HROM-M1

HROM-M1

HROM-M1 is a transformer-based Mixture-of-Experts (MoE) language model built entirely in PyTorch by me, Timur Hromek, a 15-year-old self-taught developer. It's designed for multi-turn, persona-aware dialogue with a focus on safety, modularity, and extensibility.

This implementation includes top-k expert routing, rotary position embeddings, SwiGLU activations, and a custom tokenizer, along with built-in safety filters and checkpoint management.

Features

• Mixture-of-Experts (MoE) with 8 experts and top-2 routing per token.
• Transformer architecture with 8 layers, 8 heads, and RoPE (rotary positional embeddings).
• SwiGLU activation for efficient MLP computation.
• Multi-dataset training support, including:
  • daily_dialog
  • empathetic_dialogues
  • blended_skill_talk
  • persona-chat
  • papahawk/conversational-01
• Custom tokenizer using Byte-Pair Encoding (BPE).
• SafetyManager for blocking unsafe generations using token-level filtering.
• CheckpointManager with rotating save slots and auto-recovery.
• AMP (mixed precision) and gradient accumulation support.

Model Specs

Architecture Overview

• HROMBlock: Transformer block with attention and MoE feedforward.
• MoELayer: Routes tokens to top-k experts and applies load balancing loss.
• Expert: Lightweight FFN with SwiGLU nonlinearity.
• SafetyManager: Filters generations using predefined token patterns.
• TokenizerTrainer: Builds a BPE tokenizer from dialogue data.
• CheckpointManager: Rotates and auto-recovers checkpoints.

Safety

The model includes a basic content filter that blocks sequences containing unsafe keywords by checking token IDs. Unsafe generations are interrupted before completion.

Installation

git clone https://github.com/yourusername/HROM-M1.git
cd HROM-M1
pip install -r requirements.txt

Training

python HROM-M1.py

The tokenizer will auto-train if not found. Dialogue datasets are pulled via HuggingFace datasets.Dialogue datasets are pulled via HuggingFace datasets.