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

Libraries

How to use mayflowergmbh/Brezn-7b with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mayflowergmbh/Brezn-7b")
model = AutoModelForCausalLM.from_pretrained("mayflowergmbh/Brezn-7b")
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 mayflowergmbh/Brezn-7b with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/mayflowergmbh/Brezn-7b

SGLang

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

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

🥨 Brezn-7B

This is right now our best performing german speaking 7B model with an apache license, with an average of 7.49 on mt-bench-de. You can test this model here: mayflowergmbh/Brezn-7B-GGUF-Chat.

Brezn-7B is a dpo aligned merge of the following models using LazyMergekit:

💻 Usage

In order to leverage instruction fine-tuning, your prompt should be surrounded by [INST] and [/INST] tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.

E.g.

text = "<s>[INST] What is your favourite condiment? [/INST]"
"Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> "
"[INST] Do you have mayonnaise recipes? [/INST]"

This format is available as a chat template via the apply_chat_template() method:

from transformers import AutoModelForCausalLM, AutoTokenizer

device = "cuda" # the device to load the model onto

model = AutoModelForCausalLM.from_pretrained("mayflowergmbh/Brezn-7b")
tokenizer = AutoTokenizer.from_pretrained("mayflowergmbh/Brezn-7b")

messages = [
    {"role": "user", "content": "Was ist dein Lieblingsgewürz??"},
    {"role": "assistant", "content": "Nun, ich mag besonders gerne einen guten Spritzer frischen Zitronensaft. Er fügt genau die richtige Menge an würzigem Geschmack hinzu, egal was ich gerade in der Küche zubereite!"},
    {"role": "user", "content": "Hast du Mayonnaise-Rezepte?"}
]

encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")

model_inputs = encodeds.to(device)
model.to(device)

generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])

mt-bench-de

{
    "first_turn": 7.6625,
    "second_turn": 7.31875,
    "categories": {
        "writing": 8.75,
        "roleplay": 8.5,
        "reasoning": 6.1,
        "math": 5.05,
        "coding": 5.4,
        "extraction": 7.975,
        "stem": 9,
        "humanities": 9.15
    },
    "average": 7.490625
}

🧩 Configuration

models:
  - model: mistralai/Mistral-7B-v0.1
    # no parameters necessary for base model
  - model: FelixChao/WestSeverus-7B-DPO-v2
    parameters:
      density: 0.60
      weight: 0.30
  - model: mayflowergmbh/Wiedervereinigung-7b-dpo-laser
    parameters:
      density: 0.65
      weight: 0.40
  - model: cognitivecomputations/openchat-3.5-0106-laser
    parameters:
      density: 0.6
      weight: 0.3
merge_method: dare_ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
  int8_mask: true
dtype: bfloat16
random_seed: 0