Create README.md

README.md

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+---
+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
+---
+
+# Wiedervereinigung-7b-dpo-laser-AWQ
+
+![image/png](https://huggingface.co/mayflowergmbh/Wiedervereinigung-7b/resolve/main/Wiedervereinigung-7b.png)
+
+Some of the best german models with 7b parameters as lasered dpo-trained dare_ties merge, quantized using awq.
+
+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. 
+Hence the name, no right wing or nationalistic ideas involved :-). To improve the result quality they are dpo-trained with a german translation of intel-orca-dpo using our german fork of [LLaMA-Factory](https://github.com/mayflower/LLaMA-Factory).
+After that this model got a [laserRMT](https://github.com/cognitivecomputations/laserRMT) treatment with german datasets.
+
+Wiedervereinigung-7b itself is a  [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing) merge of:
+* [DiscoResearch/DiscoLM_German_7b_v1](https://huggingface.co/DiscoResearch/DiscoLM_German_7b_v1)
+* [DRXD1000/Phoenix](https://huggingface.co/DRXD1000/Phoenix)
+* [VAGOsolutions/SauerkrautLM-7b-v1-mistral](https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-v1-mistral)
+* [malteos/hermeo-7b](https://huggingface.co/malteos/hermeo-7b)
+
+All the actual heavylifting has been done by the creators of these models.
+
+## 🧩 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
+```
+
+## mt-bench-de
+
+Using laser and dpo results seems to help.
+
+```json
+{
+    "first_turn": 7.3,
+    "second_turn": 6.6,
+    "categories": {
+        "writing": 8.6,
+        "roleplay": 8.1,
+        "reasoning": 5.25,
+        "math": 3.7,
+        "coding": 4.35,
+        "extraction": 8.15,
+        "stem": 8.875,
+        "humanities": 8.875
+    },
+    "average": 6.97
+}
+
+```
+
+## 💻 Usage
+
+```python
+!pip install -qU transformers accelerate
+
+from transformers import AutoTokenizer
+import transformers
+import torch
+
+model = "mayflowergmbh/Wiedervereinigung-7b-dpo-laser"
+messages = [{"role": "user", "content": "Was ist ein 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"])
+```