File size: 3,238 Bytes
8d225e9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 |
---
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
Some of the best german models with 7b parameters as lasered dpo-trained dare_ties merge.
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. 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).
After that this model got a [laserRMT](https://github.com/cognitivecomputations/laserRMT) treatment.
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 in pretty good results.
```json
{
"first_turn": 7.51875,
"second_turn": 6.4,
"categories": {
"writing": 8.425,
"roleplay": 8.025,
"reasoning": 5.45,
"math": 3.2,
"coding": 4.95,
"extraction": 7.525,
"stem": 8.775,
"humanities": 9.325
},
"average": 6.959375
}
```
## 💻 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"])
```
|