Instructions to use QuantFactory/Llama-3-SauerkrautLM-8b-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use QuantFactory/Llama-3-SauerkrautLM-8b-Instruct-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf QuantFactory/Llama-3-SauerkrautLM-8b-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Llama-3-SauerkrautLM-8b-Instruct-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf QuantFactory/Llama-3-SauerkrautLM-8b-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Llama-3-SauerkrautLM-8b-Instruct-GGUF:Q4_K_M

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 QuantFactory/Llama-3-SauerkrautLM-8b-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Llama-3-SauerkrautLM-8b-Instruct-GGUF:Q4_K_M

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 QuantFactory/Llama-3-SauerkrautLM-8b-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Llama-3-SauerkrautLM-8b-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Llama-3-SauerkrautLM-8b-Instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/Llama-3-SauerkrautLM-8b-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/Llama-3-SauerkrautLM-8b-Instruct-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/Llama-3-SauerkrautLM-8b-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/Llama-3-SauerkrautLM-8b-Instruct-GGUF:Q4_K_M

Ollama
How to use QuantFactory/Llama-3-SauerkrautLM-8b-Instruct-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Llama-3-SauerkrautLM-8b-Instruct-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use QuantFactory/Llama-3-SauerkrautLM-8b-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Llama-3-SauerkrautLM-8b-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Llama-3-SauerkrautLM-8b-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Llama-3-SauerkrautLM-8b-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Llama-3-SauerkrautLM-8b-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

QuantFactory/Llama-3-SauerkrautLM-8b-Instruct-GGUF

This is quantized version of VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct created using llama.cpp

Model Description

VAGO solutions Llama-3-SauerkrautLM-8b-Instruct

Introducing Llama-3-SauerkrautLM-8b-Instruct – our Sauerkraut version of the powerful meta-llama/Meta-Llama-3-8B-Instruct!

The model Llama-3-SauerkrautLM-8b-Instruct is a joint effort between VAGO Solutions and Hyperspace.ai.

Aligned with DPO

Overview of all Llama-3-SauerkrautLM-8b-Instruct
Model Details
- Prompt template
- Training procedure
Evaluation
Disclaimer
Contact
Collaborations
Acknowledgement

All SauerkrautLM-llama-3-8B-Instruct

Model	HF	EXL2	GGUF	AWQ
Llama-3-SauerkrautLM-8b-Instruct	Link	Link	Link	coming soon

Model Details

SauerkrautLM-llama-3-8B-Instruct

Model Type: Llama-3-SauerkrautLM-8b-Instruct is a finetuned Model based on meta-llama/Meta-Llama-3-8B-Instruct
Language(s): German, English
License: meta-llama
Contact: VAGO solutions, Hyperspace.ai

Training procedure:

We trained this model in a two staged DPO Fine-Tuning for 1 epoch with 70k data and another epoch with 20k data.
LaserRMT version coming soon

We improved the model's capabilities noticably by feeding it with curated German data.

Prompt Template:

English:

<|begin_of_text|><|start_header_id|>system<|end_header_id|>

You are a helpful AI assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>

Input<|eot_id|><|start_header_id|>assistant<|end_header_id|>

German:

<|begin_of_text|><|start_header_id|>system<|end_header_id|>

Du bist ein freundlicher und hilfreicher deutscher KI-Assistent.<|eot_id|><|start_header_id|>user<|end_header_id|>

Input<|eot_id|><|start_header_id|>assistant<|end_header_id|>

Example output of german language:


Prompt: Erkläre mir die Vorteile für das Fine-Tuning von Meta's LLama Modellen.

Antwort:
Sehr gern!

Fine-tuning von Meta's LLaMA-Modellen ist eine sehr effektive Möglichkeit, um Ihre Anwendung oder Ihr Projekt mit einer hochentwickelten Sprachverarbeitung auszustatten.

Die Vorteile des Fine-Tunings von LLaMA-Modellen sind:

Adaptation an spezielle Aufgaben: Durch das Fine-Tuning können Sie die Modelle auf Ihre speziellen Bedürfnisse und Anforderungen anpassen.
Dies ermöglicht es Ihnen, die Leistung der Modelle in Ihren Anwendungen zu verbessern.

Verbesserte Genauigkeit: Die fine-tuned Modelle lernen, sich an die spezifischen Merkmale Ihres Datenbestands anzupassen, was zu einer besseren Genauigkeit bei der Textklassifizierung, -generierung, -summarisierung usw. führt.

Anpassung an Branche oder Domain: Wenn Sie beispielsweise ein Medizinprojekt haben, können Sie die Modelle auf medizinische Fachterminologie und -konzepte trainieren, um eine höhere Präzision bei der Analyse medizinischer Texte zu erzielen.

