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---
tags:
- merge
- mergekit
- lazymergekit
- SanjiWatsuki/Silicon-Maid-7B
- chargoddard/loyal-piano-m7-cdpo
- jsfs11/RandomMergeNoNormWEIGHTED-7B-DARETIES
- NeverSleep/Noromaid-7b-v0.2
- athirdpath/NSFW_DPO_vmgb-7b
base_model:
- SanjiWatsuki/Silicon-Maid-7B
- chargoddard/loyal-piano-m7-cdpo
- jsfs11/RandomMergeNoNormWEIGHTED-7B-DARETIES
- NeverSleep/Noromaid-7b-v0.2
- athirdpath/NSFW_DPO_vmgb-7b
license: apache-2.0
---
# HighdensityRPMerge-7B
HighdensityRPMerge-7B is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [SanjiWatsuki/Silicon-Maid-7B](https://huggingface.co/SanjiWatsuki/Silicon-Maid-7B)
* [chargoddard/loyal-piano-m7-cdpo](https://huggingface.co/chargoddard/loyal-piano-m7-cdpo)
* [jsfs11/RandomMergeNoNormWEIGHTED-7B-DARETIES](https://huggingface.co/jsfs11/RandomMergeNoNormWEIGHTED-7B-DARETIES)
* [NeverSleep/Noromaid-7b-v0.2](https://huggingface.co/NeverSleep/Noromaid-7b-v0.2)
* [athirdpath/NSFW_DPO_vmgb-7b](https://huggingface.co/athirdpath/NSFW_DPO_vmgb-7b)
## 🧩 Configuration
```yaml
models:
- model: saishf/West-Hermes-7B
# no parameters necessary for base model
- model: SanjiWatsuki/Silicon-Maid-7B
parameters:
weight: 0.4
density: 0.8
- model: chargoddard/loyal-piano-m7-cdpo
parameters:
weight: 0.3
density: 0.8
- model: jsfs11/RandomMergeNoNormWEIGHTED-7B-DARETIES
parameters:
weight: 0.25
density: 0.45
- model: NeverSleep/Noromaid-7b-v0.2
parameters:
weight: 0.25
density: 0.4
- model: athirdpath/NSFW_DPO_vmgb-7b
parameters:
weight: 0.2
density: 0.4
merge_method: dare_ties
base_model: saishf/West-Hermes-7B
parameters:
int8_mask: true
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "jsfs11/HighdensityRPMerge-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"])
``` |