--- pipeline_tag: text-generation library_name: transformers model_name: K2-Horizon-7B language: - en license: apache-2.0 datasets: - IFM/K2-Horizon-Pretrain-Data - IFM/K2-Horizon-Midtrain-Data tags: - k2-horizon - 7b - dense - open-weights - ifm --- # K2-Horizon-7B K2-Horizon-7B is the medium dense member of the K2-Horizon family: a 7B-core decoder-only model with a 512K context window.

K2-Horizon-7B benchmark results

## K2-Horizon-7B Highlights - **Strong dense baseline.** A 7B-class dense model evaluated across agentic, coding, long-context, and reasoning benchmarks. - **512K context.** Native 524,288-token context from the midtraining stages onward. - **Intermediate checkpoints.** Intermediate checkpoints are released so capability changes can be studied across training rather than at a single checkpoint. - **Fully open.** Training data and recipe, training code, and evaluation resources are public. ## 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. ### Full Results

		Reference models · weak to strong
Benchmark	K2-Horizon-7B	Reference 1	Reference 2	Reference 3
Math
HMMT Feb 2026 Competition mathematics	73.3	Gemma 4-12B 63.1	Qwen3.5-9B 65.7	Granite 4.2-8B 66.5
Coding
SWE-bench Verified Software engineering	70.6	Gemma 4-12B 30.6	Granite 4.2-8B 47.7	Qwen3.5-9B 50.8
Scientific Reasoning
HLE Expert-level reasoning	18.6	Granite 4.2-8B 9.7	Qwen3.5-9B 14.9	Gemma 4-12B 15.7
Coding
SciCode Scientific coding	31.6	Qwen3.5-9B 27.5	Mistral Small 4 28.0	Granite 4.2-8B 30.4
General
LCR Long-context reasoning	68.0	Granite 4.2-8B 43.3	Gemma 4-12B 61.7	Qwen3.5-9B 65.3
Coding
Terminal-Bench 2.1 Agentic terminal use	39.1	Granite 4.2-8B 18.4	Gemma 4-12B 27.3	Qwen3.5-9B 29.2
Agents
tau3-Banking Agentic tool use	25.8	Qwen3.5-9B 7.0	Granite 4.2-8B 7.6	Muse Glimmer-30B 24.0
BrowseComp Web browsing	59.0	DeepSeek V4 Flash-0423 53.5	GPT-5 54.9	LongCat Flash Thinking-2601 56.6

Scores in %. Bold marks the best score in each row. BrowseComp: our model uses the Discard-all@95k context-length protocol proposed in the DeepSeek-V3.2 technical report; comparison models may use different harnesses. ## Quickstart ### Serving vLLM, recipe at [recipes.vllm.ai/IFM](https://recipes.vllm.ai/IFM): ```shell 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_horizon ``` SGLang, 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-7B \ --revision 69ada542b68fe13d767479db2ab9421baff88681 \ --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=1.0`, `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-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].message print("Reasoning:", getattr(message, "reasoning_content", None)) print("Answer:", message.content) ``` 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. ### 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-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)) ``` ## 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=1.0`, `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.** Pin a revision tag when reproducibility matters. `main` is the default checkpoint; `base_final` and the `mid_*_final` tags identify training stages. ## Citation ```bibtex @misc{k2horizon2026, title = {Introducing K2 Horizon: Frontier Performance, Radically Open}, author = {{IFM Team}}, year = {2026}, url = {https://ifm.ai/blog/k2/}, } ```