K2-Horizon-3.7B NVFP4 — GGUF
NVFP4 GGUF conversions derived from the K2-Horizon-3.7B 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-3.7B is the small dense member of the K2-Horizon family: a 3.7B-core decoder-only model with a 512K context window.
Model Architecture
| Property | Value |
|---|---|
| Architecture | K2-Horizon-3.7B |
| Parameters | 3.7B |
| Context Length | 512K tokens (524,288) |
| Supported Modalities | Text |
| Type | Dense |
Benchmark Results
| Benchmark | K2-Horizon-3.7B | Qwen3.5-4B | G9v3-3B | Granite 4.2-3B | Nemotron 3 Nano-4B |
|---|---|---|---|---|---|
| Math | |||||
| HMMT Feb 2026 | 70.5 | 61.6 | 34.1 | 57.2 | 34.7 |
| Coding | |||||
| SWE-bench Verified | 68.6 | 41.2 | 16.4 | 32.2 | 1.8 |
| Scientific Reasoning | |||||
| GPQA Diamond | 65.4 | 77.1 | 43.8 | 55.9 | 51.3 |
| HLE | 12.9 | 9.9 | 4.5 | 6.6 | 4.9 |
| SciCode | 25.9 | 16.1 | 17.7 | 24.9 | 16.4 |
| Agents | |||||
| Terminal-Bench 2.1 | 25.1 | 25.8 | 6.0 | 13.9 | 3.7 |
| tau3-Banking | 17.7 | 6.8 | — | 5.6 | — |
| BFCL v4 | 50.9 | 55.7 | 47.9 | 50.8 | 36.8 |
Scores in %. Bold marks the best score in each row. Benchmark results are sourced from the original K2-Horizon-3.7B model description.
GGUF Files
| File | Format | Description |
|---|---|---|
K2-Horizon-3.7B-nvfp4.gguf |
NVFP4 | NVFP4 quantized model |
Usage
llama.cpp (CLI)
# Run inference
./llama-cli \
-m K2-Horizon-3.7B-nvfp4.gguf \
--temp 1.0 --top-p 0.95
llama-server (OpenAI-compatible API)
# Start the server
./llama-server \
-m K2-Horizon-3.7B-nvfp4.gguf \
--port 8080
Key Features
- Strong small-model baseline. A dense model evaluated on the same agentic, coding, and reasoning benchmarks as the rest of the family.
- 512K context. Native 524,288-token context from the midtraining stages onward.
- 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/},
}