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YAML Metadata Warning:The task_categories "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
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:
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])
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