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Cataloged from IFM/K2-Horizon-MoVA-36B-A4B-GGUF
All models by IFM →[!NOTE] This repository contains GGUF versions of the IFM/K2-Horizon-MoVA-36B-A4B for use with
llama.cpp.The model tensors are stored in their original BF16 precision. The GGUF files include the tokenizer metadata and a
llama.cpp-compatible chat template.Compatibility: These models require a version of
llama.cppcontaining K2 Horizon architecture support. PR to llama.cpp is in progress. MBZUAI-IFM fork of llama.cpp is in https://github.com/MBZUAI-IFM/llama.cpp/tree/model/K2Horizon
K2-Horizon-MoVA-36B-A4B is the sparse member of the K2-Horizon family: a Mixture-of-Experts model with Mixture-of-Values attention (MoVA) that stores 36B parameters and runs 4B per token. We have released the final checkpoint; intermediate checkpoints, along with the data and the training code, will be released.
[Image blocked: K2-Horizon-MoVA-36B-A4B benchmark results against open MoE, dense, and closed models]
Scores in %. Bold marks the best score in each row. Sections follow the Artificial Analysis Intelligence Index categories. Baseline scores are from Artificial Analysis; Muse Glimmer-30B at high reasoning effort, all other open models in their reasoning mode.
vLLM, recipe at recipes.vllm.ai/IFM:
SGLang recipe validated on 2× H200 in the SGLang K2 Horizon cookbook:
[!Tip] Recommended settings:
reasoning_effort="high",temperature=1.0,top_p=0.95. 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.temperature=1.0, top_p=0.95.xllm_source_router_gemm_partitions override, which preserves the checkpoint's router numerics. Full recipes for every K2-Horizon size, with measured H200 latency and throughput, are in the SGLang cookbook and the vLLM recipe.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.vllm serve IFM/K2-Horizon-MoVA-36B-A4B \ --revision main \ --tensor-parallel-size 2 \ --enable-expert-parallel \ --trust-remote-code \ --dtype bfloat16 \ --max-model-len 131072 \ --reasoning-parser k2_horizon \ --tool-call-parser k2_horizon \ --enable-auto-tool-choicepython3 -m sglang.launch_server \ --model-path IFM/K2-Horizon-MoVA-36B-A4B \ --revision main \ --tp 2 \ --ep 2 \ --dtype bfloat16 \ --attention-backend fa3 \ --json-model-override-args '{"xllm_source_router_gemm_partitions":2}' \ --reasoning-parser k2_horizon \ --tool-call-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-MoVA-36B-A4B", 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-MoVA-36B-A4B"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/},}