kainatq/kainaticulous-rp-7b-gguf

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. 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 merge method using mistralai/Mistral-7B-v0.1 as a base.

Models Merged

The following models were included in the merge:

• mistralai/Mistral-7B-Instruct-v0.2
• Endevor/InfinityRP-v1-7B
• CalderaAI/Naberius-7B
• CalderaAI/Hexoteric-7B
• Endevor/EndlessRP-v3-7B

Configuration

The following YAML configuration was used to produce this model:

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}
}