Instructions to use cstr/Spaetzle-v60-7b-Q4_0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cstr/Spaetzle-v60-7b-Q4_0-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="cstr/Spaetzle-v60-7b-Q4_0-GGUF",
	filename="Spaetzle-v60-7b_Q4_0.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use cstr/Spaetzle-v60-7b-Q4_0-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf cstr/Spaetzle-v60-7b-Q4_0-GGUF:Q4_0
# Run inference directly in the terminal:
llama-cli -hf cstr/Spaetzle-v60-7b-Q4_0-GGUF:Q4_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf cstr/Spaetzle-v60-7b-Q4_0-GGUF:Q4_0
# Run inference directly in the terminal:
llama-cli -hf cstr/Spaetzle-v60-7b-Q4_0-GGUF:Q4_0

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf cstr/Spaetzle-v60-7b-Q4_0-GGUF:Q4_0
# Run inference directly in the terminal:
./llama-cli -hf cstr/Spaetzle-v60-7b-Q4_0-GGUF:Q4_0

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf cstr/Spaetzle-v60-7b-Q4_0-GGUF:Q4_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf cstr/Spaetzle-v60-7b-Q4_0-GGUF:Q4_0

Use Docker

docker model run hf.co/cstr/Spaetzle-v60-7b-Q4_0-GGUF:Q4_0

LM Studio
Jan
Ollama
How to use cstr/Spaetzle-v60-7b-Q4_0-GGUF with Ollama:
```
ollama run hf.co/cstr/Spaetzle-v60-7b-Q4_0-GGUF:Q4_0
```

Unsloth Studio new

How to use cstr/Spaetzle-v60-7b-Q4_0-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for cstr/Spaetzle-v60-7b-Q4_0-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for cstr/Spaetzle-v60-7b-Q4_0-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for cstr/Spaetzle-v60-7b-Q4_0-GGUF to start chatting

Docker Model Runner
How to use cstr/Spaetzle-v60-7b-Q4_0-GGUF with Docker Model Runner:
```
docker model run hf.co/cstr/Spaetzle-v60-7b-Q4_0-GGUF:Q4_0
```

Lemonade

How to use cstr/Spaetzle-v60-7b-Q4_0-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull cstr/Spaetzle-v60-7b-Q4_0-GGUF:Q4_0

Run and chat with the model

lemonade run user.Spaetzle-v60-7b-Q4_0-GGUF-Q4_0

List all available models

lemonade list

Spaetzle-v60-7b

This is 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