README.md

---
pipeline_tag: text-generation
library_name: transformers
model_name: K2-Horizon-0.9B
language:
- en
- zh
license: apache-2.0
license_name: internal-only
license_link: LICENSE
tags:
- k2-horizon
- 0.9b
- dense
- reasoning
- knowledge-distillation
- ifm
---

# K2-Horizon-0.9B

K2-Horizon-0.9B is the compact dense member of the K2-Horizon family: a 0.9B-class decoder-only model with a 128K context window.

<p align="center">
  <img src="assets/k2-horizon-0.9b-benchmarks.png" alt="K2-Horizon-0.9B benchmark results" width="100%">
</p>

## K2-Horizon-0.9B Highlights

- **Compact reasoning model.** A 0.9B-class dense model evaluated across mathematics, coding, science, and tool-use benchmarks.
- **128K context.** Supports up to 131,072 tokens with YaRN RoPE scaling.
- **Multi-teacher distillation.** Trained with domain teachers for math and code, STEM, and instruction following.
<!-- - **Intermediate checkpoints.** Intermediate checkpoints will be released so capability changes can be studied across training rather than at a single checkpoint. -->
- **Fully open.** Training data/recipe and the training code will be made public.

## Benchmark Results

The chart at the top of this card shows K2-Horizon-0.9B against selected reference models. The table below lists every comparison model used in the figure.

