Instructions to use ngquocvinh/NeoHorse-1-9B-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 ngquocvinh/NeoHorse-1-9B-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 ngquocvinh/NeoHorse-1-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ngquocvinh/NeoHorse-1-9B-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 ngquocvinh/NeoHorse-1-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ngquocvinh/NeoHorse-1-9B-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 ngquocvinh/NeoHorse-1-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ngquocvinh/NeoHorse-1-9B-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 ngquocvinh/NeoHorse-1-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ngquocvinh/NeoHorse-1-9B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/ngquocvinh/NeoHorse-1-9B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use ngquocvinh/NeoHorse-1-9B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ngquocvinh/NeoHorse-1-9B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ngquocvinh/NeoHorse-1-9B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ngquocvinh/NeoHorse-1-9B-GGUF:Q4_K_M

Ollama
How to use ngquocvinh/NeoHorse-1-9B-GGUF with Ollama:
```
ollama run hf.co/ngquocvinh/NeoHorse-1-9B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use ngquocvinh/NeoHorse-1-9B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ngquocvinh/NeoHorse-1-9B-GGUF: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": "ngquocvinh/NeoHorse-1-9B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ngquocvinh/NeoHorse-1-9B-GGUF with Docker Model Runner:
```
docker model run hf.co/ngquocvinh/NeoHorse-1-9B-GGUF:Q4_K_M
```

Lemonade

How to use ngquocvinh/NeoHorse-1-9B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ngquocvinh/NeoHorse-1-9B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.NeoHorse-1-9B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use ngquocvinh/NeoHorse-1-9B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ngquocvinh/NeoHorse-1-9B-GGUF: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 ngquocvinh/NeoHorse-1-9B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ngquocvinh/NeoHorse-1-9B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ngquocvinh/NeoHorse-1-9B-GGUF: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 "ngquocvinh/NeoHorse-1-9B-GGUF: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"

NeoHorse-1-9B GGUF

Community GGUF quantizations of TokenRhythm/NeoHorse-1-9B.

☕ If this GGUF made your day easier, a coffee would make mine.
Send a coffee ☕
I build and test these releases myself. Your coffee helps keep me going.
Thank you for supporting this work.

About NeoHorse-1-9B

NeoHorse-1-9B is an approximately 9B causal language model post-trained from Qwen3.5-9B for text-based agent harnesses, tool use, coding, reasoning, and instruction following. This package contains text-only GGUF files; vision weights are not included. See the official upstream model card for the model's original details, usage, and limitations.

The upstream card describes a native 262,144-token context and extensibility up to 1,010,000 tokens. The upstream card also reports a 69.04 ten-benchmark average versus 65.60 for Qwen3.5-9B under its SGLang evaluation protocol. Those figures are upstream measurements, not measurements of these GGUF files.

NeoHorse-1-9B evaluation results (upstream)

This is a quantization-only release. No training or fine-tuning was performed as part of this conversion.

Fidelity measurements

The table compares every published quantization with the converted BF16 reference on the same hold-out split. Evaluation used the first two chunks of wiki.valid.raw, 2,048 tokens per chunk, and the CUDA build of llama.cpp on an NVIDIA A10M. The BF16 baseline mean PPL was 5.641047. Values are averaged over the two evaluated chunks. File sizes use decimal GB (bytes / 1,000,000,000). Bold rows are practical sweet spots: Q4_K_M is the balanced default, Q5_K_M and Q5_K_S provide more fidelity headroom, and IQ4_XS is a compact alternative.

