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"

hermitdave commited on 13 days ago

Commit

2a8d4cc

verified ·

1 Parent(s): bc0a0ea

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Files changed (3) hide show

README_MLX-6bit.md +91 -0
README_MLX-8bit.md +91 -0
README_oQ4e.md +91 -0

README_MLX-6bit.md ADDED Viewed

	@@ -0,0 +1,91 @@

+---
+base_model: IFM/K2-Horizon-MoVA-36B-A4B
+tags:
+  - mlx
+  - apple-silicon
+  - text-generation
+  - oQ
+license: apache-2.0
+---
+# K2-Horizon-MoVA-36B-A4B MLX-6bit
+6-bit uniform quantization 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-MLX-6bit   --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-6bit",
+    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).

README_MLX-8bit.md ADDED Viewed

	@@ -0,0 +1,91 @@

+---
+base_model: IFM/K2-Horizon-MoVA-36B-A4B
+tags:
+  - mlx
+  - apple-silicon
+  - text-generation
+  - oQ
+license: apache-2.0
+---
+# K2-Horizon-MoVA-36B-A4B MLX-8bit
+8-bit uniform quantization 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-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.
+## 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).

README_oQ4e.md ADDED Viewed

	@@ -0,0 +1,91 @@

+---
+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).