Not-For-All-Audiences

nsfw

Instructions to use Endevor/EndlessRP-v2-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Endevor/EndlessRP-v2-7B-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Endevor/EndlessRP-v2-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Endevor/EndlessRP-v2-7B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Endevor/EndlessRP-v2-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Endevor/EndlessRP-v2-7B-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 Endevor/EndlessRP-v2-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Endevor/EndlessRP-v2-7B-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 Endevor/EndlessRP-v2-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Endevor/EndlessRP-v2-7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Endevor/EndlessRP-v2-7B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Endevor/EndlessRP-v2-7B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Endevor/EndlessRP-v2-7B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Endevor/EndlessRP-v2-7B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Endevor/EndlessRP-v2-7B-GGUF:Q4_K_M

Ollama
How to use Endevor/EndlessRP-v2-7B-GGUF with Ollama:
```
ollama run hf.co/Endevor/EndlessRP-v2-7B-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use Endevor/EndlessRP-v2-7B-GGUF with Docker Model Runner:
```
docker model run hf.co/Endevor/EndlessRP-v2-7B-GGUF:Q4_K_M
```

Lemonade

How to use Endevor/EndlessRP-v2-7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Endevor/EndlessRP-v2-7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.EndlessRP-v2-7B-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

This model is recommended for RP, but you can use it as assistant as well. Please, give it a try. This version has lees GPTism.

Prompt Format:

Extended Alpaca Format As for exemple from lemonilia/LimaRP-Mistral-7B-v0.1. Use ### Response: (length = huge) for exemple, to increase length.

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: .\Endevor_EndlessRP_v1
  - model: kubernetes-bad/good-robot+.\toxic-dpo-v0.1-NoWarning-lora # This removes most of GPTism. The Toxic DPO is a lora I finetuned myself.
    parameters:
      weight: 0.6
      density: 0.53
  - model: rwitz/go-bruins-v2+Undi95/Mistral-7B-smoll_pippa-lora # Maintein RP stability.
    parameters:
      weight: 0.4
      density: 0.53   
merge_method: dare_ties
base_model: .\Endevor_EndlessRP_v1
parameters:
  normalize: false
  int8_mask: true
dtype: bfloat16

As this mostly focuses on RP, please don't expect it being smart with riddles or logical tests.

Downloads last month: 30

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

4-bit

5-bit

6-bit

8-bit