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cstr
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Spaetzle-v31-7b

Text Generation
Transformers
Safetensors
mistral
Merge
mergekit
lazymergekit
yleo/EmertonMonarch-7B
conversational
text-generation-inference
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Spaetzle-v31-7b
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---
tags:
- merge
- mergekit
- lazymergekit
- yleo/EmertonMonarch-7B
base_model:
- yleo/EmertonMonarch-7B
---

# Spaetzle-v31-7b

Spaetzle-v31-7b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [yleo/EmertonMonarch-7B](https://huggingface.co/yleo/EmertonMonarch-7B)
* [cstr/spaetzle-v8-7b](https://huggingface.co/cstr/spaetzle-v8-7b)

|                            Model                             |AGIEval|GPT4All|TruthfulQA|Bigbench|Average|
|--------------------------------------------------------------|------:|------:|---------:|-------:|------:|
|[Spaetzle-v31-7b](https://huggingface.co/cstr/Spaetzle-v31-7b)|  46.23|   76.6|     69.58|   46.79|   59.8|

### AGIEval
|             Task             |Version| Metric |Value|   |Stderr|
|------------------------------|------:|--------|----:|---|-----:|
|agieval_aqua_rat              |      0|acc     |28.74|±  |  2.85|
|                              |       |acc_norm|27.56|±  |  2.81|
|agieval_logiqa_en             |      0|acc     |39.63|±  |  1.92|
|                              |       |acc_norm|40.25|±  |  1.92|
|agieval_lsat_ar               |      0|acc     |24.35|±  |  2.84|
|                              |       |acc_norm|24.35|±  |  2.84|
|agieval_lsat_lr               |      0|acc     |54.31|±  |  2.21|
|                              |       |acc_norm|54.12|±  |  2.21|
|agieval_lsat_rc               |      0|acc     |65.80|±  |  2.90|
|                              |       |acc_norm|66.54|±  |  2.88|
|agieval_sat_en                |      0|acc     |79.13|±  |  2.84|
|                              |       |acc_norm|79.61|±  |  2.81|
|agieval_sat_en_without_passage|      0|acc     |46.12|±  |  3.48|
|                              |       |acc_norm|45.15|±  |  3.48|
|agieval_sat_math              |      0|acc     |35.00|±  |  3.22|
|                              |       |acc_norm|32.27|±  |  3.16|

Average: 46.23%

### GPT4All
|    Task     |Version| Metric |Value|   |Stderr|
|-------------|------:|--------|----:|---|-----:|
|arc_challenge|      0|acc     |64.76|±  |  1.40|
|             |       |acc_norm|66.89|±  |  1.38|
|arc_easy     |      0|acc     |86.66|±  |  0.70|
|             |       |acc_norm|82.83|±  |  0.77|
|boolq        |      1|acc     |87.80|±  |  0.57|
|hellaswag    |      0|acc     |67.43|±  |  0.47|
|             |       |acc_norm|85.85|±  |  0.35|
|openbookqa   |      0|acc     |38.00|±  |  2.17|
|             |       |acc_norm|48.80|±  |  2.24|
|piqa         |      0|acc     |83.57|±  |  0.86|
|             |       |acc_norm|84.71|±  |  0.84|
|winogrande   |      0|acc     |79.32|±  |  1.14|

Average: 76.6%

### TruthfulQA
|    Task     |Version|Metric|Value|   |Stderr|
|-------------|------:|------|----:|---|-----:|
|truthfulqa_mc|      1|mc1   |53.37|±  |  1.75|
|             |       |mc2   |69.58|±  |  1.48|

Average: 69.58%

### Bigbench
|                      Task                      |Version|       Metric        |Value|   |Stderr|
|------------------------------------------------|------:|---------------------|----:|---|-----:|
|bigbench_causal_judgement                       |      0|multiple_choice_grade|56.84|±  |  3.60|
|bigbench_date_understanding                     |      0|multiple_choice_grade|66.94|±  |  2.45|
|bigbench_disambiguation_qa                      |      0|multiple_choice_grade|44.57|±  |  3.10|
|bigbench_geometric_shapes                       |      0|multiple_choice_grade|21.17|±  |  2.16|
|                                                |       |exact_str_match      | 0.28|±  |  0.28|
|bigbench_logical_deduction_five_objects         |      0|multiple_choice_grade|31.80|±  |  2.08|
|bigbench_logical_deduction_seven_objects        |      0|multiple_choice_grade|22.57|±  |  1.58|
|bigbench_logical_deduction_three_objects        |      0|multiple_choice_grade|56.00|±  |  2.87|
|bigbench_movie_recommendation                   |      0|multiple_choice_grade|45.40|±  |  2.23|
|bigbench_navigate                               |      0|multiple_choice_grade|52.80|±  |  1.58|
|bigbench_reasoning_about_colored_objects        |      0|multiple_choice_grade|70.65|±  |  1.02|
|bigbench_ruin_names                             |      0|multiple_choice_grade|50.67|±  |  2.36|
|bigbench_salient_translation_error_detection    |      0|multiple_choice_grade|30.66|±  |  1.46|
|bigbench_snarks                                 |      0|multiple_choice_grade|71.27|±  |  3.37|
|bigbench_sports_understanding                   |      0|multiple_choice_grade|74.34|±  |  1.39|
|bigbench_temporal_sequences                     |      0|multiple_choice_grade|49.80|±  |  1.58|
|bigbench_tracking_shuffled_objects_five_objects |      0|multiple_choice_grade|22.16|±  |  1.18|
|bigbench_tracking_shuffled_objects_seven_objects|      0|multiple_choice_grade|18.57|±  |  0.93|
|bigbench_tracking_shuffled_objects_three_objects|      0|multiple_choice_grade|56.00|±  |  2.87|

Average: 46.79%

Average score: 59.8%

Elapsed time: 02:09:50

## 🧩 Configuration

```yaml
models:
  - model: cstr/spaetzle-v8-7b
    # no parameters necessary for base model
  - model: yleo/EmertonMonarch-7B
    parameters:
      density: 0.60
      weight: 0.3
merge_method: dare_ties
base_model: cstr/spaetzle-v8-7b
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-v31-7b"
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"])
```