### Full Results


<!-- TABLE:START -->
<div style="font-family:-apple-system,BlinkMacSystemFont,'Segoe UI',Roboto,sans-serif;margin:0 auto;padding:8px 0 16px;overflow-x:auto"><table style="display:table;width:100%;table-layout:fixed;border-collapse:collapse;font-size:12px;margin:0"><thead><tr><th style="width:30%;border-bottom:none"></th><th colspan="4" style="padding:6px 4px 2px;text-align:center;font-size:11px;font-weight:600;letter-spacing:0.04em;text-transform:uppercase;opacity:0.65;border-bottom:1px solid rgba(128,128,128,0.25)">Reference models</th></tr><tr><th style="padding:10px 6px;text-align:left;border-bottom:2px solid #2450D6"></th><th style="padding:10px 3px;text-align:center;font-weight:600;border-bottom:2px solid #2450D6;color:#2450D6;font-size:12.5px;line-height:1.2;width:17.50%;overflow-wrap:anywhere;background:rgba(36,80,214,0.08);">K2-Horizon-0.9B</th><th style="padding:10px 3px;text-align:center;font-weight:600;border-bottom:2px solid #2450D6;color:#2450D6;font-size:12.5px;line-height:1.2;width:17.50%;overflow-wrap:anywhere;">Qwen3.5-0.8B</th><th style="padding:10px 3px;text-align:center;font-weight:600;border-bottom:2px solid #2450D6;color:#2450D6;font-size:12.5px;line-height:1.2;width:17.50%;overflow-wrap:anywhere;">OpenBMB-1B</th><th style="padding:10px 3px;text-align:center;font-weight:600;border-bottom:2px solid #2450D6;color:#2450D6;font-size:12.5px;line-height:1.2;width:17.50%;overflow-wrap:anywhere;">Qwen3.5-2B</th></tr></thead><tbody><tr><td style="padding:7px 4px 7px 10px;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:12.5px;font-weight:600"># Params</td><td style="padding:6px 2px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:12.5px;line-height:1.2;background:rgba(36,80,214,0.08);">0.9B</td><td style="padding:6px 2px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:12.5px;line-height:1.2;">0.8B</td><td style="padding:6px 2px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:12.5px;line-height:1.2;">1B</td><td style="padding:6px 2px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:12.5px;line-height:1.2;">2B</td></tr><tr><td style="padding:7px 4px 7px 10px;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:12.5px;font-weight:600"># Activated params</td><td style="padding:6px 2px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:12.5px;line-height:1.2;background:rgba(36,80,214,0.08);">0.9B</td><td style="padding:6px 2px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:12.5px;line-height:1.2;">0.8B</td><td style="padding:6px 2px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:12.5px;line-height:1.2;">1B</td><td style="padding:6px 2px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:12.5px;line-height:1.2;">2B</td></tr><tr><td style="padding:7px 4px 7px 10px;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:12.5px;font-weight:600">Architecture</td><td style="padding:6px 2px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:12.5px;line-height:1.2;background:rgba(36,80,214,0.08);">Dense</td><td style="padding:6px 2px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:12.5px;line-height:1.2;">Dense</td><td style="padding:6px 2px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:12.5px;line-height:1.2;">Dense</td><td style="padding:6px 2px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:12.5px;line-height:1.2;">Dense</td></tr><tr><td colspan="5" style="padding:6px 10px;font-weight:600;font-size:12.5px;color:#2450D6;border-bottom:1px solid rgba(36,80,214,0.25);background:rgba(36,80,214,0.12)">Math</td></tr><tr><td style="padding:6px 4px 6px 10px;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle"><div style="font-size:12.5px;font-weight:600;line-height:1.2;color:inherit">AIME 2025</div><div style="margin-top:2px;font-size:10px;opacity:0.65">Competition mathematics</div></td><td style="padding:6px 2px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:12.5px;line-height:1.2;background:rgba(36,80,214,0.08);"><strong>41.7</strong></td><td style="padding:6px 2px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:12.5px;line-height:1.2;">1.0</td><td style="padding:6px 2px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:12.5px;line-height:1.2;">40.4</td><td style="padding:6px 2px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:12.5px;line-height:1.2;">34.2</td></tr><tr><td style="padding:6px 4px 6px 10px;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle"><div style="font-size:12.5px;font-weight:600;line-height:1.2;color:inherit">AIME 2026</div><div style="margin-top:2px;font-size:10px;opacity:0.65">Competition mathematics</div></td><td style="padding:6px 2px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:12.5px;line-height:1.2;background:rgba(36,80,214,0.08);"><strong>48.5</strong></td><td style="padding:6px 2px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:12.5px;line-height:1.2;">0.2</td><td style="padding:6px 2px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:12.5px;line-height:1.2;">40.4</td><td style="padding:6px 2px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:12.5px;line-height:1.2;">38.8</td></tr><tr><td style="padding:6px 4px 6px 10px;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle"><div