| --- |
| license: apache-2.0 |
| language: |
| - en |
| - ru |
| - code |
| task_categories: |
| - text-generation |
| - text2text-generation |
| - question-answering |
| size_categories: |
| - 100B<n<1T |
| tags: |
| - multimodal |
| - code |
| - math |
| - pre-training |
| - instruction-tuning |
| --- |
| |
| # Dataset Card for LLM_multimodal |
| |
| **LLM_multimodal** is the official training corpus designed for the **LLM D6** model series. It contains a massive, high-quality collection of **300 Billion tokens**, carefully curated to balance linguistic diversity, mathematical reasoning, and programming capabilities. |
| |
| This repository hosts both the raw/processed pre-training data and the instruction-following datasets used for supervised fine-tuning (SFT). |
| |
| # tokenizer is located at LLM_D6 in my models! |
| --- |
|
|
|
|
| ## 📊 Dataset Summary |
|
|
| * **Total Token Count:** ~300 Billion tokens |
| * **Languages:** English (`en`), Russian (`ru`), and various Programming Languages. |
| * **Domains:** General Web Text, Academic/Mathematical Papers, Code Repositories, and Conversational data. |
| * **Intended Model:** LLaMA-3 architectures (specifically the D6 0.8B parameter custom build). |
| * **Tokenizer Compatibility:** This dataset was used to train a custom tokenizer with a **52,000 vocabulary size**, optimized for English, Cyrillic (Russian) scripts, and programming syntax. |
|
|
| --- |
|
|
| ## 📂 Dataset Structure |
|
|
| The dataset is divided into two primary subsets to support the full lifecycle of Large Language Model training: |
|
|
| ### 1. `pre-train` (Unsupervised Corpus) |
| This subset contains the bulk of the 300B tokens used for the foundational training phase. It is structured as large text documents (typically stored in `.parquet` or `.jsonl` formats) with a focus on high-quality, deduplicated data. |
|
|
| * **English & Russian Text:** Filtered web crawls, encyclopedias, and literature to ensure strong bilingual fluency. |
| * **Code:** Source code from permissive repositories covering Python, C++, JavaScript, and other major languages. |
| * **Math:** LaTeX-formatted equations, mathematical proofs, and STEM-focused academic texts to enhance reasoning. |
|
|
| ### 2. `fine-tune` (Instruction/SFT Corpus) |
| A curated subset of high-quality prompt-completion pairs formatted for dialogue and instruction following. |
| * **ChatML Format Ready:** The data structure natively aligns with standard ChatML prompt templates (`<|bos|>`, `<|end_of_text|>`, role definitions). |
| * **Tasks:** Includes physical commonsense reasoning (PIQA), general reasoning (HellaSwag), multi-task accuracy (MMLU), and Russian language understanding (XWinograd RU). |
|
|
| --- |
|
|
| ## 🚀 Usage |
|
|
| You can easily load this dataset using the `datasets` library from Hugging Face: |
|
|
| ```python |
| from datasets import load_dataset |
| |
| # Load the fine-tuning dataset |
| sft_dataset = load_dataset("firdavsus/LLM_multimodal", split="fine_tune") |
| |
| # Load the pre-training dataset (Requires streaming due to size) |
| pretrain_dataset = load_dataset("firdavsus/LLM_multimodal", split="pre_train", streaming=True) |
| |
| # Example output from the SFT dataset |
| print(sft_dataset[0]) |
