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Cataloged from IFM/K2-Horizon-7B-FP8
All models by IFM →Pre-training Code - Post-training Code - Pretraining Data - Midtraining Data
[!NOTE] This repository contains an FP8-quantized version of IFM/K2-Horizon-7B.
All linear layers except
lm_headare quantized to FP8:
- Weights: static FP8, one scale per 128*128 block.
- Activations: dynamic FP8, one scale per 1*128 group along the input-channel dim.
The FP8 model performs closely in line with the original BF16 model on our evaluations, while reducing memory footprint and enabling faster inference on FP8-capable hardware.
K2-Horizon-7B is the medium dense member of the K2-Horizon family: a 7B-core decoder-only model with a 512K context window.
[Image blocked: K2-Horizon-7B benchmark results]
The chart at the top of this card shows K2-Horizon-7B against selected reference models. The table below lists every comparison model used in the figure.
Scores in %. Bold marks the best score in each row. BrowseComp uses the Discard-all@95k context-length protocol proposed in the DeepSeek-V3.2 technical report; comparison models may use different harnesses.
vLLM, recipe at recipes.vllm.ai/IFM:
SGLang, this is the recipe validated in the SGLang K2 Horizon cookbook:
[!Tip] Recommended settings:
reasoning_effort="high",temperature=1.0,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.
Our model supports multiple tool calls 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.
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=1.0, 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 default checkpoint; base_final and the mid_*_final tags identify training stages.vllm serve IFM/K2-Horizon-7B \ --trust-remote-code \ --dtype bfloat16 \ --max-model-len 131072 \ --tensor-parallel-size 1 \ --reasoning-parser k2_horizon \ --enable-auto-tool-choice \ --tool-call-parser k2_horizonsglang serve \ --model-path IFM/K2-Horizon-7B \ --revision 69ada542b68fe13d767479db2ab9421baff88681 \ --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-7B", messages=[{"role": "user", "content": "Explain the result step by step."}], temperature=1.0, top_p=0.95, max_tokens=32768, extra_body={"chat_template_kwargs": {"reasoning_effort": "high", "tool_call_format": "xml"}},)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-7B"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/},}