Instructions to use hermitdave/K2-Horizon-7B-oQ6e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hermitdave/K2-Horizon-7B-oQ6e 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-oQ6e")

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-oQ6e 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-oQ6e"

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-oQ6e"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use hermitdave/K2-Horizon-7B-oQ6e 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-oQ6e"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "hermitdave/K2-Horizon-7B-oQ6e"
# 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-oQ6e",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use hermitdave/K2-Horizon-7B-oQ6e 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-oQ6e"

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-oQ6e

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use hermitdave/K2-Horizon-7B-oQ6e 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-oQ6e"

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-oQ6e" \
  --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 oQ6e

oQ6e (imatrix-enhanced mixed-precision 6-bit) conversion of IFM/K2-Horizon-7B, a dense reasoning model from the K2 Horizon family (7B parameters, 512K context).

Upstream model: IFM/K2-Horizon-7B by the IFM Team, released under Apache-2.0.

Conversion: Quantized to MLX format using Hermes Agent with mlx-lm and oMLX.

Quickstart

pip install -U mlx-lm

python3 -m mlx_lm.generate   --model hermitdave/K2-Horizon-7B-oQ6e   --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:

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-oQ6e",
    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-7B
SWE-bench Verified	70.6
Terminal-Bench 2.1	39.06
tau3-Banking	25.8
BrowseComp	59.0
HMMT Feb 2026	73.3

Scores in %. See model card for full results.

oMLX Patch

K2-Horizon requires oMLX v0.6.4+ with the K2-Horizon support patch (PR #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\|>`
`<ifm\|tool_call>`, `<ifm\|arg_key>`, `<ifm\|arg_value>`	Tool call structure

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

Citation

@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).

Downloads last month: 282

Safetensors

Model size

9B params

Tensor type

U32

BF16

MLX

Hardware compatibility

6-bit

Model tree for hermitdave/K2-Horizon-7B-oQ6e

Base model

IFM/K2-Horizon-7B

Quantized

(18)

this model