---
base_model:
- mistralai/Mistral-7B-Instruct-v0.2
- Endevor/InfinityRP-v1-7B
- mistralai/Mistral-7B-v0.1
- CalderaAI/Naberius-7B
- CalderaAI/Hexoteric-7B
- Endevor/EndlessRP-v3-7B
library_name: transformers
tags:
- mergekit
- merge
license: apache-2.0
pipeline_tag: text-generation
---
# merge
this is a model focused on roleplaying. please dont expect much from it in other areas. it will do its job as roleplaying.
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
careful it generates nsfw contents. whatever generated by you is your responsibility. ejoy it by roleplaying. cheers ☺️.
## Merge Details
### Merge Method

This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) as a base.

### Models Merged

The following models were included in the merge:
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
* [Endevor/InfinityRP-v1-7B](https://huggingface.co/Endevor/InfinityRP-v1-7B)
* [CalderaAI/Naberius-7B](https://huggingface.co/CalderaAI/Naberius-7B)
* [CalderaAI/Hexoteric-7B](https://huggingface.co/CalderaAI/Hexoteric-7B)
* [Endevor/EndlessRP-v3-7B](https://huggingface.co/Endevor/EndlessRP-v3-7B)

### Configuration

The following YAML configuration was used to produce this model:

```yaml
models:
  - model: mistralai/Mistral-7B-v0.1
    #no parameters necessary for base model
  - model: mistralai/Mistral-7B-Instruct-v0.2
    parameters:
      density: 0.6
      weight: 0.25
  - model: Endevor/InfinityRP-v1-7B
    parameters:
      density: 0.6
      weight: 0.25
  - model: Endevor/EndlessRP-v3-7B
    parameters:
      density: 0.6
      weight: 0.25
  - model: CalderaAI/Naberius-7B
    parameters:
      density: 0.6
      weight: 0.25
  - model: CalderaAI/Hexoteric-7B
    parameters:
      density: 0.6
      weight: 0.25
merge_method: ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
  normalize: false
  int8_mask: true
dtype: float16
```
### download
dowanlod any of one file not all of them.

### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.

Here is an incomplete list of clients and libraries that are known to support GGUF:

llama.cpp. The source project for GGUF. Offers a CLI and a server option.
text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
GPT4All, a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.
Faraday.dev, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.
ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.

### info
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [Q2_K.gguf)] | Q2_K | 2 | 2.72 GB| 5.22 GB | significant quality loss - not recommended for most purposes |
| [Q3_K_S.gguf)] | Q3_K_S | 3 | 3.16 GB| 5.66 GB | very small, high quality loss |
| [Q3_K_M.gguf)] | Q3_K_M | 3 | 3.52 GB| 6.02 GB | very small, high quality loss |
| [Q3_K_L.gguf)] | Q3_K_L | 3 | 3.82 GB| 6.32 GB | small, substantial quality loss |
| [Q4_0.gguf)]   |Q4_0 | 4 | 4.11 GB| 6.61 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [Q4_K_S.gguf)] | Q4_K_S | 4 | 4.14 GB| 6.64 GB | small, greater quality loss |
| [Q4_K_M.gguf)] | Q4_K_M | 4 | 4.37 GB| 6.87 GB | medium, balanced quality - recommended |
| [Q5_0.gguf)] | Q5_0 | 5 | 5.00 GB| 7.50 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [Q5_K_S.gguf) ]| Q5_K_S | 5 | 5.00 GB| 7.50 GB | large, low quality loss - recommended |
| [Q5_K_M.gguf) ]| Q5_K_M | 5 | 5.13 GB| 7.63 GB | large, very low quality loss - recommended |
| [Q6_K.gguf)] | Q6_K | 6 | 5.94 GB| 8.44 GB | very large, extremely low quality loss |
| [Q8_0.gguf)] | Q8_0 | 8 | 7.70 GB| 10.20 GB | very large, extremely low quality loss - not recommended |

**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
[note this info format is borrowed from @TheBloke (https://huggingface.co/TheBloke) ]
### citation
this repo has been used to make the merge.

```
@article{goddard2024arcee,
  title={Arcee's MergeKit: A Toolkit for Merging Large Language Models},
  author={Goddard, Charles and Siriwardhana, Shamane and Ehghaghi, Malikeh and Meyers, Luke and Karpukhin, Vlad and Benedict, Brian and McQuade, Mark and Solawetz, Jacob},
  journal={arXiv preprint arXiv:2403.13257},
  year={2024}
}
```