K2-Horizon-7B-Uno Merged
Merged version of IFM/K2-Horizon-7B-Uno, a diffusion-augmented LLM based on K2-Horizon-7B. This repository contains the LoRA adapter merged directly into the base model weights using PyTorch + PEFT, so it runs as a standard autoregressive model without needing the custom Ψ-Spec sampler.
Upstream model: IFM/K2-Horizon-7B-Uno by Institute of Foundation Models, released under Apache 2.0.
Base model: IFM/K2-Horizon-7B
Conversion: Merged using PyTorch + PEFT, validated with text generation, then quantized to MLX format using Hermes Agent.
Architecture
K2-Horizon-7B-Uno combines:
- AR pathway: Standard K2-Horizon-7B weights (next-token prediction)
- Diffusion pathway: LoRA-based diffusion adapters (merged into AR weights)
| Spec | Value |
|---|---|
| Total params | ~7.6B (with merged LoRA) |
| Architecture | Dense + LoRA-augmented |
| Layers | 32 |
| Context length | 512K native |
Quickstart
pip install -U mlx-lm
python3 -m mlx_lm.generate \
--model hermitdave/K2-Horizon-7B-Uno-merged \
--prompt "Explain why long-context evaluation is difficult." \
--max-tokens 512 --temp 1.0 --top-p 0.95
Reasoning
K2-Horizon-7B is a reasoning model. Always use reasoning_effort="high":
from openai import OpenAI
client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")
response = client.chat.completions.create(
model="hermitdave/K2-Horizon-7B-Uno-merged",
messages=[{"role": "user", "content": "Explain step by step."}],
extra_body={"chat_template_kwargs": {"reasoning_effort": "high"}},
)
print("Reasoning:", getattr(response.choices[0].message, "reasoning_content", None))
print("Answer:", response.choices[0].message.content)
oMLX Patch
K2-Horizon requires oMLX v0.6.4+ with the K2-Horizon support patch:
- Registers
k2_horizonmodel type - Recognizes IFM thinking tags (
<ifm|think>,<ifm|think_fast>,<ifm|think_faster>) - Parses IFM tool call format (
<ifm|tool_call>,<ifm|arg_key>,<ifm|arg_value>) - Strips tags in streaming and non-streaming paths
Citation
@misc{k2_horizon_7b_uno,
title = {K2-Horizon-7B-Uno},
author = {Institute of Foundation Models},
year = {2026},
howpublished = {\url{https://huggingface.co/IFM/K2-Horizon-7B-Uno}},
}
License
Apache 2.0 (same as upstream).