---
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"])
```