Instructions to use Avifenesh/k2h-s1-final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Avifenesh/k2h-s1-final with Transformers:

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

pipe = pipeline("text-generation", model="Avifenesh/k2h-s1-final", 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-s1-final", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Avifenesh/k2h-s1-final with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Avifenesh/k2h-s1-final"
# 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-s1-final",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Avifenesh/k2h-s1-final

SGLang

How to use Avifenesh/k2h-s1-final 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-s1-final" \
    --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-s1-final",
		"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-s1-final" \
        --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-s1-final",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Avifenesh/k2h-s1-final with Docker Model Runner:
```
docker model run hf.co/Avifenesh/k2h-s1-final
```

K2H S1 final (step 150)

A specialization stage on top of Avifenesh/k2h-r1a-final's run: continued training of IFM/K2-Horizon-MoVA-36B-A4B toward LLM inference and ML systems engineering. It keeps general skills, instruction following and tool calling level with the release, with large code gains. A tool-calling anchor in this stage recovers the BFCL drop that the longer stage-1 run showed at step 1000.

Published 2026-11-08. This checkpoint replaces k2h-r1a-final as the latest model of the project.

Model

Base: IFM/K2-Horizon-MoVA-36B-A4B (36B total, about 4B active, MoE). Same architecture, tokenizer and chat template.
Weights: bf16 safetensors. Load with trust_remote_code=True. Context limit as the release (524,288).
Sampling as the base model card recommends: temperature 1.0, top_p 0.95.

Training

Stage 1 (R1a, see k2h-r1a-final): 1,000 steps from the released weights, about 786M tokens of clean domain data, script-verified replay and a release KL anchor. This stage starts from its step 1000.
Stage 2 (this model): 150 steps at sequence length 65,536, about 786k tokens per step, about 118M tokens.
Trainer: xLLM, FSDP2, bf16, 3 GPUs, optimizer state offloaded to host memory.
AdamW (0.9, 0.95), weight decay 0.06, gradient clip 1.0. Learning rate 5e-6 with 10 warmup steps, then linear decay to 0.1x over the last 50 steps.
Data mix (sampling weights):
- 97%: the stage-1 mix, scaled: clean domain data (Avifenesh/k2h-corpus-domain-clean), script-verified replay (Avifenesh/k2h-replay-verified) and verified chat (Avifenesh/k2h-chat-reasoning-verified) as a release anchor (hinged KL(release || model) on reply tokens, tau 0.05, no cross-entropy), plus the math replay under both cross-entropy and the anchor.
- 2%: grounded outcome chat built from engineering receipts (setup, baseline and change, reasoning from the receipt's own evidence, the measured result), cross-entropy. Not published.
- 1%: a tool-calling anchor, KL only: 8,787 of the release's own replies to tool-use prompts taken from Team-ACE/ToolACE and glaiveai/glaive-function-calling-v2 (both Apache-2.0; prompts and tool lists only, answers dropped), decontaminated against every BFCL V4 file.
- The anchor reads the release's top-64 log-probabilities from a precomputed cache, so no teacher runs during training.

Evaluation

Paired per question against the release, same prompts and sampling. A checkpoint passes when no task in the family below drops significantly after Holm correction, pooled IFEval (two runs) is not more than 2 points down at p < 0.05, chat GSM8K is not more than 1.5 points down, and MMLU is not 1 point or more down at p < 0.05.

Task	Release	Step 150	p
IFEval prompt-strict, reasoning on, 2 runs pooled	83.7	84.9	0.33
GSM8K chat, reasoning on	96.6	96.9	0.48
MATH-500 chat, reasoning on	98.2	98.0	1.0
MMLU	77.3	77.3	0.94
ARC-Challenge	57.6	57.8	0.88
BoolQ	88.4	88.1	0.39
MBPP pass@1	65.2	72.2	< 0.001
HumanEval pass@1	53.1	68.9	< 0.001
BFCL V4 all live categories	76.9	75.7	0.15
BFCL V4 all non-live categories	73.8	72.1	0.15
BFCL V4 multi-turn	55.9	52.3	0.54
BFCL V4 live_multiple	75.0	72.7	0.052
BFCL V4 irrelevance	87.5	85.4	0.27
Held-out PR NLL (lower is better)	1.520	1.269

Long context (RULER, 13 tasks, paired with the release; 24 samples per task at 32k and 64k, 8 at 128k):

Length	Release	Step 150	p
32k	90.4	86.8	0.066
64k	87.2	81.3	0.26
128k	76.3	81.6	0.064

None of the long-context differences is significant, but 32k and 64k point down in both measurements we ran. Treat long-context retrieval at 32k to 64k as possibly a few points weaker than the release.

Few-shot GSM8K (5-shot completion) drops 2.3 points (90.2 to 88.0, p = 0.01). Chat GSM8K and MATH-500 hold, so this reads as drift in the few-shot completion format, not in math.

Domain judgement: on held-out tuning experiments (predict whether a change helps, is neutral, hurts or breaks correctness, scored class-balanced), this model is level with the release. The outcome data in this stage did not improve it.

Compared with k2h-r1a-final (step 800 of stage 1): BFCL about the same, slightly higher IFEval (84.9 vs 84.7), HumanEval (68.9 vs 66.5) and MBPP (72.2 vs 70.6), lower PR NLL (1.269 vs 1.280); RULER level at 32k and 4.7 points lower at 64k (p = 0.02 paired). k2h-r1a-final, measured with the same 24-sample RULER, is 5.1 points below the release at 32k (p = 0.01); this model has no significant long-context drop against the release.

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Safetensors

Model size

37B params

Tensor type

BF16

Model tree for Avifenesh/k2h-s1-final

Base model

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

Finetuned

(14)

this model