KNK-VF-153B Active-14B
KUROTAMA-NO-KAMI VOID FORGED (KNK-VF)
NULLXES frontier Sparse Mixture-of-Experts initialization artifact
| Owner | NULLXES |
| Contact | ceo@nullxes.com |
| Program | NULLXES KNKF |
| Hub | MagistrTheOne/KNK-VF-153B |
| Codename | KNK-VF-153B Active-14B |
| Phase | Init-only — prepared for aggressive datacenter pretrain |
| Pretrained | No |
| License | NULLXES proprietary / research use (license: other) |
Summary
This repository publishes a deterministically initialized, 36-shard bf16 checkpoint for the NULLXES KNKF frontier program. It is the starting weight artifact for large-scale pretraining on NULLXES-owned H200/B300 datacenter clusters — not a finished foundation model.
Warning: Random-init inference produces meaningless text. Do not deploy for end users until pretrain + eval gates complete.
Parameter report
| Metric | Value |
|---|---|
| Total parameters | 153,044,910,080 (~153.0B) |
| Active parameters / token | 14,129,561,600 (~14.1B) |
| Layers | 50 |
| Hidden size | 4,096 |
| Attention heads (GQA) | 32 / 8 KV |
| Routed experts | 128 (top-8 + 1 shared) |
| Context target | 262,144 tokens |
| Vocabulary target | 128,000 (EN / RU / code) |
| Precision | bfloat16 |
| Shards | 36 × ~8 GB safetensors |
Architecture
- Family: NULLXES KNKF / KNK-VF VOID FORGED
- Attention: GQA + hybrid local/global pattern (window 4096, global every 4 layers)
- FFN: SwiGLU dense prefix (4 layers) + sigmoid-routed Sparse MoE (SME-BU load balance)
- Position: RoPE (
theta=1e6) + YaRN scaling - Init policy: Llama-NeoX-style residual (
base_std=0.02,router_std=0.001,global_seed=42) - Config source:
configs/model/knk_vf_153b_active14b.yaml
Purpose (NULLXES)
- Validate MoE sharding, HF publishing, and cluster init pipeline on H200/B300.
- Bootstrap aggressive target-scale pretraining on NULLXES datacenter infrastructure.
- Pair with 128K EN/RU/code tokenizer +
knk_vf_chat_v2bootstrap data before SFT.
Execution policy
KNKF_CLUSTER_EXECUTION=1
KNKF_ACCELERATOR=h200|b300
- Cluster-only execution — no local full-model materialization.
- Do not load all 153B parameters into single-node RAM/VRAM.
- Megatron-Core distributed pretrain is the intended consumer after proxy gates pass.
Training roadmap
| Phase | Goal |
|---|---|
| 0 ✅ | Sharded init checkpoint on Hub (this repo) |
| 1 | Train knk_vf_tokenizer_128k on EN/RU/code corpus |
| 2 | Bootstrap SFT pipeline validation (7B proxy, LLaMA-Factory) |
| 3 | Proxy MoE pretrain on H200 (routing + muP + data gates) |
| 4 | Aggressive 153B→1T pretrain on NULLXES datacenters (Megatron) |
Bundled files
| File | Description |
|---|---|
model-00001..00036.safetensors |
Sharded bf16 weights |
model.safetensors.index.json |
Shard index |
config.json |
Architecture metadata |
init_metadata.json |
Init provenance |
tokenizer_config.json |
Tokenizer spec (no weights) |
special_tokens_map.json |
Special tokens |
chat_template.jinja |
knk_vf_chat_v2 NULLXES identity template |
Not included: tokenizer.model (train separately).
Limitations
- No pretraining or alignment — weights are randomly initialized.
- No production SLA, safety eval, or benchmark scores at this phase.
- Inference stacks (vLLM / Megatron) require NULLXES integration work.
Citation
@misc{nullxes_knkf_153b_init_2026,
title = {KNK-VF-153B Active-14B: NULLXES VOID FORGED Initialization Checkpoint},
author = {NULLXES},
year = {2026},
howpublished = {\url{https://huggingface.co/MagistrTheOne/KNK-VF-153B}},
note = {Initialization-only. Contact: ceo@nullxes.com}
}
Links
- GitHub: NULLXES-KNKF
- Stable 1T target:
configs/model/knk_vf_target_70b_active.yaml - Ultra-frontier 3.5T:
configs/model/knkf_kagutsuchi_tracks_3_5t.yaml - Bootstrap data:
configs/data/bootstrap_identity_tier0.yaml
NULLXES — KUROTAMA-NO-KAMI VOID FORGED. Prepared for aggressive frontier training on proprietary infrastructure.
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