---
tags:
- merge
- mergekit
- lazymergekit
- mlabonne/OmniTruthyBeagle-7B-v0
- mayflowergmbh/Wiedervereinigung-7b-dpo-laser
- cognitivecomputations/openchat-3.5-0106-laser
base_model:
- mlabonne/OmniTruthyBeagle-7B-v0
- mayflowergmbh/Wiedervereinigung-7b-dpo-laser
- cognitivecomputations/openchat-3.5-0106-laser
license: cc-by-nc-4.0
language:
- de
---

# Wiederchat-7b

Wiederchat-7b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [mlabonne/OmniTruthyBeagle-7B-v0](https://huggingface.co/mlabonne/OmniTruthyBeagle-7B-v0)
* [mayflowergmbh/Wiedervereinigung-7b-dpo-laser](https://huggingface.co/mayflowergmbh/Wiedervereinigung-7b-dpo-laser)
* [cognitivecomputations/openchat-3.5-0106-laser](https://huggingface.co/cognitivecomputations/openchat-3.5-0106-laser)

# Benchmark mt-bench-de
Even before dpo-alignment this model performs quite good: 
```json
{
    "first_turn": 7.46875,
    "second_turn": 6.7875,
    "categories": {
        "writing": 8.55,
        "roleplay": 8,
        "reasoning": 5.3,
        "math": 4.35,
        "coding": 4.6,
        "extraction": 8.4,
        "stem": 8.575,
        "humanities": 9.25
    },
    "average": 7.128125
}

```


## 🧩 Configuration

```yaml
models:
  - model: mistralai/Mistral-7B-v0.1
    # no parameters necessary for base model
  - model: mlabonne/OmniTruthyBeagle-7B-v0
    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
```

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "johannhartmann/Wiederchat-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"])
```