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RichardErkhov/
mayflowergmbh_-_Wiedervereinigung-7b-dpo-gguf

Name: mayflowergmbh_-_Wiedervereinigung-7b-dpo-gguf
Availability: InStock

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Quantization made by Richard Erkhov.

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Wiedervereinigung-7b-dpo - GGUF

Model creator: https://huggingface.co/mayflowergmbh/
Original model: https://huggingface.co/mayflowergmbh/Wiedervereinigung-7b-dpo/

Name	Quant method	Size
Wiedervereinigung-7b-dpo.Q2_K.gguf	Q2_K	2.53GB
Wiedervereinigung-7b-dpo.IQ3_XS.gguf	IQ3_XS	2.81GB
Wiedervereinigung-7b-dpo.IQ3_S.gguf	IQ3_S	2.96GB
Wiedervereinigung-7b-dpo.Q3_K_S.gguf	Q3_K_S	2.95GB
Wiedervereinigung-7b-dpo.IQ3_M.gguf	IQ3_M	3.06GB
Wiedervereinigung-7b-dpo.Q3_K.gguf	Q3_K	3.28GB
Wiedervereinigung-7b-dpo.Q3_K_M.gguf	Q3_K_M	3.28GB
Wiedervereinigung-7b-dpo.Q3_K_L.gguf	Q3_K_L	3.56GB
Wiedervereinigung-7b-dpo.IQ4_XS.gguf	IQ4_XS	3.67GB
Wiedervereinigung-7b-dpo.Q4_0.gguf	Q4_0	3.83GB
Wiedervereinigung-7b-dpo.IQ4_NL.gguf	IQ4_NL	3.87GB
Wiedervereinigung-7b-dpo.Q4_K_S.gguf	Q4_K_S	3.86GB
Wiedervereinigung-7b-dpo.Q4_K.gguf	Q4_K	4.07GB
Wiedervereinigung-7b-dpo.Q4_K_M.gguf	Q4_K_M	4.07GB
Wiedervereinigung-7b-dpo.Q4_1.gguf	Q4_1	4.24GB
Wiedervereinigung-7b-dpo.Q5_0.gguf	Q5_0	4.65GB
Wiedervereinigung-7b-dpo.Q5_K_S.gguf	Q5_K_S	4.65GB
Wiedervereinigung-7b-dpo.Q5_K.gguf	Q5_K	4.78GB
Wiedervereinigung-7b-dpo.Q5_K_M.gguf	Q5_K_M	4.78GB
Wiedervereinigung-7b-dpo.Q5_1.gguf	Q5_1	5.07GB
Wiedervereinigung-7b-dpo.Q6_K.gguf	Q6_K	5.53GB
Wiedervereinigung-7b-dpo.Q8_0.gguf	Q8_0	7.17GB

Original model description:

tags:

merge
mergekit
lazymergekit
DiscoResearch/DiscoLM_German_7b_v1
DRXD1000/Phoenix
VAGOsolutions/SauerkrautLM-7b-v1-mistral
malteos/hermeo-7b base_model:
DiscoResearch/DiscoLM_German_7b_v1
DRXD1000/Phoenix
VAGOsolutions/SauerkrautLM-7b-v1-mistral
malteos/hermeo-7b license: apache-2.0 language:
de
en

Wiedervereinigung-7b-dpo

This is a dpo aligned merge of our favourite german models, scoring 7.11 on the mt-bench-de average. Since the original models based on mistral - three of them on the brilliant german LeoLM/leo-mistral-hessianai-7b - they are reunited in this merged model. Therefore the name, no nationalist ideas involved :-).

To improve result quality they are dpo-trained with a german translation of slimorca dpo using hermeo-7B for reject results.

If you are gpu-poor like me you can now use LLaMA-Factory to train with german datasets.

Kudos to the authors of the original models at DiscoResearch and VAGOsolutions, Malte Ostendorff and Matthias Uhlig. We are your fan club.

This model was brought to you and the nvidia bill was paid by Mayflower GmbH.

Benchmark results: mt-bench-de

Is the merged model alone already good? Well, of course. But it is even better with the help of some dpo tuning.

json

{
    "first_turn": 7.3,
    "second_turn": 6.925,
    "categories": {
        "writing": 8.425,
        "roleplay": 8.6,
        "reasoning": 5.4,
        "math": 4.35,
        "coding": 4.3,
        "extraction": 7.975,
        "stem": 8.5,
        "humanities": 9.35
    },
    "average": 7.1125
}

Other Versions

A big thank you to LoneStriker for the quantized models.

Name	Quant method	Bits
Wiedervereinigung-7b-dpo	Unquantized	16
Wiedervereinigung-7b-dpo-GPTQ	GPTQ	4
Wiedervereinigung-7b-dpo-AWQ	AWQ	4
Wiedervereinigung-7b-dpo-GGUF	GGUF	3-8
Wiedervereinigung-7b-dpo-8.0bpw-h8-exl2	EXL2	8
Wiedervereinigung-7b-dpo-6.0bpw-h6-exl2	EXL2	6
Wiedervereinigung-7b-dpo-5.0bpw-h6-exl2	EXL2	5
Wiedervereinigung-7b-dpo-4.0bpw-h6-exl2	EXL2	4
Wiedervereinigung-7b-dpo-3.0bpw-h6-exl2	EXL2	3

Wiedervereinigung-7b is a LazyMergekit merge of:

🧩 Configuration

yaml

models:
  - model: LeoLM/leo-mistral-hessianai-7b
    # No parameters necessary for base model
  - model: DiscoResearch/DiscoLM_German_7b_v1
    parameters:
      density: 0.6
      weight: 0.25
  - model: DRXD1000/Phoenix
    parameters:
      density: 0.6
      weight: 0.25
  - model: VAGOsolutions/SauerkrautLM-7b-v1-mistral
    parameters:
      density: 0.6
      weight: 0.25
  - model: malteos/hermeo-7b
    parameters:
      density: 0.6
      weight: 0.25
merge_method: dare_ties
base_model: LeoLM/leo-mistral-hessianai-7b
parameters:
  int8_mask: true
dtype: bfloat16

💻 Usage

python

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "mayflowergmbh/Wiedervereinigung-7b-dpo"
messages = [{"role": "user", "content": "Was ist ein deutsches 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"])

RichardErkhov/mayflowergmbh_-_Wiedervereinigung-7b-dpo-gguf