Instructions to use cstr/Spaetzle-v60-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cstr/Spaetzle-v60-7b with Transformers:

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

pipe = pipeline("text-generation", model="cstr/Spaetzle-v60-7b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("cstr/Spaetzle-v60-7b")
model = AutoModelForCausalLM.from_pretrained("cstr/Spaetzle-v60-7b", 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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use cstr/Spaetzle-v60-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cstr/Spaetzle-v60-7b"
# 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-v60-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cstr/Spaetzle-v60-7b

SGLang

How to use cstr/Spaetzle-v60-7b 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-v60-7b" \
    --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-v60-7b",
		"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-v60-7b" \
        --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-v60-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use cstr/Spaetzle-v60-7b with Docker Model Runner:
```
docker model run hf.co/cstr/Spaetzle-v60-7b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Spaetzle-v60-7b

This is a progressive (mostly dare-ties, but also slerp i.a.) merge with the intention of suitable compromise for English and German local tasks.

Spaetzle-v60-7b is a merge of the following models using LazyMergekit:

Benchmarks

The performance looks ok so far: e.g. we get in EQ-Bench: Score (v2_de): 65.08 (Parseable: 171.0).

From the Occiglot Euro LLM Leaderboard:

Model	DE	EN	ARC EN	TruthfulQA EN	Belebele EN	HellaSwag EN	MMLU EN	ARC DE	TruthfulQA DE	Belebele DE	HellaSwag DE	MMLU DE
mistral-community/Mixtral-8x22B-v0.1	66.81	72.87	70.56	52.29	93.89	70.41	77.17	63.9	29.31	92.44	77.9	70.49
cstr/Spaetzle-v60-7b	60.95	71.65	69.88	66.24	90.11	68.43	63.59	58	37.31	84.22	70.09	55.11
VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct	60.07	74.71	74.49	66.19	91.67	74.55	66.65	59.37	29.57	88.56	66.43	56.44
occiglot/occiglot-7b-de-en-instruct	56.65	61.7	60.41	49.38	81.22	60.43	57.06	54.49	31.09	77.22	68.84	51.59
occiglot/occiglot-7b-de-en	54.01	58.78	55.63	42.33	79.11	59.99	56.84	50.56	26.27	74.33	67.42	51.46
meta-llama/Meta-Llama-3-8B	53.89	63.08	58.02	43.87	86.44	61.75	65.3	46.45	24.24	81.11	62.48	55.18
mistralai/Mistral-7B-Instruct-v0.2	53.52	67.63	63.74	66.81	82.44	65.96	59.2	48.59	37.69	68.89	62.24	50.2
occiglot/occiglot-7b-eu5-instruct	53.15	57.78	55.89	44.9	74.67	59.92	53.51	52.95	28.68	66.78	68.52	48.82
clibrain/lince-mistral-7b-it-es	52.98	62.43	62.46	43.32	82.44	63.86	60.06	49.44	28.17	75	61.64	50.64
mistralai/Mistral-7B-v0.1	52.8	62.73	61.26	42.62	84.44	62.89	62.46	47.65	28.43	73.89	61.06	52.96
LeoLM/leo-mistral-hessianai-7b	51.78	56.11	52.22	42.92	73.67	57.86	53.88	47.48	25.25	69.11	68.21	48.83

And for the int4-inc quantized version, from Low-bit Quantized Open LLM Leaderboard:

Type	Model	Average ⬆️	ARC-c	ARC-e	Boolq	HellaSwag	Lambada	MMLU	Openbookqa	Piqa	Truthfulqa	Winogrande	#Params (B)	#Size (G)
🍒	Intel/SOLAR-10.7B-Instruct-v1.0-int4-inc	68.49	60.49	82.66	88.29	68.29	73.36	62.43	35.6	80.74	56.06	76.95	10.57	5.98
🍒	cstr/Spaetzle-v60-7b-int4-inc	68.01	62.12	85.27	87.34	66.43	70.58	61.39	37	82.26	50.18	77.51	7.04	4.16
🔷	TheBloke/SOLAR-10.7B-Instruct-v1.0-GGUF	66.6	60.41	83.38	88.29	67.73	52.42	62.04	37.2	82.32	56.3	75.93	10.73	6.07
🔷	cstr/Spaetzle-v60-7b-Q4_0-GGUF	66.44	61.35	85.19	87.98	66.54	52.78	62.05	40.6	81.72	47	79.16	7.24	4.11
🍒	Intel/Mistral-7B-Instruct-v0.2-int4-inc	65.73	55.38	81.44	85.26	65.67	70.89	58.66	34.2	80.74	51.16	73.95	7.04	4.16
🍒	Intel/Phi-3-mini-4k-instruct-int4-inc	65.09	57.08	83.33	86.18	59.45	68.14	66.62	38.6	79.33	38.68	73.48	3.66	2.28
🔷	TheBloke/Mistral-7B-Instruct-v0.2-GGUF	63.52	53.5	77.9	85.44	66.9	50.11	58.45	38.8	77.58	53.12	73.4	7.24	4.11
🍒	Intel/Meta-Llama-3-8B-Instruct-int4-inc	62.93	51.88	81.1	83.21	57.09	71.32	62.41	35.2	78.62	36.35	72.14	7.2	5.4

Contamination check results (reference model: Mistral instruct 7b v0.1):

MMLU: result < 0.1, %: 0.19
TruthfulQA: result < 0.1, %: 0.34
GSM8k: result < 0.1, %: 0.39

🧩 Configuration

models:
  - model: cstr/Spaetzle-v58-7b
    # 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-v58-7b
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-v60-7b"
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 1 constituent models this card names are still published, so the chain can be followed from here.