style="font-size:12.5px;font-weight:600;line-height:1.2;color:inherit">HMMT Feb 2026</div><div style="margin-top:2px;font-size:10px;opacity:0.65">Competition mathematics</div></td><td style="padding:6px 2px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:12.5px;line-height:1.2;background:rgba(36,80,214,0.08);"><strong>25.8</strong></td><td style="padding:6px 2px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:12.5px;line-height:1.2;">0.6</td><td style="padding:6px 2px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:12.5px;line-height:1.2;">23.3</td><td style="padding:6px 2px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:12.5px;line-height:1.2;">22.7</td></tr><tr><td colspan="5" style="padding:6px 10px;font-weight:600;font-size:12.5px;color:#2450D6;border-bottom:1px solid rgba(36,80,214,0.25);background:rgba(36,80,214,0.12)">Scientific Reasoning</td></tr><tr><td style="padding:6px 4px 6px 10px;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle"><div style="font-size:12.5px;font-weight:600;line-height:1.2;color:inherit">GPQA Diamond</div><div style="margin-top:2px;font-size:10px;opacity:0.65">Graduate-level science QA</div></td><td style="padding:6px 2px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:12.5px;line-height:1.2;background:rgba(36,80,214,0.08);"><strong>27.3</strong></td><td style="padding:6px 2px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:12.5px;line-height:1.2;">11.9</td><td style="padding:6px 2px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:12.5px;line-height:1.2;">26.3</td><td style="padding:6px 2px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:12.5px;line-height:1.2;">54.9</td></tr><tr><td colspan="5" style="padding:6px 10px;font-weight:600;font-size:12.5px;color:#2450D6;border-bottom:1px solid rgba(36,80,214,0.25);background:rgba(36,80,214,0.12)">Coding</td></tr><tr><td style="padding:6px 4px 6px 10px;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle"><div style="font-size:12.5px;font-weight:600;line-height:1.2;color:inherit">HumanEval+</div><div style="margin-top:2px;font-size:10px;opacity:0.65">Code generation</div></td><td style="padding:6px 2px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:12.5px;line-height:1.2;background:rgba(36,80,214,0.08);"><strong>79.9</strong></td><td style="padding:6px 2px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:12.5px;line-height:1.2;">16.5</td><td style="padding:6px 2px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:12.5px;line-height:1.2;">65.2</td><td style="padding:6px 2px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:12.5px;line-height:1.2;">75.6</td></tr><tr><td style="padding:6px 4px 6px 10px;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle"><div style="font-size:12.5px;font-weight:600;line-height:1.2;color:inherit">MBPP+</div><div style="margin-top:2px;font-size:10px;opacity:0.65">Code generation</div></td><td style="padding:6px 2px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:12.5px;line-height:1.2;background:rgba(36,80,214,0.08);"><strong>68.0</strong></td><td style="padding:6px 2px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:12.5px;line-height:1.2;">35.4</td><td style="padding:6px 2px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:12.5px;line-height:1.2;">60.6</td><td style="padding:6px 2px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:12.5px;line-height:1.2;">67.7</td></tr><tr><td style="padding:6px 4px 6px 10px;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle"><div style="font-size:12.5px;font-weight:600;line-height:1.2;color:inherit">LiveCodeBench v6</div><div style="margin-top:2px;font-size:10px;opacity:0.65">Competitive coding</div></td><td style="padding:6px 2px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:12.5px;line-height:1.2;background:rgba(36,80,214,0.08);"><strong>37.4</strong></td><td style="padding:6px 2px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:12.5px;line-height:1.2;">6.6</td><td style="padding:6px 2px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:12.5px;line-height:1.2;">33.5</td><td style="padding:6px 2px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:12.5px;line-height:1.2;">29.8</td></tr><tr><td colspan="5" style="padding:6px 10px;font-weight:600;font-size:12.5px;color:#2450D6;border-bottom:1px solid rgba(36,80,214,0.25);background:rgba(36,80,214,0.12)">Agents</td></tr><tr><td style="padding:6px 4px 6px 10px;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle"><div style="font-size:12.5px;font-weight:600;line-height:1.2;color:inherit">BFCL v4</div><div style="margin-top:2px;font-size:10px;opacity:0.65">Function calling</div></td><td style="padding:6px 2px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:12.5px;line-height:1.2;background:rgba(36,80,214,0.08);"><strong>28.0</strong></td><td style="padding:6px 2px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:12.5px;line-height:1.2;">25.3</td><td style="padding:6px 2px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:12.5px;line-height:1.2;">25.2</td><td style="padding:6px 2px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:12.5px;line-height:1.2;">43.6</td></tr></tbody></table></div>
<!-- TABLE:END -->

