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

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 devanshbatham/nyx
# Run inference directly in the terminal:
./llama-cli -hf devanshbatham/nyx

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 devanshbatham/nyx
# Run inference directly in the terminal:
./build/bin/llama-cli -hf devanshbatham/nyx

Use Docker

docker model run hf.co/devanshbatham/nyx

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 / README.md

Devansh Batham

Add required tokenizer assets

47845a3 17 days ago

preview code

Raw History Blame Contribute Delete

2.74 kB

	---
	license: apache-2.0
	pipeline_tag: text-generation
	base_model:
	- Qwen/Qwen3.5-4B
	tags:
	- nyx
	- qwen3.5
	- gguf
	- llama-cpp
	- quantized
	- classification
	---

	# nyx

	nyx is a compact 4B decision model for Choice, Score, and Noul workloads. The release is one 3.29 GB GGUF for llama.cpp on CPU, Apple Metal, NVIDIA CUDA, and AMD HIP.

	![Animated green-and-white chart comparing nyx and Jev across six classification benchmarks](assets/nyx-benchmarks.svg)

	## Model

	\| Property \| Value \|
	\|---\|---\|
	\| Base \| [Qwen/Qwen3.5-4B](https://huggingface.co/Qwen/Qwen3.5-4B) \|
	\| File \| `nyx.gguf` \|
	\| Size \| 3.29 GB / 3.06 GiB \|
	\| Format \| GGUF, importance-matrix IQ4_XS with selected Q5_K/Q6_K tensors \|
	\| SHA-256 \| `39d64fb19863cfc067e2411fcb2a0444113dda13798b15de5617736d91914359` \|

	## Run

	```bash
	git clone https://github.com/devanshbatham/nyx.git /opt/nyx

	python3 -m venv /opt/nyx/.venv
	/opt/nyx/.venv/bin/pip install '/opt/nyx[server]'
	/opt/nyx/.venv/bin/hf download devanshbatham/nyx --local-dir /opt/nyx-model

	/opt/nyx/scripts/build-llama-cpp.sh /opt/llama.cpp

	export LLAMA_SERVER_BIN=/opt/llama.cpp/build/bin/llama-server
	export NYX_GGUF_PATH=/opt/nyx-model/nyx.gguf
	/opt/nyx/scripts/serve-model.sh
	```

	Allow 8 GB of free RAM, VRAM, or unified memory and 6 GB of disk for one 4,096-token slot. Python 3.11+, Git, CMake, and a recent C++ compiler are required. Larger contexts and concurrent slots need more memory.

	The production API and typed Python/TypeScript clients are in [devanshbatham/nyx](https://github.com/devanshbatham/nyx).

	## Benchmarks

	Accuracy on 2,277 frozen classification requests:

	\| Benchmark \| Qwen3.5-4B base \| nyx reference \| Jev 1.13 \|
	\|---\|---:\|---:\|---:\|
	\| AG News \| 87.75% \| 85.75% \| 86.50% \|
	\| TREC coarse \| 86.60% \| 84.20% \| 93.00% \|
	\| CoLA \| 77.75% \| 81.50% \| 77.75% \|
	\| RTE \| 84.12% \| 88.09% \| 91.34% \|
	\| Emotion \| 47.67% \| 46.33% \| 47.00% \|
	\| IMDb \| 95.75% \| 95.75% \| 97.00% \|
	\| Dataset-macro accuracy \| 79.94% \| 80.27% \| 82.10% \|
	\| Pooled accuracy \| 81.42% \| 81.51% \| 83.62% \|
	\| Round trip, median / p95 \| 54.85 / 67.96 ms \| 55.56 / 72.77 ms \| 182.33 / 245.12 ms \|
	\| Throughput \| 17.44 req/s \| 17.15 req/s \| 42.43 req/s \|

	Local Qwen and nyx latency: MI325X, concurrency 1. Jev latency: remote HTTPS, concurrency 8. The published GGUF matched the nyx reference on 98.73% of a separate 1,024-case fidelity set. [Reproduce the nyx/Jev benchmark](https://github.com/devanshbatham/nyx/tree/main/benchmarks).

	## License

	Apache-2.0. nyx is derived from [Qwen/Qwen3.5-4B](https://huggingface.co/Qwen/Qwen3.5-4B); Qwen and Alibaba Cloud are credited as the original model authors. This project is not affiliated with Qwen, Alibaba Cloud, TypeSafe, or Jev.