NSFW

Instructions to use MrRikyz/Rei-Pulse-24B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MrRikyz/Rei-Pulse-24B with Transformers:

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

pipe = pipeline("text-generation", model="MrRikyz/Rei-Pulse-24B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("MrRikyz/Rei-Pulse-24B")
model = AutoModelForMultimodalLM.from_pretrained("MrRikyz/Rei-Pulse-24B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MrRikyz/Rei-Pulse-24B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MrRikyz/Rei-Pulse-24B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MrRikyz/Rei-Pulse-24B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MrRikyz/Rei-Pulse-24B

SGLang

How to use MrRikyz/Rei-Pulse-24B 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 "MrRikyz/Rei-Pulse-24B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MrRikyz/Rei-Pulse-24B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "MrRikyz/Rei-Pulse-24B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MrRikyz/Rei-Pulse-24B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MrRikyz/Rei-Pulse-24B with Docker Model Runner:
```
docker model run hf.co/MrRikyz/Rei-Pulse-24B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Rei-Pulse-24B

A merge of specialized 24B Rp models

⚠️ Attention: It works with ChatML format and Mistral V7-Tekken (no thinking output) i didn't test the Thinking.

Tested using Ik_llama.cpp as the backend and Sillytavern as the frontend

🌟 Overview

Rei-Pulse-24B is a merge built on top of Morax-24B-v1.

The model uses the DARE TIES method

🧩 Models Merged

This model results from a merge between:

DarkArtsForge/Morax-24B-v1 (Base)
OddTheGreat/Circuitry_24B_V.3
Delta-Vector/Rei-24B-KTO
TheDrummer/Precog-24B-v1
TheDrummer/Rivermind-24B-v1

🛠️ Merge Details

Method: DARE TIES

The merge was performed using mergekit with the following parameters:

Base Model: Morax-24B-v1
Dtype: bfloat16
Density: 0.5
Weight: 0.2

⚙️ Configuration

View Full Mergekit YAML

base_model: DarkArtsForge/Morax-24B-v1
dtype: bfloat16
merge_method: dare_ties
modules:
  default:
    slices:
    - sources:
      - layer_range: [0, 40]
        model: DarkArtsForge/Morax-24B-v1
        parameters:
          density: 0.5
          weight: 0.2
      - layer_range: [0, 40]
        model: TheDrummer/Rivermind-24B-v1
        parameters:
          density: 0.5
          weight: 0.2
      - layer_range: [0, 40]
        model: OddTheGreat/Circuitry_24B_V.3
        parameters:
          density: 0.5
          weight: 0.2
      - layer_range: [0, 40]
        model: Delta-Vector/Rei-24B-KTO
        parameters:
          density: 0.5
          weight: 0.2
      - layer_range: [0, 40]
        model: TheDrummer/Precog-24B-v1
        parameters:
          density: 0.5
          weight: 0.2
parameters:
  lambda: 0.75
  normalize: True
tokenizer:
  source: base