Duplicate from mkd-ai-dev/keural-tokenizer

Co-authored-by: Md Najmul Hossain <mkd-hossain@users.noreply.huggingface.co>

.gitattributes

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LICENSE

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README.md

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+# Keural Tokenizer
+
+**Keural Tokenizer** is the official tokenizer used for training the **Keural Foundation Model**, a large-scale Mixture-of-Experts (MoE) language model architecture designed for enterprise AI, long-context reasoning, and multilingual language understanding.
+
+This repository provides the tokenizer used during the **pretraining stage of the Keural model**, including configuration files, vocabulary, and metadata required to reproduce tokenization behavior during training and inference.
+
+---
+
+# Overview
+
+Large Language Models rely heavily on efficient tokenization.
+The Keural tokenizer was designed with the following goals:
+
+* Efficient token representation for large-scale training
+* Balanced multilingual support
+* Compatibility with scientific, web, and code corpora
+* High vocabulary capacity for long-context modeling
+* Robust normalization and byte fallback support
+
+The tokenizer was trained using the **SentencePiece Unigram model** on a curated multilingual corpus.
+
+---
+
+# Tokenizer Specifications
+
+| Property        | Value                 |
+| --------------- | --------------------- |
+| Tokenizer Type  | SentencePiece Unigram |
+| Vocabulary Size | 131072 tokens         |
+| Normalization   | NFKC                  |
+| Byte Fallback   | Enabled               |
+| Digit Splitting | Enabled               |
+| Unknown Token   | `<unk>`               |
+| Padding Token   | `<pad>`               |
+| BOS Token       | `<bos>`               |
+| EOS Token       | `<eos>`               |
+
+The tokenizer supports multilingual text including:
+
+* English
+* Korean
+* Scientific documents
+* Literature
+* Programming languages
+* Web-scale data
+
+---
+
+# Training Corpus
+
+The tokenizer was trained on a **54.77 GB multilingual corpus** consisting of multiple domains to ensure robust token coverage.
+
+### Domain Distribution
+
+| Domain              | Description                    |
+| ------------------- | ------------------------------ |
+| Web Text            | Large-scale English web corpus |
+| Scientific Papers   | ArXiv and PubMed datasets      |
+| Literature          | PG19 and BookCorpus            |
+| Wikipedia           | Clean Korean Wikipedia         |
+| Source Code         | Large-scale code repositories  |
+| Korean Web Data     | Korean web text corpora        |
+| Multilingual Corpus | CC100 Korean                   |
+
+The dataset pipeline was designed to reduce noise while preserving linguistic diversity across domains.
+
+---
+
+# Tokenizer Files
+
+This repository contains the following tokenizer artifacts:
+
+```text
+keural_tokenizer.model
+keural_tokenizer.vocab
+tokenizer_config.json
+tokenizer_metadata.json
+tokenizer.sha256
+```
+
+### File Description
+
+**keural_tokenizer.model**
+Binary SentencePiece tokenizer model used for tokenization.
+
+**keural_tokenizer.vocab**
+Vocabulary mapping tokens to IDs.
+
+**tokenizer_config.json**
+Tokenizer configuration used during model training.
+
+**tokenizer_metadata.json**
+Metadata including training corpus information.
+
+**tokenizer.sha256**
+Checksum file for verifying tokenizer integrity.
+
+---
+
+# Example Usage
+
+### Using SentencePiece
+
+```python
+import sentencepiece as spm
+
+sp = spm.SentencePieceProcessor()
+sp.load("keural_tokenizer.model")
+
+tokens = sp.encode("Keural is a foundation model.", out_type=int)
+print(tokens)
