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Cataloged from IFM/K2-Horizon-0.9B
All models by IFM βK2-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.
[Image blocked: K2-Horizon-0.9B 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.
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 [blocked].
vLLM (source at PR #53806, commit d9fd5f11):
SGLang, from a source checkout that includes sgl-project/sglang#37654. This is the recipe validated in the SGLang K2 Horizon cookbook:
[!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 throughchat_template_kwargs. Thinking is returned inreasoning_contentand the answer incontent.
Validated with Transformers 5.15.0, PyTorch 2.13.0, Safetensors 0.8.0.
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.temperature=0.6, top_p=0.95.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.main is the MOPD release checkpoint; mid1_75k and mid2_47k preserve the context-extension stages.vllm serve IFM/K2-Horizon-0.9B \ --trust-remote-code \ --dtype bfloat16 \ --max-model-len 131072 \ --hf-overrides '{"rope_parameters":{"rope_type":"yarn","factor":16.0,"original_max_position_embeddings":8192}}' \ --gpu-memory-utilization 0.85 \ --tensor-parallel-size 1 \ --reasoning-parser k2_horizon \ --enable-auto-tool-choice \ --tool-call-parser k2_horizonsglang 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 30000from 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].messageprint("Reasoning:", getattr(message, "reasoning_content", None))print("Answer:", message.content)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))@misc{k2horizon2026, title = {Introducing K2 Horizon: Frontier Performance, Radically Open}, author = {{IFM Team}}, year = {2026}, url = {https://ifm.ai/blog/k2/},}