Instructions to use devanshbatham/nyx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use devanshbatham/nyx 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 devanshbatham/nyx
# Run inference directly in the terminal:
llama cli -hf devanshbatham/nyx

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf devanshbatham/nyx
# Run inference directly in the terminal:
llama cli -hf devanshbatham/nyx

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf devanshbatham/nyx
# Run inference directly in the terminal:
./build/bin/llama cli -hf devanshbatham/nyx

Use Docker

docker model run hf.co/devanshbatham/nyx

Llama new
LM Studio
Jan

vLLM

How to use devanshbatham/nyx with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/devanshbatham/nyx

Ollama
How to use devanshbatham/nyx with Ollama:
```
ollama run hf.co/devanshbatham/nyx
```
Unsloth Desktop

How to use devanshbatham/nyx with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf devanshbatham/nyx

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": "devanshbatham/nyx"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use devanshbatham/nyx with Docker Model Runner:
```
docker model run hf.co/devanshbatham/nyx
```

Lemonade

How to use devanshbatham/nyx with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull devanshbatham/nyx

Run and chat with the model

lemonade run user.nyx-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use devanshbatham/nyx with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf devanshbatham/nyx

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 devanshbatham/nyx

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use devanshbatham/nyx with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf devanshbatham/nyx

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 "devanshbatham/nyx" \
  --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"

nyx

File size: 1,000 Bytes

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  "format_version": 1,
  "model": "devanshbatham/nyx",
  "base_model": "Qwen/Qwen3.5-4B",
  "base_revision": "851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a",
  "license": "Apache-2.0",
  "artifact": {
    "path": "nyx.gguf",
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    "sha256": "39d64fb19863cfc067e2411fcb2a0444113dda13798b15de5617736d91914359",
    "format": "GGUF",
    "quantization": "importance-matrix IQ4_XS with selected Q5_K/Q6_K tensors"
  },
  "reference": {
    "bytes": 8424393056,
    "sha256": "b225f62bb79e2f0bc0bcd9fa00dba4579bade89e6b9581537da7fa2482a6b500"
  },
  "fidelity_evaluation": {
    "cases": 1024,
    "reference_correct": 873,
    "artifact_correct": 874,
    "decision_agreement": 0.9873046875,
    "decision_disagreements": 13,
    "gains": 7,
    "losses": 6
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  "tested_runtime": {
    "llama_cpp_revision": "e6cef8152f6e8351a870d8e1a98627139c9c379a",
    "cpu_load_smoke_test": true,
    "full_public_benchmark_on_release_artifact": false,
    "nvidia_qualification": false
  }
}