Instructions to use Indexnusrefather/Nyx-RP-9B-Instruct-2608-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Indexnusrefather/Nyx-RP-9B-Instruct-2608-v1 with Transformers:

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

pipe = pipeline("text-generation", model="Indexnusrefather/Nyx-RP-9B-Instruct-2608-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Indexnusrefather/Nyx-RP-9B-Instruct-2608-v1")
model = AutoModelForCausalLM.from_pretrained("Indexnusrefather/Nyx-RP-9B-Instruct-2608-v1", device_map="auto")
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

llama.cpp

How to use Indexnusrefather/Nyx-RP-9B-Instruct-2608-v1 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 Indexnusrefather/Nyx-RP-9B-Instruct-2608-v1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Indexnusrefather/Nyx-RP-9B-Instruct-2608-v1:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Indexnusrefather/Nyx-RP-9B-Instruct-2608-v1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Indexnusrefather/Nyx-RP-9B-Instruct-2608-v1: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 Indexnusrefather/Nyx-RP-9B-Instruct-2608-v1:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Indexnusrefather/Nyx-RP-9B-Instruct-2608-v1: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 Indexnusrefather/Nyx-RP-9B-Instruct-2608-v1:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Indexnusrefather/Nyx-RP-9B-Instruct-2608-v1:Q4_K_M

Use Docker

docker model run hf.co/Indexnusrefather/Nyx-RP-9B-Instruct-2608-v1:Q4_K_M

LM Studio
Jan

vLLM

How to use Indexnusrefather/Nyx-RP-9B-Instruct-2608-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Indexnusrefather/Nyx-RP-9B-Instruct-2608-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Indexnusrefather/Nyx-RP-9B-Instruct-2608-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Indexnusrefather/Nyx-RP-9B-Instruct-2608-v1:Q4_K_M

SGLang

How to use Indexnusrefather/Nyx-RP-9B-Instruct-2608-v1 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 "Indexnusrefather/Nyx-RP-9B-Instruct-2608-v1" \
    --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": "Indexnusrefather/Nyx-RP-9B-Instruct-2608-v1",
		"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 "Indexnusrefather/Nyx-RP-9B-Instruct-2608-v1" \
        --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": "Indexnusrefather/Nyx-RP-9B-Instruct-2608-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Indexnusrefather/Nyx-RP-9B-Instruct-2608-v1 with Ollama:
```
ollama run hf.co/Indexnusrefather/Nyx-RP-9B-Instruct-2608-v1:Q4_K_M
```
Unsloth Desktop

How to use Indexnusrefather/Nyx-RP-9B-Instruct-2608-v1 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Indexnusrefather/Nyx-RP-9B-Instruct-2608-v1:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Indexnusrefather/Nyx-RP-9B-Instruct-2608-v1:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Indexnusrefather/Nyx-RP-9B-Instruct-2608-v1 with Docker Model Runner:
```
docker model run hf.co/Indexnusrefather/Nyx-RP-9B-Instruct-2608-v1:Q4_K_M
```

Lemonade

How to use Indexnusrefather/Nyx-RP-9B-Instruct-2608-v1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Indexnusrefather/Nyx-RP-9B-Instruct-2608-v1:Q4_K_M

Run and chat with the model

lemonade run user.Nyx-RP-9B-Instruct-2608-v1-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Indexnusrefather/Nyx-RP-9B-Instruct-2608-v1 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Indexnusrefather/Nyx-RP-9B-Instruct-2608-v1:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Indexnusrefather/Nyx-RP-9B-Instruct-2608-v1:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Indexnusrefather/Nyx-RP-9B-Instruct-2608-v1 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Indexnusrefather/Nyx-RP-9B-Instruct-2608-v1:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "Indexnusrefather/Nyx-RP-9B-Instruct-2608-v1:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Nyx-RP-9B-Instruct-2608-v1: Final release version of a roleplay finetune for Qwen 3.5 9B.

"Engineered to punch above its weight."

Quick Overview:

Way better roleplay than the base model
Better pacing and punctuation
Better creative writing
More consistent interactions
Specialized private dataset with carefully selected data was used

Quant info:

BF16: Overkill
Q8_0: Highest quality, mostly overkill
Q6_K: Near lossless, recommended
Q5_K_M: Very high quality, recommended.
Q4_K_M: High quality, fast, recommended
Q3_K_M: Lower quality, fastest, recommended.

Quants are all located in the repository.

I am currently also working on gemma 3 12b it finetune, but this one did surprisingly well.

Note:

I have also now released my similar gemma 3 12b it finetune, in comparison to Nyx its more creative, but Nyx is incredibly smart and follows instructions flawlessly, issue with qwen models writing dryly is less noticeable here after my observations, I'll leave a link to it here just in case: https://huggingface.co/Indexnusrefather/Erebus-RP-12B-Instruct-2608-v1

UPD: Benchmark attached, currently leading 9B and under ERP category on caliperbench.com:

I will continue gathering more high quality RP logs to use for the v2, but I'm already amazed.

v2 needs trial:

I updated my methodology and released a couple of different test versions which will later become Nyx-RP-9B-Instruct-2608-v2, I made them in an org not to dump everything into my main profile, they are named Interferons(from alpha to gamma for now) and you can find them here: https://huggingface.co/Wonderlab-Testing-Grounds

Feedback for v2s is very important, I recommend trying out the gemma version as its the most promising one to me this far, but others might be good too. First results were as expected, testing versions are testing for a reason, most of them sucked, the v1 still leading in the ERP category, now I know what approach to take though. For now I will leave this for later, I'm currently more focused on my other plans and projects.

Downloads last month: 4,265

Safetensors

Model size

9B params

Tensor type

BF16

F32

Model tree for Indexnusrefather/Nyx-RP-9B-Instruct-2608-v1

Base model

Qwen/Qwen3.5-9B-Base

Finetuned

Qwen/Qwen3.5-9B

Finetuned

(695)

this model

Finetunes

1 model

Quantizations

2 models