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

Libraries

How to use cstr/Spaetzle-v69-7b-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="cstr/Spaetzle-v69-7b-GGUF",
	filename="Spaetzle-v69-7b-q4-k-m.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-v69-7b-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-v69-7b-GGUF
# Run inference directly in the terminal:
llama-cli -hf cstr/Spaetzle-v69-7b-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf cstr/Spaetzle-v69-7b-GGUF
# Run inference directly in the terminal:
llama-cli -hf cstr/Spaetzle-v69-7b-GGUF

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-v69-7b-GGUF
# Run inference directly in the terminal:
./llama-cli -hf cstr/Spaetzle-v69-7b-GGUF

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-v69-7b-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf cstr/Spaetzle-v69-7b-GGUF

Use Docker

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

LM Studio
Jan
Ollama
How to use cstr/Spaetzle-v69-7b-GGUF with Ollama:
```
ollama run hf.co/cstr/Spaetzle-v69-7b-GGUF
```

Unsloth Studio new

How to use cstr/Spaetzle-v69-7b-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-v69-7b-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-v69-7b-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-v69-7b-GGUF to start chatting

Docker Model Runner
How to use cstr/Spaetzle-v69-7b-GGUF with Docker Model Runner:
```
docker model run hf.co/cstr/Spaetzle-v69-7b-GGUF
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull cstr/Spaetzle-v69-7b-GGUF

Run and chat with the model

lemonade run user.Spaetzle-v69-7b-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

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 an unquantized version.

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).

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

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

The merge tree in total involves to following original models:

🧩 Configuration

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"])