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llama3.1-8b-spaetzle-v90
cstr/llama3.1-8b-spaetzle-v90
llama3.1-8b-spaetzle-v90 is a machine learning model from cstr. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as llama3.
llama3.1-8b-spaetzle-v90 is a progressive merge of merges.
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At a glance
- License
- llama3
- Model type
- llama
- Access
- Public
- Created
- Sep 15, 2024
- Updated
- Aug 2, 2026
- SHA
- 916ce665
Base models
Model card
From the Hugging Face model README
llama3.1-8b-spaetzle-v90
llama3.1-8b-spaetzle-v90 is a progressive merge of merges.
evaluation
German EQ-Bench v2_de: 69.93 (171/171). English (v2): 77.88 (171/171)
Open LLM Leaderboard Evaluation Results Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 27.59 |
| IFEval (0-Shot) | 73.56 |
| BBH (3-Shot) | 32.76 |
| MATH Lvl 5 (4-Shot) | 13.37 |
| GPQA (0-shot) | 4.36 |
| MuSR (0-shot) | 11.15 |
| MMLU-PRO (5-shot) | 30.34 |
| Model | AGIEval | TruthfulQA | Bigbench |
|---|---|---|---|
| llama3.1-8b-spaetzle-v90 | 42.05 | 57.2 | 44.75 |
AGIEval
| Task | Version | Metric | Value | Stderr | |
|---|---|---|---|---|---|
| agieval_aqua_rat | 0 | acc | 24.02 | ± | 2.69 |
| acc_norm | 23.62 | ± | 2.67 | ||
| agieval_logiqa_en | 0 | acc | 40.09 | ± | 1.92 |
| acc_norm | 39.78 | ± | 1.92 | ||
| agieval_lsat_ar | 0 | acc | 22.17 | ± | 2.75 |
| acc_norm | 21.74 | ± | 2.73 | ||
| agieval_lsat_lr | 0 | acc | 50.39 | ± | 2.22 |
| acc_norm | 45.29 | ± | 2.21 | ||
| agieval_lsat_rc | 0 | acc | 64.31 | ± | 2.93 |
| acc_norm | 58.36 | ± | 3.01 | ||
| agieval_sat_en | 0 | acc | 81.07 | ± | 2.74 |
| acc_norm | 73.79 | ± | 3.07 | ||
| agieval_sat_en_without_passage | 0 | acc | 45.15 | ± | 3.48 |
| acc_norm | 38.83 | ± | 3.40 | ||
| agieval_sat_math | 0 | acc | 40.91 | ± | 3.32 |
| acc_norm | 35.00 | ± | 3.22 |
Average: 42.05%
TruthfulQA
| Task | Version | Metric | Value | Stderr | |
|---|---|---|---|---|---|
| truthfulqa_mc | 1 | mc1 | 39.66 | ± | 1.71 |
| mc2 | 57.20 | ± | 1.51 |
Average: 57.2%
Bigbench
| Task | Version | Metric | Value | Stderr | |
|---|---|---|---|---|---|
| bigbench_causal_judgement | 0 | multiple_choice_grade | 58.42 | ± | 3.59 |
| bigbench_date_understanding | 0 | multiple_choice_grade | 70.46 | ± | 2.38 |
| bigbench_disambiguation_qa | 0 | multiple_choice_grade | 31.40 | ± | 2.89 |
| bigbench_geometric_shapes | 0 | multiple_choice_grade | 33.43 | ± | 2.49 |
| exact_str_match | 0.00 | ± | 0.00 | ||
| bigbench_logical_deduction_five_objects | 0 | multiple_choice_grade | 30.00 | ± | 2.05 |
| bigbench_logical_deduction_seven_objects | 0 | multiple_choice_grade | 24.29 | ± | 1.62 |
| bigbench_logical_deduction_three_objects | 0 | multiple_choice_grade | 56.00 | ± | 2.87 |
| bigbench_movie_recommendation | 0 | multiple_choice_grade | 38.20 | ± | 2.18 |
| bigbench_navigate | 0 | multiple_choice_grade | 50.20 | ± | 1.58 |
| bigbench_reasoning_about_colored_objects | 0 | multiple_choice_grade | 69.50 | ± | 1.03 |
| bigbench_ruin_names | 0 | multiple_choice_grade | 54.46 | ± | 2.36 |
| bigbench_salient_translation_error_detection | 0 | multiple_choice_grade | 32.77 | ± | 1.49 |
| bigbench_snarks | 0 | multiple_choice_grade | 65.19 | ± | 3.55 |
| bigbench_sports_understanding | 0 | multiple_choice_grade | 50.30 | ± | 1.59 |
| bigbench_temporal_sequences | 0 | multiple_choice_grade | 45.70 | ± | 1.58 |
| bigbench_tracking_shuffled_objects_five_objects | 0 | multiple_choice_grade | 22.08 | ± | 1.17 |
| bigbench_tracking_shuffled_objects_seven_objects | 0 | multiple_choice_grade | 17.03 | ± | 0.90 |
| bigbench_tracking_shuffled_objects_three_objects | 0 | multiple_choice_grade | 56.00 | ± | 2.87 |
Average: 44.75%
merge tree