File	Size (GB)	Mean KLD ↓	Top-1 vs BF16 ↑	ΔPPL	RMS Δp
NeoHorse-1-9B-Q8_0.gguf	9.528	0.006655	98.436%	+0.732%	0.811%
NeoHorse-1-9B-Q6_K.gguf	7.359	0.009702	97.312%	+1.120%	1.408%
NeoHorse-1-9B-Q5_K_M.gguf	6.468	0.030926	94.673%	+2.158%	5.841%
NeoHorse-1-9B-Q4_K_M.gguf	5.629	0.029467	94.428%	+0.951%	4.429%
NeoHorse-1-9B-Q3_K_M.gguf	4.624	0.069233	88.368%	+4.573%	7.326%
NeoHorse-1-9B-Q2_K.gguf	3.827	0.199997	82.063%	+14.167%	12.510%
NeoHorse-1-9B-IQ2_XS.gguf	3.285	0.342380	77.761%	+30.698%	18.365%
NeoHorse-1-9B-IQ1_M.gguf	2.877	0.807561	64.565%	+96.977%	28.701%
NeoHorse-1-9B-Q1_0.gguf	1.966	15.570850	2.933%	+596289635.160%	63.365%
NeoHorse-1-9B-Q5_K_S.gguf	6.305	0.030149	95.112%	+1.381%	5.817%
NeoHorse-1-9B-Q4_K_S.gguf	5.352	0.030445	93.842%	+1.040%	4.639%
NeoHorse-1-9B-Q3_K_L.gguf	4.926	0.062186	89.541%	+3.793%	6.790%
NeoHorse-1-9B-Q3_K_S.gguf	4.259	0.131594	85.386%	+6.965%	9.989%
NeoHorse-1-9B-Q2_K_S.gguf	3.697	0.240966	81.183%	+21.317%	14.223%
NeoHorse-1-9B-IQ4_XS.gguf	5.196	0.026534	93.891%	+2.125%	3.807%
NeoHorse-1-9B-IQ4_NL.gguf	5.418	0.029899	93.695%	+3.189%	4.788%
NeoHorse-1-9B-IQ3_M.gguf	4.415	0.064802	88.905%	+2.843%	7.357%
NeoHorse-1-9B-IQ3_S.gguf	4.371	0.081996	89.541%	+2.072%	7.758%
`ΔPPL` is the percentage change in perplexity relative to BF16. Lower KLD,
ΔPPL, and RMS Δp, together with higher Top-1 agreement, indicate closer
next-token behavior to BF16. These are next-token fidelity measurements, not a
task benchmark; reasoning, coding, and tool-use behavior can vary by workload.
The machine-readable results are in `reproducibility/quality-summary.tsv`,
with the evaluation details in `reproducibility/manifest.md`.

Quick start

Q4_K_M is a practical starting point for local use. Q5_K_M, Q6_K, or Q8_0 provide more headroom for fidelity when memory allows. Q3_K_M and lower are low-memory experimental options; the measurements above show increasing distance from BF16.

./llama-cli \
  -m NeoHorse-1-9B-Q4_K_M.gguf \
  --chat-template-file chat_template.jinja \
  --jinja \
  --reasoning off \
  -p 'Answer briefly in English: What is GGUF and why is it useful for running language models locally?' \
  -n 128 -c 4096 -ngl 99

The included chat_template.jinja is retained from the upstream text-only checkpoint. Adjust context size, GPU layers, and batch settings for the available hardware.

Reproducibility and validation

Source: upstream revision ba5b6e4.
The BF16 GGUF was converted directly from the upstream BF16 safetensors with --no-nextn, because this text-only checkpoint has no MTP tensors even though its config retains an MTP layer-count field.
Every published file was quantized directly from that BF16 GGUF with the model-specific imatrix included under calibration/; no re-quantization from another quantized file was used.
All published files passed load and short English generation smoke tests; the BF16 source additionally passed --check-tensors. Runtime readings are summarized in reproducibility/runtime-summary.tsv.
Verify artifacts with SHA256SUMS.txt and reproduce the provenance from reproducibility/manifest.md.

Raw conversion, calibration, quantization, smoke-test, fidelity, and benchmark logs are kept local and are not part of this public model package.

License and attribution

The upstream model and these community quantizations are released under the Apache License 2.0, with the upstream LICENSE retained in this repository.

These are community GGUF quantizations, not an official TokenRhythm release or endorsement.