llama3-8b-spaetzle-v33

llama3-8b-spaetzle-v33

This is a merge of the following models:

• cstr/llama3-8b-spaetzle-v31
• cstr/llama3-8b-spaetzle-v28
• cstr/llama3-8b-spaetzle-v26
• cstr/llama3-8b-spaetzle-v20

It attempts a compromise in usefulness for German and English tasks.

For GGUF quants see cstr/llama3-8b-spaetzle-v33-GGUF,

Benchmarks

It achieves on EQ-Bench v2_de as q4km (old version without pre-tokenizer-fix) quants 66.59 (171 of 171 parseable) and 73.17 on v2 (english) (171/171).

For the int4-inc quants:

<!-- | Average | ARC-c | ARC-e | Boolq | HellaSwag | Lambada | MMLU | Openbookqa | Piqa | Truthfulqa | Winogrande | |----------|-------|-------|--------|-----------|---------|-------|------------|-------|------------|------------| | 66.13 | 59.81 | 85.27 | 84.10 | 62.47 | 73.28 | 64.11 | 37.2 | 80.30 | 50.21 | 73.72 | -->

Nous

🧩 Configuration

models:
  - model: cstr/llama3-8b-spaetzle-v20
    # no parameters necessary for base model
  - model: cstr/llama3-8b-spaetzle-v31
    parameters:
      density: 0.65
      weight: 0.25
  - model: cstr/llama3-8b-spaetzle-v28
    parameters:
      density: 0.65
      weight: 0.25
  - model: cstr/llama3-8b-spaetzle-v26
    parameters:
      density: 0.65
      weight: 0.15
merge_method: dare_ties
base_model: cstr/llama3-8b-spaetzle-v20
parameters:
  int8_mask: true
dtype: bfloat16
random_seed: 0
tokenizer_source: base

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "cstr/llama3-8b-spaetzle-v33"
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"])

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 4 constituent models this card names, 2 are still published and 2 are not: cstr/llama3-8b-spaetzle-v28, cstr/llama3-8b-spaetzle-v26. 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.