Instructions to use Avifenesh/k2h-c4-ctrl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Avifenesh/k2h-c4-ctrl with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Avifenesh/k2h-c4-ctrl", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Avifenesh/k2h-c4-ctrl", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Avifenesh/k2h-c4-ctrl with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Avifenesh/k2h-c4-ctrl"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Avifenesh/k2h-c4-ctrl",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Avifenesh/k2h-c4-ctrl

SGLang

How to use Avifenesh/k2h-c4-ctrl with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Avifenesh/k2h-c4-ctrl" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Avifenesh/k2h-c4-ctrl",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Avifenesh/k2h-c4-ctrl" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Avifenesh/k2h-c4-ctrl",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Avifenesh/k2h-c4-ctrl with Docker Model Runner:
```
docker model run hf.co/Avifenesh/k2h-c4-ctrl
```

K2H c4-ctrl

The recipe control arm of a continued-pretraining study on IFM/K2-Horizon-MoVA-36B-A4B: clean domain data plus script-verified math replay.

Research checkpoint, published 2026-11-03. It is not a release candidate. Compare it with the release, not in isolation.

Model

Base: IFM/K2-Horizon-MoVA-36B-A4B (36B total, about 4B active, MoE). Same architecture, tokenizer and chat template.
Weights: bf16 safetensors. The custom modeling code ships with the weights, so load with trust_remote_code=True.
Sampling as the base model card recommends: temperature 1.0, top_p 0.95.

Training

Trainer: xLLM, FSDP2, bf16, 3 GPUs.
Start: the released weights of IFM/K2-Horizon-MoVA-36B-A4B.
100 steps. Sequence length 65,536, batch 1 per GPU, gradient accumulation 4: about 786k tokens per step, about 79M tokens in total.
AdamW, beta1 0.9, beta2 0.95, weight decay 0.06, gradient clip 1.0.
Learning rate 1.5e-5, 20 warmup steps, then constant.
Chat records, where present, are trained on assistant tokens only.

Data mix (sampling weights):

44.5%: domain slices from Avifenesh/k2h-corpus-domain-clean (inference, framework, general and Linux code bundles, commits) plus pull-request text and a small slice of tuning notes that are not published.
47.7%: replay-synth-verified and 7.8%: replay-math-verified from Avifenesh/k2h-replay-verified, as raw text.

Evaluation

All numbers come from the same harness as the release column (lm-evaluation-harness against a vLLM server). IFEval and chat GSM8K use the chat template with reasoning on, temperature 1.0, top_p 0.95, 14,336 new tokens, and score only the text after the reasoning block. The PR NLL is the mean token NLL on 150 held-out pull requests (530k tokens).

Task	Release	This model	Change
GSM8K, 5-shot, strict	90.1	89.3	-0.8
GSM8K, chat zero-shot (reasoning on)	96.6	86.7	-9.9
IFEval, prompt-strict (reasoning on)	84.3	82.6	-1.7
MMLU	77.3	76.9	-0.4
ARC-Challenge	57.6	57.4	-0.2
ARC-Easy	85.6	85.3	-0.3
BoolQ	88.4	88.5	+0.1
HellaSwag (norm)	67.6	68.2	+0.6
PIQA	80.5	80.6	+0.1
WinoGrande	69.1	69.8	+0.7
MBPP pass@1	65.2	71.6	+6.4
HumanEval pass@1 (4 samples)	53.0	65.9	+12.9
Held-out PR NLL (lower is better)	1.519	1.325	-0.194

What the numbers say:

Every general task stays level with the release except chat-format GSM8K, and coding goes up (MBPP +6.4, HumanEval +12.9). The held-out PR NLL drops from 1.519 to 1.325.
IFEval 82.6 vs 84.3 is level (p=0.35, paired).
Chat-format GSM8K falls 10 points: 140 of 1,319 replies end early inside the reasoning block. The cause is the math replay trained as raw text, which teaches the document-end token where a chat turn should end. Few-shot GSM8K hides this.
The same data at LR 5e-6 kept chat GSM8K level (96.3). Use that learning rate, or the chat-format replay, for this data.

License

Apache-2.0, the license of the base model.

Downloads last month: 299

Safetensors

Model size

37B params

Tensor type

BF16

Model tree for Avifenesh/k2h-c4-ctrl

Base model

IFM/K2-Horizon-MoVA-36B-A4B

Finetuned

(12)

this model