Scores in %. Bold highlights K2-Horizon-0.9B; Qwen3.5-2B is included as a larger reference model. Protocol and provenance details are in the [Technical Appendix](APPENDIX.md#evaluation).

## Quickstart

### Serving


vLLM (source at [PR #53806](https://github.com/vllm-project/vllm/pull/53806), commit `d9fd5f11`):

```shell
vllm serve IFM/K2-Horizon-0.9B \
  --trust-remote-code \
  --dtype bfloat16 \
  --max-model-len 131072 \
  --hf-overrides '{"rope_parameters":{rope_type: yarn, factor: 16, original_max_position_embeddings: 8192, rope_theta: 1000000, beta_fast: 128, beta_slow: 4}' \
  --gpu-memory-utilization 0.85 \
  --tensor-parallel-size 1 \
  --reasoning-parser k2_horizon \
  --enable-auto-tool-choice \
  --tool-call-parser k2_horizon
```

Use an exact branch name from the inventory with vLLM's `--revision` option. For example, `--revision pretrain_600000` selects the final checkpoint of Pretraining, at step 600,000.

SGLang, from a source checkout that includes [sgl-project/sglang#37654](https://github.com/sgl-project/sglang/pull/37654). This is the recipe validated in the [SGLang K2 Horizon cookbook](https://docs.sglang.io/cookbook/autoregressive/IFM/K2-Horizon):

```shell
sglang serve \
  --model-path IFM/K2-Horizon-0.9B \
  --revision 9b9ec1f7e17f62ed218df542687a144116219d84 \
  --tp 1 \
  --dtype bfloat16 \
  --attention-backend fa3 \
  --reasoning-parser k2_horizon \
  --host 0.0.0.0 \
  --port 30000
```

### API Usage

> [!Tip]
> Recommended settings: `reasoning_effort="high"`, `temperature=0.6`, `top_p=0.95`, and at least 32,768 output tokens.
> Reasoning depth is selected per request through `chat_template_kwargs`. Thinking is returned in `reasoning_content` and the answer in `content`.

```python
from openai import OpenAI

client = OpenAI(base_url="http://localhost:30000/v1", api_key="EMPTY")
response = client.chat.completions.create(
    model="IFM/K2-Horizon-0.9B",
    messages=[{"role": "user", "content": "Explain the result step by step."}],
    temperature=0.6,
    top_p=0.95,
    max_tokens=32768,
    extra_body={"chat_template_kwargs": {"reasoning_effort": "high"}},
)
message = response.choices[0].message
print("Reasoning:", getattr(message, "reasoning_content", None))
print("Answer:", message.content)
```

### Transformers

Validated with Transformers 5.15.0, PyTorch 2.13.0, Safetensors 0.8.0.

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "IFM/K2-Horizon-0.9B"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id, device_map="auto", dtype="bfloat16", low_cpu_mem_usage=True, trust_remote_code=True
)

inputs = tokenizer("Explain why long-context evaluation is difficult.", return_tensors="pt").to(model.device)
inputs.pop("token_type_ids", None)
outputs = model.generate(**inputs, max_new_tokens=32768, temperature=1.0, top_p=0.95, do_sample=True)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Training Overview

The table below lists the training stages in order and the purpose of each stage.

Training steps are counted within each stage or phase. Token budgets cover only the additional training in that stage or phase.

Each stage or phase continues from the final checkpoint of the preceding stage or phase.

During RL, training branches into seven expert models, which are then merged, as described below.


| Training stage | Training steps | Training tokens | Sequence length | Purpose |
| --- | --- | --- | --- | --- |
| Pretraining | 600000 | 5T | 8K | Pretraining. |
| Midtraining — Stage 1 | 75000 | 393B | 32K | Context extension. |
| Midtraining — Stage 2 | 47684 | 200B | 128K | Context extension. |
| RL | To be updated | To be updated | 128K | We trained seven expert models from the final checkpoint of Midtraining Stage 2: math1, code1, math2a, math2b, code2, IF, and stem. We then merged the expert models. |
| MOPD | 249 | To be updated | 128K | Resolve structural interference and performance degradation caused by weight merging, aligning multi-domain specialist capabilities in the behavioral space via on-policy distillation. |

## Release Artifacts

The tables below list the release artifacts for **K2-Horizon-0.9B**, their availability, and the expected release dates for remaining items.

**Last updated:** 2026-09-11

**Status:**

- **Available** — fully released for the scope listed;
- **Partial** — some items are available, with remaining items listed in the notes;
- **In Progress** — being prepared for release but not yet available.

