Update README.md

README.md

@@ -1,3 +1,71 @@
 ---
 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).
+
+---
+
+## 📊 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|>`, `<|eos|>`, 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])