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---
language:
- de
- en
license: cc-by-nc-4.0
tags:
- merge
- mergekit
- lazymergekit
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
model-index:
- name: Spaetzle-v69-7b
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 69.54
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=cstr/Spaetzle-v69-7b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 86.77
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=cstr/Spaetzle-v69-7b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 64.63
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=cstr/Spaetzle-v69-7b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 65.61
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=cstr/Spaetzle-v69-7b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 81.93
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=cstr/Spaetzle-v69-7b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 68.76
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=cstr/Spaetzle-v69-7b
name: Open LLM Leaderboard
---
# 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).
| 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)
- [yleo/EmertonMonarch-7B](https://huggingface.co/yleo/EmertonMonarch-7B)
- [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"])
```
# [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|
|