Instructions to use Baekpica/K2-Horizon-375B-A23B-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 Baekpica/K2-Horizon-375B-A23B-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 Baekpica/K2-Horizon-375B-A23B-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf Baekpica/K2-Horizon-375B-A23B-GGUF:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Baekpica/K2-Horizon-375B-A23B-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf Baekpica/K2-Horizon-375B-A23B-GGUF:BF16

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 Baekpica/K2-Horizon-375B-A23B-GGUF:BF16
# Run inference directly in the terminal:
./llama-cli -hf Baekpica/K2-Horizon-375B-A23B-GGUF:BF16

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 Baekpica/K2-Horizon-375B-A23B-GGUF:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Baekpica/K2-Horizon-375B-A23B-GGUF:BF16

Use Docker

docker model run hf.co/Baekpica/K2-Horizon-375B-A23B-GGUF:BF16

LM Studio
Jan

vLLM

How to use Baekpica/K2-Horizon-375B-A23B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Baekpica/K2-Horizon-375B-A23B-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": "Baekpica/K2-Horizon-375B-A23B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Baekpica/K2-Horizon-375B-A23B-GGUF:BF16

Ollama
How to use Baekpica/K2-Horizon-375B-A23B-GGUF with Ollama:
```
ollama run hf.co/Baekpica/K2-Horizon-375B-A23B-GGUF:BF16
```
Unsloth Desktop

How to use Baekpica/K2-Horizon-375B-A23B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Baekpica/K2-Horizon-375B-A23B-GGUF:BF16

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": "Baekpica/K2-Horizon-375B-A23B-GGUF:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Baekpica/K2-Horizon-375B-A23B-GGUF with Docker Model Runner:
```
docker model run hf.co/Baekpica/K2-Horizon-375B-A23B-GGUF:BF16
```

Lemonade

How to use Baekpica/K2-Horizon-375B-A23B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Baekpica/K2-Horizon-375B-A23B-GGUF:BF16

Run and chat with the model

lemonade run user.K2-Horizon-375B-A23B-GGUF-BF16

List all available models

lemonade list

Hermes Agent

How to use Baekpica/K2-Horizon-375B-A23B-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 Baekpica/K2-Horizon-375B-A23B-GGUF:BF16

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 Baekpica/K2-Horizon-375B-A23B-GGUF:BF16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Baekpica/K2-Horizon-375B-A23B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Baekpica/K2-Horizon-375B-A23B-GGUF:BF16

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 "Baekpica/K2-Horizon-375B-A23B-GGUF:BF16" \
  --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-375B-A23B GGUF intermediates

Public, reproducible intermediate GGUF artifacts converted directly from IFM/K2-Horizon-375B-A23B at revision d33e3ae45281865ebf9f044b12d3635b1d1e17fe.

This repository contains both:

BF16: the lossless GGUF conversion used as the sole quantization source.
Q8_0: the high-precision intermediate generated directly from that BF16 GGUF.

The memory-targeted mixed quant is published separately at Baekpica/K2-Horizon-375B-A23B-Mixed-Quant-GGUF.

Provenance

Source parameters: original BF16 checkpoint; the official FP8 checkpoint was deliberately not used, avoiding quantization-on-quantization.
Source parameter count: 379,167,159,168.
Converter: IFM's K2-Horizon llama.cpp branch, commit 35999d101cf2233fc54f09c3c8d599da7303ce02.
BF16 split target: 30G per shard.
Q8 policy: all 2-D weights Q8_0; router weights/biases, normalization weights, and other 1-D control tensors remain F32.

Machine-readable structural audits are stored in validation/BF16.audit.json and validation/Q8_0.audit.json. Content hashes are in the corresponding *-SHA256SUMS files; both 30-shard sets were checked against the Hub's LFS object IDs after every upload closed.

Status

BF16: complete — 30/30 shards, 758,484,189,216 bytes (706.393448 GiB), 842 tensors (BF16=603, F32=239).
BF16 structural audit: passed.
BF16 remote inventory: passed — 30/30 objects and aggregate byte count match.
Q8_0: complete — 30/30 shards, 403,079,839,776 bytes (375.397354 GiB), with tensor payload 403,068,341,760 bytes (375.386646 GiB).
Q8_0 structural audit: passed — 842 tensors (Q8_0=603, F32=239).
Q8_0 remote verification: passed — all 30 Hub LFS SHA-256 values and the aggregate byte count match Q8_0-SHA256SUMS.

Each artifact is usable from its 00001-of-00030 file when all 30 numbered siblings are present in the same directory.

Downloads last month: 862

GGUF

Model size

379B params

Architecture

k2-horizon

Hardware compatibility

8-bit

16-bit

Model tree for Baekpica/K2-Horizon-375B-A23B-GGUF

Base model

IFM/K2-Horizon-375B-A23B

Quantized

(2)

this model