---

pipeline_tag: text-generation

base_model:

• IFM/K2-Horizon-0.9B

library_name: llama-cpp

tags:

• k2-horizon
• 0.9b
• dense
• open-weights
• ifm
• gguf
• nvfp4

language:

• en

license: apache-2.0

license_name: apache-license-2.0

license_link: https://apache.org/licenses/LICENSE-2.0

---

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/},
}