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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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license: apache-2.0 |
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datasets: |
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- mayflowergmbh/intel_orca_dpo_pairs_de |
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language: |
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- de |
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--- |
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# Wiedervereinigung-7b-dpo-laser |
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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, 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). |
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After that this model got a [laserRMT](https://github.com/cognitivecomputations/laserRMT) treatment with german datasets. |
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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 seems to help. |
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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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``` |
