Instructions to use cstr/Spaetzle-v69-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cstr/Spaetzle-v69-7b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="cstr/Spaetzle-v69-7b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("cstr/Spaetzle-v69-7b")
model = AutoModelForCausalLM.from_pretrained("cstr/Spaetzle-v69-7b", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use cstr/Spaetzle-v69-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cstr/Spaetzle-v69-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cstr/Spaetzle-v69-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cstr/Spaetzle-v69-7b

SGLang

How to use cstr/Spaetzle-v69-7b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "cstr/Spaetzle-v69-7b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cstr/Spaetzle-v69-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "cstr/Spaetzle-v69-7b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cstr/Spaetzle-v69-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use cstr/Spaetzle-v69-7b with Docker Model Runner:
```
docker model run hf.co/cstr/Spaetzle-v69-7b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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 a 4q_k_m quantized GGUF.

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

Evaluation

Benchmark scores are not the possible optimum, as the model attempts a compromise with a number of parameters, like German language performance, instruction following, reasoning capabilities, robustness (so far, i did not encounter inserted tokens, e.g.), model licensing, and other criteria. Nevertheless, they are not too bad:

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).

Open LLM Leaderboard Evaluation Results: Detailed results can be found here

Metric	Value
Avg.	72.87
AI2 Reasoning Challenge (25-Shot)	69.54
HellaSwag (10-Shot)	86.77
MMLU (5-Shot)	64.63
TruthfulQA (0-shot)	65.61
Winogrande (5-shot)	81.93
GSM8k (5-shot)	68.76

Nous benchmark results:

Model	AGIEval	GPT4All	TruthfulQA	Bigbench	Average
Spaetzle-v69-7b	44.48	75.84	66.15	46.59	58.27

AGIEval

Task	Version	Metric	Value		Stderr
agieval_aqua_rat	0	acc	25.98	±	2.76
		acc_norm	23.62	±	2.67
agieval_logiqa_en	0	acc	39.78	±	1.92
		acc_norm	39.48	±	1.92
agieval_lsat_ar	0	acc	23.48	±	2.80
		acc_norm	23.91	±	2.82
agieval_lsat_lr	0	acc	50.00	±	2.22
		acc_norm	51.76	±	2.21
agieval_lsat_rc	0	acc	63.94	±	2.93
		acc_norm	64.31	±	2.93
agieval_sat_en	0	acc	76.70	±	2.95
		acc_norm	77.67	±	2.91
agieval_sat_en_without_passage	0	acc	46.12	±	3.48
		acc_norm	44.17	±	3.47
agieval_sat_math	0	acc	34.09	±	3.20
		acc_norm	30.91	±	3.12

Average: 44.48%

GPT4All

Task	Version	Metric	Value		Stderr
arc_challenge	0	acc	63.23	±	1.41
		acc_norm	64.16	±	1.40
arc_easy	0	acc	85.90	±	0.71
		acc_norm	82.49	±	0.78
boolq	1	acc	87.80	±	0.57
hellaswag	0	acc	67.05	±	0.47
		acc_norm	85.19	±	0.35
openbookqa	0	acc	38.40	±	2.18
		acc_norm	48.40	±	2.24
piqa	0	acc	82.75	±	0.88
		acc_norm	84.28	±	0.85
winogrande	0	acc	78.53	±	1.15

Average: 75.84%

TruthfulQA

Task	Version	Metric	Value		Stderr
truthfulqa_mc	1	mc1	50.67	±	1.75
		mc2	66.15	±	1.48

Average: 66.15%

Bigbench

Task	Version	Metric	Value		Stderr
bigbench_causal_judgement	0	multiple_choice_grade	56.84	±	3.60
bigbench_date_understanding	0	multiple_choice_grade	66.67	±	2.46
bigbench_disambiguation_qa	0	multiple_choice_grade	40.70	±	3.06
bigbench_geometric_shapes	0	multiple_choice_grade	24.79	±	2.28
		exact_str_match	10.58	±	1.63
bigbench_logical_deduction_five_objects	0	multiple_choice_grade	31.00	±	2.07
bigbench_logical_deduction_seven_objects	0	multiple_choice_grade	23.00	±	1.59
bigbench_logical_deduction_three_objects	0	multiple_choice_grade	58.00	±	2.85
bigbench_movie_recommendation	0	multiple_choice_grade	45.80	±	2.23
bigbench_navigate	0	multiple_choice_grade	52.10	±	1.58
bigbench_reasoning_about_colored_objects	0	multiple_choice_grade	69.55	±	1.03
bigbench_ruin_names	0	multiple_choice_grade	48.88	±	2.36
bigbench_salient_translation_error_detection	0	multiple_choice_grade	30.96	±	1.46
bigbench_snarks	0	multiple_choice_grade	73.48	±	3.29
bigbench_sports_understanding	0	multiple_choice_grade	74.14	±	1.40
bigbench_temporal_sequences	0	multiple_choice_grade	42.70	±	1.56
bigbench_tracking_shuffled_objects_five_objects	0	multiple_choice_grade	23.60	±	1.20
bigbench_tracking_shuffled_objects_seven_objects	0	multiple_choice_grade	18.40	±	0.93
bigbench_tracking_shuffled_objects_three_objects	0	multiple_choice_grade	58.00	±	2.85

Average: 46.59%

Average score: 58.27%

🧩 Merge Configuration

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

The merge tree in total involves the following original models:

For this last merge:

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"])

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 14 constituent models this card names, 13 are still published and 1 are not: VAGOsolutions/SauerkrautLM-7b-v1-mistral. 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.