初始化项目，由ModelHub XC社区提供模型

Model: cooler8/ZeliDesk-sLM-1B
Source: Original Platform

.gitattributes

@@ -0,0 +1,35 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,154 @@
+---
+language:
+- ko
+license: apache-2.0
+datasets:
+- HuggingFaceFW/fineweb-2
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- llama
+- korean
+- slm
+- from-scratch
+widget:
+- text: "대한민국의 수도는"
+  example_title: "Korean completion"
+- text: "오늘 날씨가 좋아서"
+  example_title: "Casual Korean"
+---
+
+# 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](#license--provenance)).
+
+| | |
+|---|---|
+| Parameters | ~1.040B (embeddings 67M, 6.5% of total) |
+| Hidden size | 2048 |
+| Layers | 16 |
+| Attention heads | 32 (8 KV heads, GQA) |
+| FFN size | 8192 (SwiGLU) |
+| Context length | 2048 |
+| RoPE θ | 500,000 |
+| Normalization | RMSNorm, no bias terms |
+| Embeddings | Tied input/output |
+| Vocabulary | 32,768 |
+
+### 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](https://huggingface.co/datasets/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](https://huggingface.co/datasets/HuggingFaceFW/fineweb-2)) is linked here and the full preprocessing procedure is reproducible from the public notebooks.
+
+## Training procedure
+
+| Hyperparameter | Value |
+|---|---|
+| Total token budget | 20B (~76,293 steps) |
+| Effective batch size | 262,144 tokens/step (micro-batch 8 × grad-accum 16 × ctx 2048) |
+| Learning rate | 4e-4, cosine schedule |
+| Warmup | 2,000 steps |
+| Precision | bf16 mixed precision |
+| Optimizer | AdamW (β₂ = 0.95, weight decay 0.1) |
+| Hardware | Colab Pro+ G4 — RTX PRO 6000 Blackwell (96GB), ~45k tok/s measured |
+
+## 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.
+
+| Checkpoint | Training progress | BPB ↓ | Token perplexity ↓ |
+|---|---|---|---|
+| `ckpt-step17000` | ~22% (~4.5B / 20B tokens) | **0.7742** | 17.35 |
+
+**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
+
+```python
+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:
+
+```python
+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
+
+```bibtex
+@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}
+}
+```

config.json

@@ -0,0 +1,32 @@
+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 0,
+  "dtype": "float32",
+  "eos_token_id": 0,
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 2048,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 16,
+  "num_key_value_heads": 8,
+  "pad_token_id": 1,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 500000.0,
+    "rope_type": "default"
+  },
+  "tie_word_embeddings": true,
+  "transformers_version": "5.13.1",
+  "use_cache": false,
+  "vocab_size": 32768
+}

generation_config.json

@@ -0,0 +1,10 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 0,
+  "eos_token_id": 0,
+  "output_attentions": false,
+  "output_hidden_states": false,
+  "pad_token_id": 1,
+  "transformers_version": "5.13.1",
+  "use_cache": true
+}

last-checkpoint/config.json

@@ -0,0 +1,32 @@
+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 0,
+  "dtype": "float32",
+  "eos_token_id": 0,
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 2048,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 16,
+  "num_key_value_heads": 8,
+  "pad_token_id": 1,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 500000.0,
+    "rope_type": "default"
+  },
+  "tie_word_embeddings": true,
+  "transformers_version": "5.13.1",
+  "use_cache": false,
+  "vocab_size": 32768
+}

last-checkpoint/generation_config.json

@@ -0,0 +1,10 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 0,
+  "eos_token_id": 0,
+  "output_attentions": false,
+  "output_hidden_states": false,
+  "pad_token_id": 1,
+  "transformers_version": "5.13.1",
+  "use_cache": true
+}

last-checkpoint/model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6670b37483d0b5e29b58d245c1746e00c887e871eef5a5a8ed2d9ed4bfd10e18
+size 4161036576

last-checkpoint/optimizer.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:31345cd76893333263772b127f09ab37bf24cd3f03ce84e09357b5270590937d
+size 8322168787

last-checkpoint/rng_state.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:77ca98ed10a20c7ebf9c36ba9d30a165fa497f9abd0ea9c9b3aaf71be5943d05
+size 14645

last-checkpoint/scheduler.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5d09db64044541a35294764f5d948f0482d36f836a748cc1119f3bcfe9d7f22b
+size 1465

last-checkpoint/training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e846a7d292f68b8582d5f303f02a1adce0cf5998bfbdccfe6132a58a5bb0f3a4
+size 5201

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6670b37483d0b5e29b58d245c1746e00c887e871eef5a5a8ed2d9ed4bfd10e18
+size 4161036576

tokenizer_config.json

@@ -0,0 +1,7 @@
+{
+  "backend": "tokenizers",
+  "eos_token": "<|endoftext|>",
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<|pad|>",
+  "tokenizer_class": "TokenizersBackend"
+}

training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e846a7d292f68b8582d5f303f02a1adce0cf5998bfbdccfe6132a58a5bb0f3a4
+size 5201