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 mostly a quick test, and considerably weaker in German grammar and orthography than DiscoLM e.g., but for use cases where this is not too important, but e.g. instruction following, reasoning, etc, it might actually be a little bit preferable.

It is a merge of the following models using LazyMergekit:

• flemmingmiguel/NeuDist-Ro-7B
• johannhartmann/Brezn3
• ResplendentAI/Flora_DPO_7B
• on the basis of 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

Evaluation

Open LLM Leaderboard Evaluation Results Detailed results can be found here

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

ScandEval 12.5.2 scores

Benchmark	Spaetzle-v8-7b Value
Model ID	cstr/Spaetzle-v8-7b (few-shot, val)
Parameters	7242
Vocabulary Size	32
Context	32768
Commercial	False
Speed	5,980 ± 1,031 / 1,714 ± 552
Rank	1.85
GermEval	58.90 ± 2.30 / 45.55 ± 3.30
SB10k	61.34 ± 1.90 / 72.98 ± 1.30
ScaLA-De	31.58 ± 4.39 / 65.51 ± 2.23
GermanQuAD	24.91 ± 3.98 / 60.88 ± 3.31
MLSum	67.25 ± 1.06 / 22.95 ± 2.64
MMLU-De	34.62 ± 2.20 / 50.43 ± 1.52
HellaSwag-De	48.70 ± 2.47 / 61.05 ± 1.79

Model	AGIEval	GPT4All	TruthfulQA	Bigbench	Average
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

!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).

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