---

tags:

• merge
• mergekit
• lazymergekit
• abideen/AlphaMonarch-dora

base_model:

• abideen/AlphaMonarch-dora

license: cc-by-nc-4.0

---

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 an unquantized version.

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).

It should work sufficiently well with ChatML prompt template (for all merged models should have seen ChatML prompts at least in DPO stage).

Spaetzle-v69-7b is a merge of the following models using LazyMergekit:

• abideen/AlphaMonarch-dora
• cstr/Spaetzle-v68-7b

The merge tree in total involves to following original models:

- 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

- yleo/EmertonMonarch-7B

- 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

🧩 Configuration

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

!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"])

Provenance and EU AI Act Art. 53 note

• Base model: cstr/Spaetzle-v69-7b — a mergekit merge published by the same maintainer as this repository. It is not a third-party upstream: the maintainer authored that model.
• What was done here: format conversion and/or quantisation of that base model only (GGUF). No further training, fine-tuning or merging was applied at this step.
• Licence: cc-by-nc-4.0, inherited through the base model from the models it was built from.
• Training data: none was used, added or selected at this conversion step. The base model's card lists the models it was built from; their training content is documented — where it is documented at all — by their respective providers.
• Provider status: under Regulation (EU) 2024/1689 this repository makes no provider claim for the conversion step. Any provider obligations attaching to the model itself — including Art. 53(1)(c) copyright policy and Art. 53(1)(d) training-content summary — attach at cstr/Spaetzle-v69-7b, not here.