Instructions to use hermitdave/K2-Horizon-7B-Uno-oQ4e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hermitdave/K2-Horizon-7B-Uno-oQ4e with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("hermitdave/K2-Horizon-7B-Uno-oQ4e")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use hermitdave/K2-Horizon-7B-Uno-oQ4e with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "hermitdave/K2-Horizon-7B-Uno-oQ4e"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "hermitdave/K2-Horizon-7B-Uno-oQ4e"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use hermitdave/K2-Horizon-7B-Uno-oQ4e with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "hermitdave/K2-Horizon-7B-Uno-oQ4e"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "hermitdave/K2-Horizon-7B-Uno-oQ4e"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "hermitdave/K2-Horizon-7B-Uno-oQ4e",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use hermitdave/K2-Horizon-7B-Uno-oQ4e with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "hermitdave/K2-Horizon-7B-Uno-oQ4e"

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default hermitdave/K2-Horizon-7B-Uno-oQ4e

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use hermitdave/K2-Horizon-7B-Uno-oQ4e with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "hermitdave/K2-Horizon-7B-Uno-oQ4e"

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "hermitdave/K2-Horizon-7B-Uno-oQ4e" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

K2-Horizon-7B-Uno-oQ4e / README.md

hermitdave

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	---
	base_model: IFM/K2-Horizon-7B-Uno
	tags:
	- mlx
	- apple-silicon
	- text-generation
	- uno
	- oQ
	- oQ4e
	license: apache-2.0
	---

	# K2-Horizon-7B-Uno oQ4e

	oQ4e (imatrix-enhanced mixed-precision ~4.5 BPW) quantization of the merged [IFM/K2-Horizon-7B-Uno](https://huggingface.co/IFM/K2-Horizon-7B-Uno) model — a diffusion-augmented LLM based on K2-Horizon-7B. The LoRA adapter is baked into the base weights, so it runs as a standard autoregressive model.

	Upstream model: [IFM/K2-Horizon-7B-Uno](https://huggingface.co/IFM/K2-Horizon-7B-Uno) by Institute of Foundation Models, released under Apache 2.0.

	Conversion: Merged and quantized to MLX format using [Hermes Agent](https://hermes-agent.nousresearch.com) with `oMLX`.

	## Quickstart

	```bash
	pip install -U mlx-lm

	python3 -m mlx_lm.generate \
	--model hermitdave/K2-Horizon-7B-Uno-oQ4e \
	--prompt "Explain step by step." \
	--max-tokens 512 --temp 1.0 --top-p 0.95
	```

	## Reasoning

	K2-Horizon-7B is a reasoning model. Always use `reasoning_effort="high"`:

	```python
	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-oQ4e",
	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](https://github.com/jundot/omlx/pull/3441).

	## Citation

	```bibtex
	@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).