🦀 Decoder-only LLM built from scratch in pure Rust using Candle — no Python, no PyTorch. Gated DeltaNet + sparse attention, fine-grained MoE, native video/document understanding, long-horizon tool agents, quantization-aware training. Scales: Tiny (25M) to Large (1.3B).

[ICCV 2025] DONUT: A Decoder-Only Model for Trajectory Prediction

Efficient encoder-decoder architecture for small language models (≤1B parameters) with cross-architecture knowledge distillation and vision-language capabilities

使用Decoder-only的Transformer进行时序预测，包含SwiGLU和RoPE(Rotary Positional Embedding)，Time series prediction using Decoder-only Transformer, Including SwiGLU and RoPE(Rotary Positional Embedding)

Code for paper "Modality Plug-and-Play: Elastic Modality Adaptation in Multimodal LLMs for Embodied AI"

[ACL 2025] 🔍 Multilingual Evaluation of English-Centric LLMs via Cross-Lingual Alignment

SAMPO: Scale-wise Autoregression with Motion Prompt for Generative World Models

Minimal decoder-only seq2seq pipeline with proper causal masking, teacher forcing, Ignite training loop, and checkpointed inference

ViAG: A Novel Framework for Fine-tuning Answer Generation models ultilizing Encoder-Decoder and Decoder-only Transformers's architecture

Implementation of the GPT-2 architecture using PyTorch, trained on the TinyStories dataset. Features custom training pipelines on Modal (cloud computing) and integration with the Hugging Face ecosystem.

Clean-room GPT-2/GPT-3 implementation: tokenizers, architecture blocks, training loop with AdamW + cosine decay, CLI scripts, inference tools, and pytest suite. Covers OpenWebText-10k & WikiText-103 workflows. Designed as an academic reference for understanding and scaling decoder-only transformers

A from-scratch implementation of a scaled-down GPT-2 model in PyTorch, trained on the Snappfood dataset for sentiment-controlled Persian text generation.

Developed and pre-trained a 20.39M-parameter Punjabi GPT-style base model from scratch, including corpus preparation, tokenizer training, benchmark evaluation, and text generation, using a cleaned Punjabi corpus and local Apple Silicon GPU acceleration.

Open source implementation of AMALIA: A Fully Open Large Language Model for European Portuguese

Decoder OID Certificado

A decoder-only transformer specializing in long-horizon reasoning, software architecture, and autonomous software engineering.

Implementing GPT2 from scratch for learning purposes.

Train a decoder-only GPT language model from scratch for code and math reasoning — custom 16k BPE tokenizer, streaming data pipeline, ablation studies, and hardware-aware training on Apple Silicon (MPS) and CUDA.

This project is my PyTorch reproduction of PaliGemma, a compact 3B vision–language model that integrates SigLIP vision features with a Gemma decoder. I implemented the full multimodal pipeline from vision encoding to autoregressive text generation to study modern VLM architectures from a research perspective.