Kosten-Effektivität: Im Vergleich zum Training neuer Modelle von Grund auf kann das Fine-Tuning viel kosteneffektiver sein, da Sie nur einen kleinen Teil des Trainingsdatums benötigen, um die Modelle an Ihre spezifischen Anforderungen anzupassen.

Schnellere Entwicklung: Das Fine-Tuning ermöglicht es Entwicklern, schnell neue Funktionen und Features in ihre Anwendungen einzufügen, ohne dass sie lange warten müssen, bis neue Modelle trainiert werden.

Bessere Interpretierbarkeit: Da die fine-tuned Modelle auf Ihre spezifischen Daten bestimmt wurden, können Sie leichter verstehen, warum bestimmte Entscheidungen getroffen werden, was wichtig ist, wenn es um Transparenz und Verantwortlichkeit geht.

Insgesamt bietet das Fine-Tuning von LLaMA-Modellen eine flexible und effektive Möglichkeit, um Ihre Anwendungen und Projekte durch die Integration von fortschrittlichen Sprachmodellen zu verbessern.

Evaluation

Open LLM Leaderboard:

evaluated with lm-evaluation-benchmark-harness 0.4.2

Metric	Value
Avg.	74.57
ARC (25-shot)	74.66
HellaSwag (10-shot)	89.60
MMLU (5-shot)	66.55
TruthfulQA (0-shot)	66.32
Winogrande (5-shot)	80.98
GSM8K (5-shot)	69.29

MT-Bench English

########## First turn ########## 
                                         score 
model                            turn 
Llama-3-SauerkrautLM-8b-Instruct 1     8.15625 
  
########## Second turn ########## 
                                       score 
model                            turn 
Llama-3-SauerkrautLM-8b-Instruct 2      7.65 
  
########## Average ########## 
                                     score 
model 
Llama-3-SauerkrautLM-8b-Instruct  7.903125 *

due to specific instruction training the english MT-Bench score is slightly lower than the original LLama-3-8B-Instruct

MT-Bench German

########## First turn ##########
                                       score
model                            turn
Llama-3-SauerkrautLM-8b-Instruct 1     7.675

########## Second turn ##########
                                        score
model                            turn
Llama-3-SauerkrautLM-8b-Instruct 2     7.6375

########## Average ##########
                                    score
model
Llama-3-SauerkrautLM-8b-Instruct  7.65625

German RAG LLM Evaluation corrected result after FIX: https://github.com/huggingface/lighteval/pull/171

|                         Task                         |Version|Metric|Value|   |Stderr|
|------------------------------------------------------|------:|------|----:|---|-----:|
|all                                                   |       |acc   |0.910|±  |0.0084|
|community:german_rag_eval:_average:0                  |       |acc   |0.910|±  |0.0084|
|community:german_rag_eval:choose_context_by_question:0|      0|acc   |0.928|±  |0.0082|
|community:german_rag_eval:choose_question_by_context:0|      0|acc   |0.824|±  |0.0120|
|community:german_rag_eval:context_question_match:0    |      0|acc   |0.982|±  |0.0042|
|community:german_rag_eval:question_answer_match:0     |      0|acc   |0.906|±  |0.0092|

Disclaimer

We must inform users that despite our best efforts in data cleansing, the possibility of uncensored content slipping through cannot be entirely ruled out. However, we cannot guarantee consistently appropriate behavior. Therefore, if you encounter any issues or come across inappropriate content, we kindly request that you inform us through the contact information provided. Additionally, it is essential to understand that the licensing of these models does not constitute legal advice. We are not held responsible for the actions of third parties who utilize our models.

Model Contact

If you are interested in customized LLMs for business applications, please get in contact with us via our websites. We are also grateful for your feedback and suggestions.

Model Collaborations

We are also keenly seeking support and investment for our startups, VAGO solutions and Hyperspace where we continuously advance the development of robust language models designed to address a diverse range of purposes and requirements. If the prospect of collaboratively navigating future challenges excites you, we warmly invite you to reach out to us at VAGO solutions, Hyperspace.computer

Model Acknowledgement

Many thanks to Meta for providing such valuable model to the Open-Source community. Also many thanks to bartowski for super fast quantification of our Model in GGUF and EXL format.

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GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

2-bit

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8-bit

Model tree for QuantFactory/Llama-3-SauerkrautLM-8b-Instruct-GGUF

Base model

VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct

Quantized

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this model

QuantFactory
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Llama-3-SauerkrautLM-8b-Instruct-GGUF