37.94|±  |  1.90|
|agieval_lsat_ar               |      0|acc     |25.22|±  |  2.87|
|                              |       |acc_norm|23.04|±  |  2.78|
|agieval_lsat_lr               |      0|acc     |49.41|±  |  2.22|
|                              |       |acc_norm|50.78|±  |  2.22|
|agieval_lsat_rc               |      0|acc     |64.68|±  |  2.92|
|                              |       |acc_norm|63.20|±  |  2.95|
|agieval_sat_en                |      0|acc     |77.67|±  |  2.91|
|                              |       |acc_norm|78.16|±  |  2.89|
|agieval_sat_en_without_passage|      0|acc     |46.12|±  |  3.48|
|                              |       |acc_norm|45.15|±  |  3.48|
|agieval_sat_math              |      0|acc     |35.45|±  |  3.23|
|                              |       |acc_norm|34.09|±  |  3.20|

Average: 44.35%

### GPT4All
|    Task     |Version| Metric |Value|   |Stderr|
|-------------|------:|--------|----:|---|-----:|
|arc_challenge|      0|acc     |63.82|±  |  1.40|
|             |       |acc_norm|64.76|±  |  1.40|
|arc_easy     |      0|acc     |85.90|±  |  0.71|
|             |       |acc_norm|82.32|±  |  0.78|
|boolq        |      1|acc     |87.61|±  |  0.58|
|hellaswag    |      0|acc     |67.39|±  |  0.47|
|             |       |acc_norm|85.36|±  |  0.35|
|openbookqa   |      0|acc     |38.80|±  |  2.18|
|             |       |acc_norm|48.80|±  |  2.24|
|piqa         |      0|acc     |83.03|±  |  0.88|
|             |       |acc_norm|84.17|±  |  0.85|
|winogrande   |      0|acc     |78.93|±  |  1.15|

Average: 75.99%

### TruthfulQA
|    Task     |Version|Metric|Value|   |Stderr|
|-------------|------:|------|----:|---|-----:|
|truthfulqa_mc|      1|mc1   |50.80|±  |  1.75|
|             |       |mc2   |67.23|±  |  1.49|

Average: 67.23%

### Bigbench
|                      Task                      |Version|       Metric        |Value|   |Stderr|
|------------------------------------------------|------:|---------------------|----:|---|-----:|
|bigbench_causal_judgement                       |      0|multiple_choice_grade|54.74|±  |  3.62|
|bigbench_date_understanding                     |      0|multiple_choice_grade|68.29|±  |  2.43|
|bigbench_disambiguation_qa                      |      0|multiple_choice_grade|39.53|±  |  3.05|
|bigbench_geometric_shapes                       |      0|multiple_choice_grade|22.28|±  |  2.20|
|                                                |       |exact_str_match      |12.26|±  |  1.73|
|bigbench_logical_deduction_five_objects         |      0|multiple_choice_grade|32.80|±  |  2.10|
|bigbench_logical_deduction_seven_objects        |      0|multiple_choice_grade|23.00|±  |  1.59|
|bigbench_logical_deduction_three_objects        |      0|multiple_choice_grade|59.00|±  |  2.84|
|bigbench_movie_recommendation                   |      0|multiple_choice_grade|45.60|±  |  2.23|
|bigbench_navigate                               |      0|multiple_choice_grade|51.10|±  |  1.58|
|bigbench_reasoning_about_colored_objects        |      0|multiple_choice_grade|70.10|±  |  1.02|
|bigbench_ruin_names                             |      0|multiple_choice_grade|52.68|±  |  2.36|
|bigbench_salient_translation_error_detection    |      0|multiple_choice_grade|33.57|±  |  1.50|
|bigbench_snarks                                 |      0|multiple_choice_grade|71.27|±  |  3.37|
|bigbench_sports_understanding                   |      0|multiple_choice_grade|74.54|±  |  1.39|
|bigbench_temporal_sequences                     |      0|multiple_choice_grade|40.00|±  |  1.55|
|bigbench_tracking_shuffled_objects_five_objects |      0|multiple_choice_grade|21.52|±  |  1.16|
|bigbench_tracking_shuffled_objects_seven_objects|      0|multiple_choice_grade|18.86|±  |  0.94|
|bigbench_tracking_shuffled_objects_three_objects|      0|multiple_choice_grade|59.00|±  |  2.84|

Average: 46.55%

Average score: 58.53%

## 🧩 Configuration

```yaml
models:
  - model: cstr/Spaetzle-v84-7b
    # no parameters necessary for base model
  - model: cstr/Spaetzle-v80-7b
    parameters:
      density: 0.65
      weight: 0.2
  - model: cstr/Spaetzle-v79-7b
    parameters:
      density: 0.65
      weight: 0.2
  - model: cstr/Spaetzle-v81-7b
    parameters:
      density: 0.65
      weight: 0.2
  - model: cstr/Spaetzle-v71-7b
    parameters:
      density: 0.65
      weight: 0.2
merge_method: dare_ties
base_model: cstr/Spaetzle-v84-7b
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-v85-7b"
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"])
```