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Wiedervereinigung-7b-dpo-laser/README.md
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---
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tags:
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- merge
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- mergekit
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- lazymergekit
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- DiscoResearch/DiscoLM_German_7b_v1
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- DRXD1000/Phoenix
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- VAGOsolutions/SauerkrautLM-7b-v1-mistral
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- malteos/hermeo-7b
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base_model:
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- DiscoResearch/DiscoLM_German_7b_v1
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- DRXD1000/Phoenix
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- VAGOsolutions/SauerkrautLM-7b-v1-mistral
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- malteos/hermeo-7b
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---
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# Wiedervereinigung-7b-dpo-laser
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![image/png](https://huggingface.co/mayflowergmbh/Wiedervereinigung-7b/resolve/main/Wiedervereinigung-7b.png)
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Some of the best german models with 7b parameters as lasered dpo-trained dare_ties merge.
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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.
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Hence the name. To improve result quality they are dpo-trained with a german translation of oaast-dpo using our german fork of [LLaMA-Factory](https://github.com/mayflower/LLaMA-Factory).
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After that this model got a [laserRMT](https://github.com/cognitivecomputations/laserRMT) treatment.
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Wiedervereinigung-7b itself is a [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing) merge of:
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* [DiscoResearch/DiscoLM_German_7b_v1](https://huggingface.co/DiscoResearch/DiscoLM_German_7b_v1)
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* [DRXD1000/Phoenix](https://huggingface.co/DRXD1000/Phoenix)
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* [VAGOsolutions/SauerkrautLM-7b-v1-mistral](https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-v1-mistral)
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* [malteos/hermeo-7b](https://huggingface.co/malteos/hermeo-7b)
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All the actual heavylifting has been done by the creators of these models.
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## 🧩 Configuration
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```yaml
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models:
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- model: LeoLM/leo-mistral-hessianai-7b
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# No parameters necessary for base model
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- model: DiscoResearch/DiscoLM_German_7b_v1
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parameters:
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density: 0.6
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weight: 0.25
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- model: DRXD1000/Phoenix
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parameters:
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density: 0.6
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weight: 0.25
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- model: VAGOsolutions/SauerkrautLM-7b-v1-mistral
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parameters:
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density: 0.6
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weight: 0.25
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- model: malteos/hermeo-7b
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parameters:
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density: 0.6
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weight: 0.25
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merge_method: dare_ties
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base_model: LeoLM/leo-mistral-hessianai-7b
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parameters:
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int8_mask: true
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dtype: bfloat16
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```
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## mt-bench-de
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Using laser and dpo results in pretty good results.
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```json
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{
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"first_turn": 7.51875,
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"second_turn": 6.4,
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"categories": {
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"writing": 8.425,
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"roleplay": 8.025,
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"reasoning": 5.45,
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"math": 3.2,
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"coding": 4.95,
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"extraction": 7.525,
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"stem": 8.775,
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"humanities": 9.325
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},
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"average": 6.959375
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}
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```
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## 💻 Usage
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```python
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!pip install -qU transformers accelerate
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from transformers import AutoTokenizer
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import transformers
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import torch
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model = "mayflowergmbh/Wiedervereinigung-7b-dpo-laser"
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messages = [{"role": "user", "content": "Was ist ein large language model?"}]
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tokenizer = AutoTokenizer.from_pretrained(model)
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prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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pipeline = transformers.pipeline(
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"text-generation",
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model=model,
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torch_dtype=torch.float16,
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device_map="auto",
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)
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outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
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print(outputs[0]["generated_text"])
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```
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Wiedervereinigung-7b-dpo-laser/added_tokens.json
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{
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"</s>": 2,
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"<s>": 1,
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"<unk>": 0
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}
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Wiedervereinigung-7b-dpo-laser/config.json
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{
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"_name_or_path": "mayflowergmbh/Wiedervereinigung-7b-dpo",
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"architectures": [
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"MistralForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings": 32768,
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"model_type": "mistral",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"rms_norm_eps": 1e-05,
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"rope_theta": 10000.0,
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"sliding_window": 4096,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.34.0",
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"use_cache": true,
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"vocab_size": 32000
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}
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Wiedervereinigung-7b-dpo-laser/generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"transformers_version": "4.34.0"
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}
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Wiedervereinigung-7b-dpo-laser/model.safetensors.index.json
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