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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kainatq/kainaticulous-rp-7b")
model = AutoModelForCausalLM.from_pretrained("kainatq/kainaticulous-rp-7b")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use kainatq/kainaticulous-rp-7b 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"
# 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",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

SGLang

How to use kainatq/kainaticulous-rp-7b 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" \
    --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",
		"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" \
        --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",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use kainatq/kainaticulous-rp-7b with Docker Model Runner:
```
docker model run hf.co/kainatq/kainaticulous-rp-7b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

other repos:

gguf: (https://huggingface.co/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:

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

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}
}