Instructions to use ngquocvinh/K2-Horizon-0.9B-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/K2-Horizon-0.9B-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/K2-Horizon-0.9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ngquocvinh/K2-Horizon-0.9B-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/K2-Horizon-0.9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ngquocvinh/K2-Horizon-0.9B-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/K2-Horizon-0.9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ngquocvinh/K2-Horizon-0.9B-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/K2-Horizon-0.9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ngquocvinh/K2-Horizon-0.9B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/ngquocvinh/K2-Horizon-0.9B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use ngquocvinh/K2-Horizon-0.9B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ngquocvinh/K2-Horizon-0.9B-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/K2-Horizon-0.9B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ngquocvinh/K2-Horizon-0.9B-GGUF:Q4_K_M

Ollama
How to use ngquocvinh/K2-Horizon-0.9B-GGUF with Ollama:
```
ollama run hf.co/ngquocvinh/K2-Horizon-0.9B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use ngquocvinh/K2-Horizon-0.9B-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/K2-Horizon-0.9B-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/K2-Horizon-0.9B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ngquocvinh/K2-Horizon-0.9B-GGUF with Docker Model Runner:
```
docker model run hf.co/ngquocvinh/K2-Horizon-0.9B-GGUF:Q4_K_M
```

Lemonade

How to use ngquocvinh/K2-Horizon-0.9B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ngquocvinh/K2-Horizon-0.9B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.K2-Horizon-0.9B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use ngquocvinh/K2-Horizon-0.9B-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/K2-Horizon-0.9B-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/K2-Horizon-0.9B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ngquocvinh/K2-Horizon-0.9B-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/K2-Horizon-0.9B-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/K2-Horizon-0.9B-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"

K2-Horizon-0.9B GGUF

Community GGUF quantizations of IFM/K2-Horizon-0.9B.

☕ If you find this GGUF useful, please consider
buying me a coffee.
Your support helps cover the GPU costs of future releases.
Thank you for supporting this work.

About K2-Horizon-0.9B

K2-Horizon-0.9B is IFM's compact 0.9B-class dense reasoning model. It has a 128K-token context window with YaRN RoPE scaling and is intended for English and Chinese conversational, reasoning, coding, science, and tool-use workloads. See the official model card for the supported serving stack, prompt conventions, and full evaluation protocol.

Upstream K2-Horizon-0.9B benchmark results; image and results are from the official model card.

This is a quantization-only release. No training, fine-tuning, merging, or weight modification other than GGUF conversion and quantization was performed.

Files

Quantization	File size (GiB)	A10M generation token/s	Validation	Recommendation / Notes
Q8_0	1.07	281.05	Load/generate pass	Highest-fidelity option in this release.
Q6_K	0.83	310.87	Load/generate pass	High-fidelity local inference.
Q5_K_M	0.72	316.41	Load/generate pass	Balanced quality and memory.
Q4_K_M	0.62	352.89	Load/generate pass	Recommended starting point for general use.
Q3_K_M	0.51	261.54	Load/generate pass	Lower-memory profile; validate your workload.
Q2_K	0.41	326.72	Load/generate pass	Aggressive low-memory option.
IQ2_XS	0.34	328.10	Load/generate pass	Experimental low-memory profile.
IQ1_M	0.29	343.17	Load/generate pass	Experimental; instruction following and reasoning may degrade.
Q1_0	0.21	446.95	Load/generate pass	Experimental / legacy minimum-memory option.

Every file above was loaded and generated non-empty text in the K2 pre-release llama.cpp runtime on an NVIDIA A10M. The A10M measurements were taken with single-stream llama-bench, prompt 256, generation 64, batch/ubatch 512, 64 CPU threads, full GPU offload, and three repetitions on an otherwise idle GPU. Low-bit files—especially Q2, IQ2, IQ1, and Q1—can materially reduce reasoning, instruction following, and tool-call reliability; test the selected file with the prompts that matter to you.

K2-Horizon support is pre-release in llama.cpp. The upstream full tool-aware Jinja template uses constructs not yet parsed by the runtime revision used for this release; the reproducibility folder includes the compatible single-turn chat wrapper used for smoke testing. Tool calling is therefore not certified by this GGUF release.

License and attribution

The upstream model is licensed under Apache License 2.0. Preserve upstream attribution and the license when redistributing these derivative artifacts. These are community GGUF quantizations, not an official IFM release or endorsement.

The compact benchmark summary and reproduction script are in reproducibility/a10m-benchmark.tsv and reproducibility/benchmark_gpu1.sh.

Checksums for published artifacts and reproducibility inputs are in SHA256SUMS.txt.

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k2-horizon

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