K2-Horizon-0.9B NVFP4 — GGUF
NVFP4 GGUF conversions derived from the K2-Horizon-0.9B model family. This repository provides NVFP4 (NVIDIA 4-bit floating-point) GGUF files, making the model usable with llama.cpp and other GGUF-compatible inference engines.
Model Overview
K2-Horizon-0.9B is the compact dense member of the K2-Horizon family: a 0.9B-class decoder-only model with a 128K context window. It is a compact reasoning model evaluated across mathematics, coding, science, and tool-use benchmarks.
Model Architecture
| Property | Value |
|---|---|
| Architecture | K2-Horizon-0.9B |
| Parameters | 0.9B |
| Context Length | 128K tokens (131,072) |
| Supported Modalities | Text |
| Type | Dense |
Benchmark Results
| Benchmark | K2-Horizon-0.9B | Qwen3.5-0.8B | OpenBMB-1B | Qwen3.5-2B |
|---|---|---|---|---|
| Math | ||||
| AIME 2025 | 41.7 | 1.0 | 40.4 | 34.2 |
| AIME 2026 | 48.5 | 0.2 | 40.4 | 38.8 |
| HMMT Feb 2026 | 25.8 | 0.6 | 23.3 | 22.7 |
| Scientific Reasoning | ||||
| GPQA Diamond | 27.3 | 11.9 | 26.3 | 54.9 |
| Coding | ||||
| HumanEval+ | 79.9 | 16.5 | 65.2 | 75.6 |
| MBPP+ | 68.0 | 35.4 | 60.6 | 67.7 |
| LiveCodeBench v6 | 37.4 | 6.6 | 33.5 | 29.8 |
| Agents | ||||
| BFCL v4 | 28.0 | 25.3 | 25.2 | 43.6 |
Scores in %. Bold marks the best score in each row. Benchmark results are sourced from the original K2-Horizon-0.9B model description.
GGUF Files
| File | Format | Description |
|---|---|---|
K2-Horizon-0.9B-nvfp4.gguf | NVFP4 | NVFP4 quantized model |
Usage
llama.cpp (CLI)
# Run inference
./llama-cli \
-m K2-Horizon-0.9B-nvfp4.gguf \
--temp 1.0 --top-p 0.95
llama-server (OpenAI-compatible API)
# Start the server
./llama-server \
-m K2-Horizon-0.9B-nvfp4.gguf \
--port 8080
Key Features
- Compact reasoning model. A 0.9B-class dense model evaluated across mathematics, coding, science, and tool-use benchmarks.
- 128K context. Supports up to 131,072 tokens with YaRN RoPE scaling.
- Multi-teacher distillation. Trained with domain teachers for math and code, STEM, and instruction following.
- NVFP4 Quantization. Optimized for NVIDIA Blackwell and compatible hardware.
Citation
@misc{k2horizon2026,
title = {Introducing K2 Horizon: Frontier Performance, Radically Open},
author = {{IFM Team}},
year = {2026},
url = {https://ifm.ai/blog/k2/},
}