llama3.1-8b-spaetzle-v59
llama3.1-8b-spaetzle-v59 is a dare ties merge of the models
EQ-Bench v2_de: 67.38 (171/171) (which is not bad...) with q4 quants: cstr/llama3.1-8b-spaetzle-v59-GGUF
🧩 Configuration
models:
- model: Dampfinchen/Llama-3.1-8B-Ultra-Instruct
# no parameters necessary for base model
- model: Undi95/Meta-Llama-3.1-8B-Claude
parameters:
density: 0.65
weight: 0.4
merge_method: dare_ties
base_model: Dampfinchen/Llama-3.1-8B-Ultra-Instruct
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/llama3.1-8b-spaetzle-v59"
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. All 1 constituent models this card names are still published, so the chain can be followed from here.