ZeliDesk-sLM-1B

ZeliDesk-sLM-1B

⚠️ Work in progress — pretraining at ~22% (4.5B / 20B tokens). Weights are intermediate and will be updated. This repository is updated continuously as training progresses. Intermediate checkpoints are pinned with git tags (e.g. ckpt-step17000) so any published state remains reproducible.

ZeliDesk-sLM-1B is a 1.04B-parameter Korean language model pretrained entirely from scratch — random initialization, a custom-trained tokenizer, and a fully documented, reproducible training pipeline. It is not a fine-tune, continuation, or distillation of any existing model.

The goal of this project is not leaderboard performance. It is an honest, fully backfillable Korean 1B baseline: every stage — tokenizer training, data preprocessing, and pretraining — is reproducible from public notebooks, and the model card reports only verifiable numbers. No benchmark scores are claimed until they are actually measured.

Architecture

Standard Llama-style decoder-only transformer (LlamaForCausalLM). Only the publicly documented architecture recipe (RMSNorm + SwiGLU + RoPE + GQA) is adopted — no weights, tokenizer, or other artifacts are inherited from Meta Llama or any other model (see License & provenance).

Tokenizer

Custom ByteLevel BPE with a 32,768-token vocabulary, trained from scratch on a sample of FineWeb-2 kor_Hang. Special tokens: <|endoftext|> (eos, id 0), <|pad|> (pad, id 1). This is a base model — no chat template is defined.

Measured fertility on held-out Korean web text (FineWeb-2 kor_Hang test split, 150 docs): 5.318 UTF-8 bytes/token ≈ 2.310 chars/token.

Training data

Pretrained on HuggingFaceFW/fineweb-2, kor_Hang split (license: ODC-By 1.0), with a Chinchilla-style budget of 20B tokens.

The tokenized data shards used for training are kept in a private repository because they are a re-processed derivative of the source dataset; instead, the original dataset (HuggingFaceFW/fineweb-2) is linked here and the full preprocessing procedure is reproducible from the public notebooks.

Training procedure

Intermediate evaluation

Every number below was actually measured on the published checkpoint — nothing is estimated or extrapolated. BPB (bits per byte) is the project's primary pretraining metric; it will be re-measured at later checkpoints and at completion, forming a public training curve.

Methodology (deterministic, reproducible): first 150 documents of FineWeb-2 data/kor_Hang/test/000_00000.parquet (held-out test split, disjoint from training data), in file order. Each document is prefixed with <|endoftext|> and truncated to a 2,048-token window; cross-entropy is summed over all document tokens and divided by the UTF-8 byte length of the exact decoded scored text: BPB = NLL_nats / ln(2) / bytes. Totals: 87,539 tokens / 465,560 bytes scored, float32 inference.

No standard benchmark scores (e.g. reasoning or knowledge suites) are published yet — the absence of those numbers is deliberate, not an oversight.

Sample outputs (step 17000)

Unedited samples from the current checkpoint (do_sample=True, top_p=0.9, temperature=0.8, seed 42), shown to set expectations honestly — grammar and fluency are forming, factual grounding is not yet there:

대한민국의 수도는 어디입니까? - "전라북도 전주시 완산구, 전주읍, 진안군 진안읍, …" (fails to answer; drifts into place-name lists)

오늘 날씨가 좋아서 꽃도 활짝 폈다. 봄이 오면 들판에서 새싹이 움트듯, 꽃도 활짝 피겠지. 이곳, 이 길을 걷는 우리들도 이제 꽃처럼 활짝 피어나게 될 테니.

인공지능 기술의 발전은 향후 더욱 가속화 될 것으로 예측합니다. - "Software Development" 저널에 실린 … (fluent register, but fabricated citation)

License & provenance

Weights are released under Apache-2.0.

This model does not inherit any weights, tokenizer, or training artifacts from Meta Llama. It adopts only the openly published architecture recipe (RMSNorm, SwiGLU, RoPE, GQA) as implemented by the standard LlamaForCausalLM class in 🤗 Transformers, and was pretrained from random initialization. The Meta Llama Community License therefore does not apply to these weights.

Training data attribution: FineWeb-2 is released by HuggingFaceFW under ODC-By 1.0.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "cooler8/ZeliDesk-sLM-1B"
tokenizer = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype="bfloat16")

inputs = tokenizer("대한민국의 수도는", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=64, do_sample=True, top_p=0.9)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

To load the pinned intermediate checkpoint instead of the moving main branch:

model = AutoModelForCausalLM.from_pretrained(repo, revision="ckpt-step17000")

Limitations

Read this section before using the model. It is intentionally thorough.

• Training is incomplete. The current weights represent ~22% of the planned 20B-token budget (~4.5B tokens). Output quality is far below what the finished model is expected to produce, and the weights on main will keep changing until training completes.
• Base model only. No instruction tuning, no RLHF, no safety alignment. It does not follow instructions or chat — it only continues text. Outputs may be incoherent, repetitive, factually wrong, or offensive, reflecting patterns in web-crawled data.
• Korean only. Trained exclusively on Korean (kor_Hang) text. English or other languages will produce degraded output.
• 1B-class capacity. This is a small language model. It cannot be expected to match larger models on knowledge, reasoning, or generation quality — that is not its purpose.
• Short context. Maximum context length is 2,048 tokens.
• No standard benchmark results yet. Only the intermediate BPB measurement above has been published, with its full methodology. Any other capability numbers attributed to this model are not from us.
• Web-data risks. FineWeb-2 is filtered web text; despite filtering it may contain biases, inaccuracies, and personally identifiable information, which the model may reproduce.
• Not for production or high-stakes use in its current state — including medical, legal, or financial advice and any automated decision-making affecting people.

한국어 요약

ZeliDesk-sLM-1B는 처음부터(from scratch) 사전학습한 한국어 1.04B 파라미터 언어 모델입니다. Meta Llama의 가중치·토크나이저를 일절 상속하지 않고, 공개 표준 아키텍처 규격(RMSNorm + SwiGLU + RoPE + GQA)만 채택하여 랜덤 초기화에서 학습했습니다. 따라서 가중치는 Apache-2.0으로 공개됩니다.

• 현재 상태: 학습 진행 중 (~22%, 4.5B / 20B 토큰). 미완성 중간 가중치입니다.
• 학습 데이터: FineWeb-2 한국어(kor_Hang) 스플릿 (ODC-By 1.0), 20B 토큰 예산
• 토크나이저: FineWeb-2 한국어 샘플로 자체 학습한 ByteLevel BPE 32,768 어휘
• 목표: 성능 경쟁이 아닌, 전 과정이 재현 가능한 정직한 한국어 1B 베이스라인
• 중간 평가 (step 17000, 실측): FineWeb-2 한국어 held-out 테스트 스플릿 기준 BPB 0.7742, 토큰 perplexity 17.35. 측정 방법론은 위 Intermediate evaluation 섹션에 전부 공개.
• 베이스 모델이므로 지시 따르기·대화 능력이 없으며, 표준 벤치마크 점수는 아직 측정 전입니다. 검증된 숫자만 기재하는 것이 이 프로젝트의 원칙입니다.

Citation

@misc{zelidesk-slm-1b,
  title  = {ZeliDesk-sLM-1B: A from-scratch Korean 1B language model},
  author = {ZeliAI},
  year   = {2026},
  url    = {https://huggingface.co/cooler8/ZeliDesk-sLM-1B}
}