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hermitdave/K2-Horizon-7B-Uno-merged
K2-Horizon-7B-Uno-merged is a text generation model from hermitdave. Use it when you need the model to write or continue text. It is set up for mlx. The card lists the license as apache-2.0.
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 st…
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From the Hugging Face model README
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.
K2-Horizon-7B-Uno combines:
| Spec | Value |
|---|---|
| Total params | ~7.6B (with merged LoRA) |
| Architecture | Dense + LoRA-augmented |
| Layers | 32 |
| Context length | 512K native |
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
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)
K2-Horizon requires oMLX v0.6.4+ with the K2-Horizon support patch:
k2_horizon model type<ifm|think>, <ifm|think_fast>, <ifm|think_faster>)<ifm|tool_call>, <ifm|arg_key>, <ifm|arg_value>)@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}},
}
Apache 2.0 (same as upstream).