### Artifact Index

| Artifact | Link | Status | Remaining items / expected availability |
| --- | --- | --- | --- |
| Model card | [Hugging Face](https://huggingface.co/IFM/K2-Horizon-0.9B) | Available | N/A |
| Training logs | [W&B](https://wandb.ai/llm360/K2-Horizon-0.9B) | Available | N/A |
| Blog post | [Blog post](https://ifm.ai/blog/k2/) | Available | N/A |
| Checkpoints | [Checkpoint inventory](#checkpoint-inventory) | Partial | See details below |
| Technical report | Not yet available | In Progress | End of September 2026 |
| Code repository | [GitHub](https://github.com/ifm-ai/xllm) | In Progress | End of September 2026 |

### Checkpoint Inventory

**Model repository:** [IFM/K2-Horizon-0.9B](https://huggingface.co/IFM/K2-Horizon-0.9B)

Branch names below refer to this repository. Patterns containing `*` group branches by training stage or phase. The `*` is a placeholder for a training-step number, not a literal branch name. Intermediate checkpoint groups exclude the final checkpoint listed separately; a pattern does not imply that a checkpoint is available at every step.

For example, `pretrain_600000` is the checkpoint saved at training step 600,000 within Pretraining stage, and is the final checkpoint of that stage. The numeric suffix is the step within the named stage, not the cumulative step across all training. Thus, `mid_1_75000` refers to step 75,000 within Midtraining Stage 1.

For a partially released group, the available checkpoints and the remaining checkpoints are listed in the notes.

| Checkpoint | Branch / repository | Status | Remaining items / expected availability |
| --- | --- | --- | --- |
| Pretrain Intermediate Checkpoints | `pretrain_*` | Available | N/A |
| Pretrain Final Checkpoint | `pretrain_600000` | Available | N/A |
| Midtrain Stage 1 Intermediate Checkpoints | `mid_1_*` | Available | N/A |
| Midtrain Stage 1 Final Checkpoint | `mid_1_75000` | Available | N/A |
| Midtrain Stage 2 Intermediate Checkpoints | `mid_2_*` | Available | N/A |
| Midtrain Stage 2 Final Checkpoint | `mid_2_47684` | Available | N/A |
| RL Math1 Expert Checkpoint | `rl_math1` | In Progress | Mid-September 2026 |
| RL Code1 Expert Checkpoint | `rl_code1` | In Progress | Mid-September 2026 |
| RL Math2a Expert Checkpoint | `rl_math2a` | In Progress | Mid-September 2026 |
| RL Math2b Expert Checkpoint | `rl_math2b` | In Progress | Mid-September 2026 |
| RL Code2 Expert Checkpoint | `rl_code2` | In Progress | Mid-September 2026 |
| RL IF Expert Checkpoint | `rl_if` | In Progress | Mid-September 2026 |
| RL Stem Expert Checkpoint | `rl_stem` | In Progress | Mid-September 2026 |
| RL Merged Final Checkpoint | `rl_merged` | Available | N/A |
| RL MOPD Final Checkpoint | `rl_mopd` | Available | N/A |


## Best Practices

1. **Reasoning effort: always `high`.** All reported results use high reasoning effort. Pass `{"chat_template_kwargs": {"reasoning_effort": "high"}}` on every request; `medium` and `low` trade accuracy for speed and are not recommended for evaluation.
2. **Sampling parameters.** `temperature=0.6`, `top_p=0.95`.
3. **Output length.** Allow at least 32,768 output tokens so reasoning is never cut off. Truncated reasoning is a failed response, not a shorter one.
4. **Serving.** Use the validated SGLang recipe above: BF16, TP=1, FlashAttention-3. Full recipes for every K2-Horizon size, with measured H200 latency and throughput, are in the [SGLang cookbook](https://docs.sglang.io/cookbook/autoregressive/IFM/K2-Horizon).
5. **Parsers.** Enable the `k2_horizon` reasoning parser for chat, and add the `k2_horizon` tool-call parser for agent use. Leave both off for plain completion-style generation.
6. **Revisions.** `main` is the MOPD release checkpoint; `mid1_75k` and `mid2_47k` preserve the context-extension stages.

## Citation

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