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.

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.

• Fully open. Training data/recipe and the training code are 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

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.

Quickstart

Serving

vLLM (source at PR #53806, commit d9fd5f11):

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. This is the recipe validated in the SGLang K2 Horizon cookbook:

sglang serve \
  --model-path IFM/K2-Horizon-0.9B \
  --revision 9fa6faa55fe1c9eb008bb241cc6fb7e4536d0e91 \
  --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.

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", "tool_call_format": "xml"}},
)
message = response.choices[0].message
print("Reasoning:", getattr(message, "reasoning_content", None))
print("Answer:", message.content)

Our model supports multiple tool-call formats, which can be changed with chat_template_kwargs. The supported values are json, xml, and xml_typed. The default is xml. Keep --tool-call-parser k2_horizon enabled to parse the selected format.

Transformers

Validated with Transformers 5.15.0, PyTorch 2.13.0, Safetensors 0.8.0.

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 multiple expert models, which are then merged, as described below.

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: 2026-09-28

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

Checkpoint Inventory

Model repository: 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.

[!IMPORTANT] Note: - Released K2-Horizon Hugging Face checkpoints (e.g. Huggingface) can be used for inference, evaluation, and downstream fine-tuning (including SFT). Training behavior in the bundled Hugging Face implementation may differ from native xLLM, including the auxiliary load-balancing loss. To continue the original pretraining with xLLM's training behavior, use the native xLLM checkpoint and xLLM runtime.

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. medium and low effort settings are not recommended except for research on reasoning efforts.
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.
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

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