+```
+
+### Using HuggingFace Transformers
+
+```python
+from transformers import AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("mkd-ai/keural-tokenizer")
+
+tokens = tokenizer("Keural foundation model tokenizer example")
+print(tokens)
+```
+
+---
+
+# Model Compatibility
+
+This tokenizer is used for training the **Keural Foundation Model**, which uses the following architecture:
+
+| Parameter                | Value                          |
+| ------------------------ | ------------------------------ |
+| Architecture             | Transformer Mixture-of-Experts |
+| Hidden Size              | 4096                           |
+| Layers                   | 32                             |
+| Attention Heads          | 32                             |
+| Experts per Layer        | 32                             |
+| Active Experts per Token | 4                              |
+| Context Length           | 4096 (scalable)                |
+| Vocabulary Size          | 131072                         |
+
+Estimated model capacity:
+
+* Total parameters: ~120B
+* Active parameters per token: ~13B
+
+---
+
+# Context Length Roadmap
+
+The Keural model is designed to scale context length progressively using **YaRN positional scaling**.
+
+| Stage   | Context Length |
+| ------- | -------------- |
+| Stage 1 | 4096           |
+| Stage 2 | 8192           |
+| Stage 3 | 32768          |
+| Stage 4 | 131072         |
+| Stage 5 | 262144         |
+| Stage 6 | 524288         |
+| Stage 7 | 1,048,576      |
+
+This staged context expansion enables efficient training while supporting ultra-long context inference.
+
+---
+
+# Training Pipeline
+
+The tokenizer was trained as part of the Keural dataset pipeline, which includes:
+
+* Streaming dataset ingestion
+* Text normalization and cleaning
+* Multithreaded tokenization
+* Domain-based token balancing
+* Fault-tolerant dataset checkpointing
+* Large-scale corpus collection
+
+The dataset preparation pipeline is available in the Keural model repository.
+
+---
+
+# Roadmap
+
+The Keural project roadmap includes the following stages.
+
+### Stage 1 — Tokenizer Development
+
+* Multilingual tokenizer training
+* Vocabulary optimization
+* Token coverage validation
+
+### Stage 2 — Dataset Preparation
+
+* Large-scale corpus collection
+* Domain balancing
+* Token budget enforcement
+
+### Stage 3 — Foundation Model Training
+
+* Mixture-of-Experts transformer architecture
+* Long-context support
+* Distributed GPU training
+
+### Stage 4 — Instruction Tuning
+
+* Alignment with instruction datasets
+* conversational fine-tuning
+* domain adaptation
+
+### Stage 5 — Deployment
+
+* vLLM inference support
+* enterprise deployment
+* retrieval-augmented reasoning
+
+---
+
+# Hardware Environment
+
+Tokenizer development and dataset processing were performed on a high-performance server environment:
+
+* CPU: 32 cores
+* RAM: ~480 GB
+* Storage: NVMe SSD
+* GPU: 2 H200 class GPUs used during model training (not yet)
+
+---
+
+# License
+
+This tokenizer is part of the **Keural Foundation Model project**.
+
+[![License](https://img.shields.io/github/license/MKD-CORP/Keural-MoE-14B-Training)](https://github.com/MKD-CORP/Keural-MoE-14B-Training/blob/main/LICENSE)
+
+---
+
+# Organization
+
+Developed by
+
+**MKD Corp AI Research**
+
+Republic of Korea
+
+---
+
+# Source Code
+
+https://github.com/MKD-CORP/keural-tokenizer-model
+
+# Citation
+
+If you use the Keural tokenizer in research, please cite the Keural project repository.
+
+```bibtex
+@misc{keural_tokenizer,
+  title={Keural Tokenizer},
+  author={MKD Corp AI Research, Md. Najmul Hossain},
+  year={2026},
+  url={https://huggingface.co/mkd-ai/keural-tokenizer}
+}
+```

