Instructions to use mlx-community/K2-Horizon-MoVA-36B-A4B-oQ4e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlx-community/K2-Horizon-MoVA-36B-A4B-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("mlx-community/K2-Horizon-MoVA-36B-A4B-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 mlx-community/K2-Horizon-MoVA-36B-A4B-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 "mlx-community/K2-Horizon-MoVA-36B-A4B-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": "mlx-community/K2-Horizon-MoVA-36B-A4B-oQ4e"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use mlx-community/K2-Horizon-MoVA-36B-A4B-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 "mlx-community/K2-Horizon-MoVA-36B-A4B-oQ4e"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "mlx-community/K2-Horizon-MoVA-36B-A4B-oQ4e"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "mlx-community/K2-Horizon-MoVA-36B-A4B-oQ4e",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use mlx-community/K2-Horizon-MoVA-36B-A4B-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 "mlx-community/K2-Horizon-MoVA-36B-A4B-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 mlx-community/K2-Horizon-MoVA-36B-A4B-oQ4e

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use mlx-community/K2-Horizon-MoVA-36B-A4B-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 "mlx-community/K2-Horizon-MoVA-36B-A4B-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 "mlx-community/K2-Horizon-MoVA-36B-A4B-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-MoVA-36B-A4B-oQ4e / README_oQ4e.md

hermitdave

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	---
	base_model: IFM/K2-Horizon-MoVA-36B-A4B
	tags:
	- mlx
	- apple-silicon
	- text-generation
	- oQ
	license: apache-2.0
	---

	# K2-Horizon-MoVA-36B-A4B oQ4e

	oQ4e (imatrix-enhanced mixed-precision 4-bit) conversion of [IFM/K2-Horizon-MoVA-36B-A4B](https://huggingface.co/IFM/K2-Horizon-MoVA-36B-A4B), a sparse Mixture-of-Experts model with Mixture-of-Values attention (36B total / 4B active parameters, 512K context).

	Upstream model: [IFM/K2-Horizon-MoVA-36B-A4B](https://huggingface.co/IFM/K2-Horizon-MoVA-36B-A4B) by the IFM Team, released under Apache-2.0.

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

	## Quickstart

	```bash
	pip install -U mlx-lm

	python3 -m mlx_lm.generate --model hermitdave/K2-Horizon-MoVA-36B-A4B-oQ4e --prompt "Explain why long-context evaluation is difficult." --max-tokens 512 --temp 1.0 --top-p 0.95
	```

	## Reasoning

	K2-Horizon is a reasoning model. Always use `reasoning_effort="high"` for best results:

	```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-MoVA-36B-A4B-oQ4e",
	messages=[{"role": "user", "content": "Explain quantum entanglement."}],
	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)
	```

	## Benchmark Results

	\| Benchmark \| K2-Horizon-MoVA-36B-A4B \|
	\|-----------\|------------------------\|
	\| tau3-Banking (Agentic tool use) \| 26.8 \|
	\| Terminal-Bench 2.1 (Agentic terminal use) \| 58.6 \|
	\| GPQA Diamond (Graduate-level science QA) \| 80.8 \|
	\| AA-LCR (Long-context reasoning) \| 66.3 \|

	Scores in %. See [model card](https://huggingface.co/IFM/K2-Horizon-MoVA-36B-A4B) for full results.

	## oMLX Patch

	K2-Horizon requires oMLX v0.6.4+ with the [K2-Horizon support patch (PR #3441)](https://github.com/jundot/omlx/pull/3441). This patch adds:

	- `k2_horizon` model type support
	- Reasoning content handling (`<ifm\|think>` tags)
	- Tool call parsing (plain text and XML formats)
	- Multi-turn conversation support

	Without this patch, oMLX will refuse to load K2-Horizon models with `ValueError: Model type k2_horizon not supported`.

	## Chat Template

	K2-Horizon uses IFM's custom chat template with reasoning and tool calling support. Key tags:

	\| Tag \| Purpose \|
	\|-----\|---------\|
	\| `<ifm\\|think>`, `<ifm\\|think_fast>`, `<ifm\\|think_faster>` \| Thinking blocks \|
	\| `<\\|ifm\\|im_start\|>`, `<\\|ifm\\|im_end\\|>` \| Message delimiters \|
	\| `<ifm\\|tool_call>`, `<ifm\\|arg_key>`, `<ifm\\|arg_value>` \| Tool call structure \|

	All tags are automatically stripped by oMLX before responses reach users.

	## Citation

	```bibtex
	@misc{k2horizon2026,
	title = {Introducing K2 Horizon: Frontier Performance, Radically Open},
	author = {{IFM Team}},
	year = {2026},
	url = {https://ifm.ai/blog/k2/},
	}
	```

	## License

	Apache-2.0 (same as upstream).