MAP-Neo

Provides a complete, reproducible training pipeline from raw data ingestion through model checkpointing, enabling researchers to train bilingual language models from scratch with full visibility into data processing, tokenization, and training dynamics. The pipeline includes data collection, cleaning, tokenization, and distributed training orchestration with intermediate checkpoint preservation at configurable intervals.

Implements a data pipeline that collects, deduplicates, and preprocesses text from multiple sources in two languages, applying language detection, quality filtering, and normalization to create a balanced bilingual training corpus. The pipeline handles encoding issues, removes low-quality content, and maintains language-pair alignment for effective bilingual training.

Evaluates bilingual models on language-specific benchmarks and multilingual tasks, measuring performance across both languages and analyzing language-specific strengths and weaknesses. The evaluation framework supports custom benchmarks and provides detailed analysis of cross-lingual transfer and language interference.

Implements a tokenization layer that builds byte-pair encoding (BPE) vocabularies from training data, with configurable vocabulary size and language-specific token allocation. The tokenizer is optimized for bilingual efficiency, balancing vocabulary coverage across both languages to minimize token overhead while maintaining compression ratios.

Orchestrates distributed training across multiple GPUs/TPUs using PyTorch's Fully Sharded Data Parallel (FSDP) or DeepSpeed, with automatic gradient accumulation, mixed-precision training, and periodic checkpoint saving. The system manages training state, optimizer states, and model weights across distributed workers, enabling resumption from checkpoints and fault tolerance.

Evaluates model performance at intermediate training checkpoints using standard NLP benchmarks (perplexity, downstream task accuracy), enabling researchers to analyze training dynamics and identify optimal stopping points. The evaluation framework supports multiple benchmark suites and logs metrics for comparison across checkpoints.

Manages training configurations through YAML/JSON files with full hyperparameter tracking, enabling reproducible training runs and systematic hyperparameter exploration. The system logs all configuration decisions, random seeds, and environment details to ensure complete reproducibility and facilitate ablation studies.

Implements a configurable transformer architecture supporting variable model sizes (from 1B to 70B+ parameters) with standard components (attention, MLP, layer normalization), enabling researchers to experiment with different architectural choices while maintaining reproducibility. The architecture supports both dense and sparse attention patterns, rotary positional embeddings, and configurable activation functions.

Logs comprehensive training metrics (loss, perplexity, throughput, GPU utilization, gradient norms) at configurable intervals and provides visualization tools for analyzing training dynamics. The system supports multiple logging backends (TensorBoard, Weights & Biases, local files) and generates plots for loss curves, learning rate schedules, and hardware utilization.

Implements deterministic training through careful random seed management across PyTorch, NumPy, and Python's random module, with explicit documentation of non-deterministic operations. The system ensures that training runs with identical configurations produce identical results, enabling perfect reproducibility for research and debugging.

Implements inference and text generation with multiple decoding strategies (greedy, beam search, nucleus sampling, temperature scaling), supporting both batch and streaming inference modes. The system includes optimizations for inference efficiency (KV-cache, attention optimization) and supports quantization for reduced memory footprint.