The merge tree involves the following models:
- NousResearch/Hermes-3-Llama-3.1-8B
- Undi95/Meta-Llama-3.1-8B-Claude
- Dampfinchen/Llama-3.1-8B-Ultra-Instruct
- VAGOsolutions/Llama-3.1-SauerkrautLM-8b-Instruct
- akjindal53244/Llama-3.1-Storm-8B
- nbeerbower/llama3.1-gutenberg-8B
- Undi95/Meta-Llama-3.1-8B-Claude
- DiscoResearch/Llama3-DiscoLeo-Instruct-8B-v0.1
- nbeerbower/llama-3-wissenschaft-8B-v2
- Azure99/blossom-v5-llama3-8b
- VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct
- princeton-nlp/Llama-3-Instruct-8B-SimPO
- Locutusque/llama-3-neural-chat-v1-8b
- Locutusque/Llama-3-Orca-1.0-8B
- DiscoResearch/Llama3_DiscoLM_German_8b_v0.1_experimental
- seedboxai/Llama-3-Kafka-8B-v0.2
- VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct
- nbeerbower/llama-3-wissenschaft-8B-v2
- mlabonne/Daredevil-8B-abliterated-dpomix
There have been a number of steps involved, among which, slep merging of only middle layers compensating for tokenizer / chat template differences. An illustration below.
🧩 Configuration
The final merge for this was:
models:
- model: cstr/llama3.1-8b-spaetzle-v59
# no parameters necessary for base model
- model: cstr/llama3.1-8b-spaetzle-v85
parameters:
density: 0.65
weight: 0.3
- model: cstr/llama3.1-8b-spaetzle-v86
parameters:
density: 0.65
weight: 0.3
- model: cstr/llama3.1-8b-spaetzle-v74
parameters:
density: 0.65
weight: 0.3
merge_method: dare_ties
base_model: cstr/llama3.1-8b-spaetzle-v59
parameters:
int8_mask: true
dtype: bfloat16
random_seed: 0
tokenizer_source: base
Among the previous steps:
models:
- model: NousResearch/Hermes-3-Llama-3.1-8B
merge_method: slerp
base_model: cstr/llama3.1-8b-spaetzle-v74
parameters:
t:
- value: [0, 0, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0, 0]
dtype: float16
💻 Usage
Use with llama3 chat template as common. Here are GGUF quants for use with llama.cpp & wrappers as e.g. ollama: cstr/llama3.1-8b-spaetzle-v90-GGUF
EU AI Act Art. 53 — provider obligations
Added 2026-08-02 during an account-wide provenance review.
This is a model merge, not a format conversion. Most cstr/* repositories
are GGUF conversions, where the upstream research team remains the provider of
the model and the conversion changes only the numeric representation of the
weights. A merge produces a model that did not previously exist, so under
Regulation (EU) 2024/1689 the maintainer of this repository is plausibly the
provider of it, and the duties that survive the Art. 53(2)
free-and-open-source exemption — Art. 53(1)(c) and 53(1)(d) — attach here rather
than upstream.
Art. 53(1)(c) — copyright policy. No training corpus was assembled by this repository. Merging combines weights that other providers already published; it performs no text or data mining, so no rights reservation under Art. 4(3) of Directive (EU) 2019/790 was engaged by this step. Copyright questions arising from how the constituent models were themselves trained attach to their respective providers. Any credible claim that this repository redistributes material it has no right to redistribute will be acted on — contact via the Community tab.
Art. 53(1)(d) — training content. No data was used to train this model: it
is a weight-space combination of models trained by others, and its training
content is theirs. Of the 3 constituent models this card names, 1 are still published and 2 are not: cstr/llama3.1-8b-spaetzle-v85, cstr/llama3.1-8b-spaetzle-v86. For those, the training-content chain cannot be followed from this card, and no summary is reconstructed here in their place — an untraceable summary presented as a traceable one would be worse than the gap.
- Type
- llama
- License
- llama3
- Languages
- en, de
- Created
- Sep 15, 2024
- Updated
- Aug 2, 2026