K2-Horizon-0.9B-Uno
This model accompanies the paper Unlocking Lossless Speedups in LLMs via Discrete Diffusion.
Project page: https://s-sahoo.github.io/uno/
Code: https://github.com/ifm-ai/uno
K2-Horizon-0.9B-Uno is a conditional-LoRA adapter for diffusion-style decoding with K2-Horizon-0.9B. This repository contains the adapter only. The base-model weights are hosted separately in IFM/K2-Horizon-0.9B.
Code
Selected evaluation scripts are available in scripts/k2_horizon. The full evaluation suite will be released soon.
Evaluation results
The main number is the benchmark accuracy and the subscript is TPF. --
denotes an unavailable TPF.
| Ours | |
|---|---|
| Benchmark | Uno 0.9B |
| Long-Context Reasoning | |
| AA-LCR | 18.01.81 |
| Science and Knowledge | |
| ARC-Challenge | 78.21.46 |
| GPQA-Diamond (avg@16) | 27.31.59 |
| HLE (Full) | 5.41.53 |
| AA-Omniscience | 7.21.64 |
| Math | |
| AIME 2024 | 43.31.50 |
| AIME 2025 (avg@16) | 41.71.54 |
| AIME 2026 (avg@16) | 48.51.52 |
| GSM8K | 88.21.58 |
| HMMT February 2026 (avg@16) | 25.8-- |
| MATH500 (Full) | 86.21.57 |
| Coding | |
| HumanEval | 62.81.79 |
| HumanEval+ (pass@1) | 79.9-- |
| LiveCodeBench v6 (avg@3) | 37.4-- |
| MBPP | 70.41.53 |
| MBPP+ (pass@1) | 68.0-- |
| Instruction Following | |
| IFEval (strict instruction) | 80.8%1.72 |
Conversion provenance
conversion_summary.json records the adapter hash, source checkpoint,
base-weight match, and tensor-key compatibility validation.
The public K2-Horizon base is a schema migration of the exact local base used for training. The base weight, index, and tokenizer hashes match; only the public Python architecture name changed from K2Aurora to K2Horizon. All 392 adapter tensors map to valid target weights in the public base model.
Citation
If you find this model useful, please cite:
@misc{k2_horizon_09b_uno,
title = {K2-Horizon-0.9B-Uno},
author = {Institute of Foundation Models},
year = {2026},
howpublished = {\url{https://huggingface.co/IFM/K2-Horizon-0.9B-Uno}},
}