K2-Horizon-3.7B-GGUF

This repository contains GGUF versions of the IFM/K2-Horizon-3.7B 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.cpp containing 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-3.7B is the small dense member of the K2-Horizon family: a 3.7B-core decoder-only model with a 512K context window.

K2-Horizon-3.7B Highlights

• Strong small-model baseline. A dense model evaluated on the same agentic, coding, and reasoning benchmarks as the rest of the family.
• 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-3.7B against selected reference models. The table below lists every comparison model used in the figure.

Full Results

	Open-weight dense models
	K2-Horizon-3.7B	Qwen3.5-4B	G9v3-3B	Granite 4.2-3B	Nemotron 3 Nano-4B
# Params	3.7B	4B	3B	3B	4B
# Activated params	3.7B	4B	3B	3B	4B
Architecture	Dense	Dense	Dense	Dense	Dense
Math
HMMT Feb 2026 Competition mathematics	70.5	61.6	34.1	57.2	34.7
Coding
SWE-bench Verified Software engineering	68.6	41.2	16.4	32.2	1.8
Scientific Reasoning
GPQA Diamond Graduate-level science QA	65.4	77.1	43.8	55.9	51.3
HLE Expert-level reasoning	12.9	9.9	4.5	6.6	4.9
Coding
SciCode Scientific coding	25.9	16.1	17.7	24.9	16.4
Terminal-Bench 2.1 Agentic terminal use	25.1	25.8	6.0	13.9	3.7
Agents
tau3-Banking Agentic tool use	17.7	6.8	—	5.6	—
BFCL v4 Function calling	50.9	55.7	47.9	50.8	36.8

Scores in %. Bold marks the best score in each row. Baseline protocols may differ;

Quickstart

Serving

vLLM, recipe at recipes.vllm.ai/IFM:

vllm serve IFM/K2-Horizon-3.7B \
  --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:

sglang serve \
  --model-path IFM/K2-Horizon-3.7B \
  --revision c177771836a4c460743c00002c22483f6f18d1eb \
  --tp 1 \
  --dtype bfloat16 \
  --attention-backend fa3 \
  --reasoning-parser k2_horizon \
  --host 0.0.0.0 \
  --port 30000

API Usage

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.

from openai import OpenAI

client = OpenAI(base_url="http://localhost:30000/v1", api_key="EMPTY")
response = client.chat.completions.create(
    model="IFM/K2-Horizon-3.7B",
    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.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "IFM/K2-Horizon-3.7B"
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.
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

@misc{k2horizon2026,
  title  = {Introducing K2 Horizon: Frontier Performance, Radically Open},
  author = {{IFM Team}},
  year   = {2026},
  url    = {https://ifm.ai/blog/k2/},
}