Instructions to use cstr/Spaetzle-v12-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cstr/Spaetzle-v12-7b with Transformers:

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

pipe = pipeline("text-generation", model="cstr/Spaetzle-v12-7b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("cstr/Spaetzle-v12-7b")
model = AutoModelForCausalLM.from_pretrained("cstr/Spaetzle-v12-7b", 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-v12-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cstr/Spaetzle-v12-7b"
# 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-v12-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cstr/Spaetzle-v12-7b

SGLang

How to use cstr/Spaetzle-v12-7b 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-v12-7b" \
    --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-v12-7b",
		"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-v12-7b" \
        --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-v12-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use cstr/Spaetzle-v12-7b with Docker Model Runner:
```
docker model run hf.co/cstr/Spaetzle-v12-7b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Spaetzle-v12-7b

Spaetzle-v12-7b is a merge of the following models using LazyMergekit:

As expected, this is a little bit worse in general English tasks over cstr/spaetzle-v8-7b, but a tiny little bit better on German tasks, at least some: e.g. it reaches an EQ-Bench (de) score of 64.81, but only

Metric	Value
Avg.	69.36
AI2 Reasoning Challenge (25-Shot)	65.96
HellaSwag (10-Shot)	86.16
MMLU (5-Shot)	63.48
TruthfulQA (0-shot)	57.84
Winogrande (5-shot)	80.03
GSM8k (5-shot)	62.70

Model	AGIEval	GPT4All	TruthfulQA	Bigbench	Average
Spaetzle-v12-7b	42.64	74.3	58.44	44.44	54.95

AGIEval

Task	Version	Metric	Value		Stderr
agieval_aqua_rat	0	acc	24.02	±	2.69
		acc_norm	21.65	±	2.59
agieval_logiqa_en	0	acc	36.10	±	1.88
		acc_norm	37.63	±	1.90
agieval_lsat_ar	0	acc	24.35	±	2.84
		acc_norm	23.04	±	2.78
agieval_lsat_lr	0	acc	48.82	±	2.22
		acc_norm	47.25	±	2.21
agieval_lsat_rc	0	acc	60.59	±	2.98
		acc_norm	57.99	±	3.01
agieval_sat_en	0	acc	76.21	±	2.97
		acc_norm	74.76	±	3.03
agieval_sat_en_without_passage	0	acc	46.60	±	3.48
		acc_norm	45.63	±	3.48
agieval_sat_math	0	acc	37.27	±	3.27
		acc_norm	33.18	±	3.18

Average: 42.64%

GPT4All

Task	Version	Metric	Value		Stderr
arc_challenge	0	acc	59.13	±	1.44
		acc_norm	61.26	±	1.42
arc_easy	0	acc	83.67	±	0.76
		acc_norm	80.89	±	0.81
boolq	1	acc	87.83	±	0.57
hellaswag	0	acc	66.45	±	0.47
		acc_norm	84.63	±	0.36
openbookqa	0	acc	37.40	±	2.17
		acc_norm	45.80	±	2.23
piqa	0	acc	82.15	±	0.89
		acc_norm	83.13	±	0.87
winogrande	0	acc	76.56	±	1.19

Average: 74.3%

TruthfulQA

Task	Version	Metric	Value		Stderr
truthfulqa_mc	1	mc1	42.59	±	1.73
		mc2	58.44	±	1.58

Average: 58.44%

Bigbench

Task	Version	Metric	Value		Stderr
bigbench_causal_judgement	0	multiple_choice_grade	55.26	±	3.62
bigbench_date_understanding	0	multiple_choice_grade	64.77	±	2.49
bigbench_disambiguation_qa	0	multiple_choice_grade	37.60	±	3.02
bigbench_geometric_shapes	0	multiple_choice_grade	32.31	±	2.47
		exact_str_match	21.45	±	2.17
bigbench_logical_deduction_five_objects	0	multiple_choice_grade	31.00	±	2.07
bigbench_logical_deduction_seven_objects	0	multiple_choice_grade	22.43	±	1.58
bigbench_logical_deduction_three_objects	0	multiple_choice_grade	53.00	±	2.89
bigbench_movie_recommendation	0	multiple_choice_grade	40.40	±	2.20
bigbench_navigate	0	multiple_choice_grade	51.30	±	1.58
bigbench_reasoning_about_colored_objects	0	multiple_choice_grade	68.50	±	1.04
bigbench_ruin_names	0	multiple_choice_grade	48.66	±	2.36
bigbench_salient_translation_error_detection	0	multiple_choice_grade	30.36	±	1.46
bigbench_snarks	0	multiple_choice_grade	70.17	±	3.41
bigbench_sports_understanding	0	multiple_choice_grade	70.39	±	1.45
bigbench_temporal_sequences	0	multiple_choice_grade	31.00	±	1.46
bigbench_tracking_shuffled_objects_five_objects	0	multiple_choice_grade	21.44	±	1.16
bigbench_tracking_shuffled_objects_seven_objects	0	multiple_choice_grade	18.29	±	0.92
bigbench_tracking_shuffled_objects_three_objects	0	multiple_choice_grade	53.00	±	2.89

Average: 44.44%

Average score: 54.95%

Elapsed time: 02:50:51

🧩 Configuration

models:
  - model: mayflowergmbh/Wiedervereinigung-7b-dpo-laser
    # no parameters necessary for base model
  - model: flemmingmiguel/NeuDist-Ro-7B
    parameters:
      density: 0.60
      weight: 0.30
  - model: Blizado/discolm-mfto-7b-german-v0.1
    parameters:
      density: 0.65
      weight: 0.40
  - model: ResplendentAI/Flora_DPO_7B
    parameters:
      density: 0.6
      weight: 0.3
merge_method: dare_ties
base_model: mayflowergmbh/Wiedervereinigung-7b-dpo-laser
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-v12-7b"
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. All 3 constituent models this card names are still published, so the chain can be followed from here.

Licence — resolved 2026-08-02. cc-by-sa-4.0. ResplendentAI/Flora_DPO_7B is CC-BY-SA-4.0. This card previously declared apache-2.0, which ShareAlike does not permit — Apache-2.0 does not satisfy the obligation to license derivatives alike.

This was derived from the mergekit configuration reproduced in this card by resolving each named constituent's licence on the Hub and taking the most restrictive, rather than assumed from the model family. An earlier revision of this section said the terms were unresolved; they are resolved now, and the method is recorded so the conclusion can be checked rather than trusted.