Instructions to use kainatq/kainaticulous-rp-7b-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kainatq/kainaticulous-rp-7b-gguf with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="kainatq/kainaticulous-rp-7b-gguf")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("kainatq/kainaticulous-rp-7b-gguf", dtype="auto")

llama-cpp-python

How to use kainatq/kainaticulous-rp-7b-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="kainatq/kainaticulous-rp-7b-gguf",
	filename="kainaticulous-rp-7B-F16.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use kainatq/kainaticulous-rp-7b-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf kainatq/kainaticulous-rp-7b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf kainatq/kainaticulous-rp-7b-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf kainatq/kainaticulous-rp-7b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf kainatq/kainaticulous-rp-7b-gguf:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf kainatq/kainaticulous-rp-7b-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf kainatq/kainaticulous-rp-7b-gguf:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf kainatq/kainaticulous-rp-7b-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf kainatq/kainaticulous-rp-7b-gguf:Q4_K_M

Use Docker

docker model run hf.co/kainatq/kainaticulous-rp-7b-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use kainatq/kainaticulous-rp-7b-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kainatq/kainaticulous-rp-7b-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kainatq/kainaticulous-rp-7b-gguf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/kainatq/kainaticulous-rp-7b-gguf:Q4_K_M

SGLang

How to use kainatq/kainaticulous-rp-7b-gguf with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "kainatq/kainaticulous-rp-7b-gguf" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kainatq/kainaticulous-rp-7b-gguf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "kainatq/kainaticulous-rp-7b-gguf" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kainatq/kainaticulous-rp-7b-gguf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use kainatq/kainaticulous-rp-7b-gguf with Ollama:
```
ollama run hf.co/kainatq/kainaticulous-rp-7b-gguf:Q4_K_M
```

Unsloth Studio

How to use kainatq/kainaticulous-rp-7b-gguf with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for kainatq/kainaticulous-rp-7b-gguf to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for kainatq/kainaticulous-rp-7b-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for kainatq/kainaticulous-rp-7b-gguf to start chatting

Docker Model Runner
How to use kainatq/kainaticulous-rp-7b-gguf with Docker Model Runner:
```
docker model run hf.co/kainatq/kainaticulous-rp-7b-gguf:Q4_K_M
```

Lemonade

How to use kainatq/kainaticulous-rp-7b-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull kainatq/kainaticulous-rp-7b-gguf:Q4_K_M

Run and chat with the model

lemonade run user.kainaticulous-rp-7b-gguf-Q4_K_M

List all available models

lemonade list

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:

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}
}