keural_tokenizer.model

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tokenizer.sha256

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tokenizer_config.json

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+{
+  "architectures": [
+    "KeuralMoEForCausalLM"
+  ],
+  "model_type": "keural_moe",
+  "vocab_size": 131072,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 0,
+  "unk_token_id": 3,
+  "tokenizer_class": "SentencePieceTokenizer",
+  "sentencepiece_model_file": "keural_tokenizer.model",
+  "special_tokens_map": {
+    "pad_token": "<pad>",
+    "bos_token": "<bos>",
+    "eos_token": "<eos>",
+    "unk_token": "<unk>"
+  },
+  "normalization": "nfkc",
+  "split_digits": true,
+  "byte_fallback": true,
+  "max_context_target": 1048576,
+  "context_stages": [
+    4096,
+    8192,
+    32768,
+    131072,
+    262144,
+    524288,
+    1048576
+  ]
+}

tokenizer_metadata.json

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+{
+  "model_name": "Keural-13B Tokenizer",
+  "status": "LOCKED - DO NOT MODIFY AFTER PRETRAINING",
+  "created_at": "2026-03-11T11:55:40.906220",
+  "corpus": {
+    "file": "/home/work/keural-model/keural-model/data/raw/tokenizer_corpus_clean.txt",
+    "size_gb": 26.74,
+    "total_lines": 4345100,
+    "total_chars": 25348127381,
+    "estimated_tokens": 6337031845,
+    "short_lines_lt_80": 0,
+    "long_lines_gt_max_sentence_length": 422538
+  },
+  "tokenizer": {
+    "model_type": "unigram",
+    "vocab_size": 131072,
+    "character_coverage": 0.9995,
+    "byte_fallback": true,
+    "split_digits": true,
+    "split_by_unicode_script": true,
+    "normalization_rule_name": "nfkc",
+    "max_sentence_length": 10000,
+    "num_threads": 32,
+    "train_extremely_large_corpus": true,
+    "shuffle_input_sentence": true
+  },
+  "special_tokens": {
+    "pad": {
+      "id": 0,
+      "piece": "<pad>"
+    },
+    "bos": {
+      "id": 1,
+      "piece": "<bos>"
+    },
+    "eos": {
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+      "piece": "<eos>"
+    },
+    "unk": {
+      "id": 3,
+      "piece": "<unk>"
+    }
+  },
+  "training": {
+    "elapsed_minutes": 67.4,
+    "train_log_file": "/home/work/keural-model/keural-model/data/logs/tokenizer_train_20260311_104736.log",
+    "resource_log_file": "/home/work/keural-model/keural-model/data/logs/tokenizer_resources_20260311_104736.jsonl"
+  },
+  "validation": {
+    "english_chars_per_token": 4.15,
+    "validation_cases": {
+      "korean": {
+        "input": "안녕하세요 저는 큐럴 토크나이저를 테스트합니다.",
+        "num_tokens": 10,
+        "pieces_preview": [
+          "안녕하세요",
+          "▁저는",
+          "▁큐",
+          "럴",
+          "▁토크",
+          "나이",
+          "저를",
+          "▁테스트",
+          "합니다",
+          "."
+        ],
+        "roundtrip_ok": true
+      },
+      "english": {
+        "input": "Hello, this is a tokenizer validation test for Keural.",
+        "num_tokens": 13,
+        "pieces_preview": [
+          "Hello",
+          ",",
+          "▁this",
+          "▁is",
+          "▁a",
+          "▁token",
+          "izer",
+          "▁validation",
+          "▁test",
+          "▁for",
+          "▁Ke",
+          "ural",
+          "."
+        ],
+        "roundtrip_ok": true
+      },
+      "code": {
+        "input": "def hello_world():\n    return 42",
+        "num_tokens": 10,
+        "pieces_preview": [
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+          "4",
+          "2"
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+        "roundtrip_ok": false
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+      "mixed": {
+        "input": "안녕하세요 world 123 def test(): return True",
+        "num_tokens": 11,
+        "pieces_preview": [
+          "안녕하세요",
+          "▁world",
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+          "3",
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+          "▁test",
+          "():",
+          "▁return",
+          "▁True"
+        ],
+        "roundtrip_ok": true
+      },
+      "rare_char": {
+        "input": "𠜎",
+        "num_tokens": 4,
+        "pieces_preview": [
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+  "immutability": {
+    "sha256_model": "b982818ea2f2057ba791e2006d17683799f1d8ceb9c91322018a638c4ec4b170",
+    "model_file": "/home/work/keural-model/keural-model/tokenizer/keural_tokenizer.model",
+    "vocab_file": "/home/work/keural-model/keural-model/tokenizer/keural_tokenizer.vocab"
+  }
+}