Instructions to use cstr/Spaetzle-v60-7b-int4-inc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cstr/Spaetzle-v60-7b-int4-inc with Transformers:

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

pipe = pipeline("text-generation", model="cstr/Spaetzle-v60-7b-int4-inc")
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-int4-inc")
model = AutoModelForCausalLM.from_pretrained("cstr/Spaetzle-v60-7b-int4-inc", 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-v60-7b-int4-inc 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-int4-inc"
# 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-int4-inc",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cstr/Spaetzle-v60-7b-int4-inc

SGLang

How to use cstr/Spaetzle-v60-7b-int4-inc 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-int4-inc" \
    --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-int4-inc",
		"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-int4-inc" \
        --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-int4-inc",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use cstr/Spaetzle-v60-7b-int4-inc with Docker Model Runner:
```
docker model run hf.co/cstr/Spaetzle-v60-7b-int4-inc
```

Spaetzle-v60-7b

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

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

Benchmarks

The performance looks ok so far: e.g. we get (for the GGUF q4) in EQ-Bench: Score (v2_de): 65.08 (Parseable: 171.0).

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

Provenance and EU AI Act Art. 53 note

Base model: cstr/Spaetzle-v60-7b — 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 (GPTQ, INT4 precision). 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-v60-7b, not here.