Text Generation
MLX
Safetensors
k2_horizon
apple-silicon
oQ
conversational
custom_code
8-bit precision
Instructions to use hermitdave/K2-Horizon-MoVA-36B-A4B-MLX-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use hermitdave/K2-Horizon-MoVA-36B-A4B-MLX-8bit 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-MoVA-36B-A4B-MLX-8bit") 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
- Pi
How to use hermitdave/K2-Horizon-MoVA-36B-A4B-MLX-8bit 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-MoVA-36B-A4B-MLX-8bit"
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-MoVA-36B-A4B-MLX-8bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use hermitdave/K2-Horizon-MoVA-36B-A4B-MLX-8bit 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-MoVA-36B-A4B-MLX-8bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "hermitdave/K2-Horizon-MoVA-36B-A4B-MLX-8bit" # 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-MoVA-36B-A4B-MLX-8bit", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use hermitdave/K2-Horizon-MoVA-36B-A4B-MLX-8bit 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-MoVA-36B-A4B-MLX-8bit"
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-MoVA-36B-A4B-MLX-8bit
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use hermitdave/K2-Horizon-MoVA-36B-A4B-MLX-8bit 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-MoVA-36B-A4B-MLX-8bit"
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-MoVA-36B-A4B-MLX-8bit" \ --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"
File size: 2,047 Bytes
ecaf5f4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 | # K2-Horizon-MoVA-36B-A4B MLX
MLX conversions 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).
## Available Formats
| Format | Size | Quality | Use Case |
|--------|------|---------|----------|
| **oQ4e** | ~21 GB | ~uniform 6-bit quality | Best quality-per-GB |
| **6-bit** | ~28 GB | High | Quality-focused, fits 40+ GB |
| **8-bit** | ~40 GB | Near-lossless | Reference quality, 64 GB+ |
## Quickstart
```bash
pip install -U mlx-lm
# Generate
python3 -m mlx_lm.generate \
--model hermitdave/K2-Horizon-MoVA-36B-A4B-MLX-8bit \
--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-MLX-8bit",
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.
## 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).
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