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 checkpoints, along with the data and the training code.
Frontier-class results at 4B active parameters. On agentic and reasoning benchmarks it outscores open weight dense (approximately 30B model size) and MoE models up to 15× its size; and also performs competitively against closed frontier models (see Benchmark Results).
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/recipe and the training code are public.
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.
Use an exact branch name from the inventory with vLLM's --revision option. For example, --revision pretrain_1100000 selects the final checkpoint of Pretraining, at step 1,100,000.
Tip. Recommended settings: reasoning_effort="high", temperature=1.0, top_p=0.95. 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-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].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.
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. For example, the 50B tokens listed for SFT Phase 2 are additional to the 219B tokens in Phase 1, bringing the cumulative budget to 269B tokens by the end of Phase 2. Here, B and T denote billion and trillion tokens, respectively.
Each stage or phase continues from the final checkpoint of the preceding stage or phase.
Some stages, such as SFT, have multiple phases with slight changes to the data mix while retaining the same overall purpose.
Training stage
Training steps
Training tokens
Sequence length
Purpose
Pretraining
1100000
22.9T
8K
Pretraining.
Midtraining — Stage 1
55000
1.1T
32K
Context extension.
Midtraining — Stage 2
25000
498B
128K
Context extension.
Midtraining — Stage 3
5500
110B
512K
Context extension.
Midtraining — Stage 4
10000
199B
512K
Continued context extension from Stage 3, with the data mix shifted toward agentic and reasoning SFT data.
SFT — Phase 1
11000
219B
512K
SFT for better domain coverage, starting from the final checkpoint of midtraining stage 4.
SFT — Phase 2
2500
50B
512K
SFT on a high-quality subset of the data used in Phase 1, with learning rate decay.
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, sft_1_11000 is the checkpoint saved at training step 11,000 within SFT Phase 1, and is the final checkpoint of that phase. The numeric suffix is the step within the named stage or phase, not the cumulative step across all training. Thus, sft_2_2500 refers to step 2,500 within SFT Phase 2.
For a partially released group, the available checkpoints and the remaining checkpoints are listed in the notes.
Checkpoint
Branch / repository
Status
Remaining items / expected availability
Pretrain Intermediate Checkpoints
pretrain_*
Available
N/A
Pretrain Final Checkpoint
pretrain_1100000
Available
N/A
Midtrain Stage 1 Intermediate Checkpoints
mid_1_*
Available
N/A
Midtrain Stage 1 Final Checkpoint
mid_1_55000
Available
N/A
Midtrain Stage 2 Intermediate Checkpoints
mid_2_*
Available
N/A
Midtrain Stage 2 Final Checkpoint
mid_2_25000
Available
N/A
Midtrain Stage 3 Intermediate Checkpoints
mid_3_*
Available
N/A
Midtrain Stage 3 Final Checkpoint
mid_3_5500
Available
N/A
Midtrain Stage 4 Intermediate Checkpoints
mid_4_*
Available
N/A
Midtrain Stage 4 Final Checkpoint
mid_4_10000
Available
N/A
SFT Phase 1 Intermediate Checkpoints
sft_1_*
Available
N/A
SFT Phase 1 Final Checkpoint
sft_1_11000
Available
N/A
SFT Phase 2 Intermediate Checkpoints
sft_2_*
Available
N/A
SFT Phase 2 Final Checkpoint
sft_2_2500
Available
N/A
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.
Reasoning effort: always high. All reported results use high reasoning effort. Pass {"chat_template_kwargs": {"reasoning_effort": "high"}} on every request. medium and low effort settings are not recommended except for research on reasoning efforts.
Sampling parameters.temperature=1.0, top_p=0.95.
Serving. Use the validated SGLang recipe above: BF16, TP=2, FlashAttention-3, and the 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.
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.
import os
os.environ["HF_ENDPOINT"] = "https://freeweights.allternit.com"
from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("IFM/K2-Horizon-MoVA-36B-A4B", revision="cf253b232c10a938cd587c50035ccfb50b7864f3")
model = AutoModelForCausalLM.from_pretrained("IFM/K2-Horizon-MoVA-36B-A4B", revision="cf253b232c10a938cd587c50035ccfb50b7864f3", device_map="auto")
