thehardwarestore.ai

This repository provides the official PyTorch implementation of TaDiCodec, a text-aware diffusion speech tokenizer accepted at NeurIPS 2025. The core contribution is a novel speech codec that achieves ultra-low bitrate (0.0875 kbps at 6.25 Hz) by combining a non-autoregressive Llama encoder, binary spherical quantization (BSQ), and a flow-matching diffusion decoder conditioned on text and prompt speech. The repo also includes token-based zero-shot TTS models: an autoregressive (AR) model using LLMs (Qwen2.5, Phi-3.5) and a masked diffusion (MGM) model. Inference pipelines with automatic model downloading from Hugging Face are provided. Training scripts for the tokenizer are partially implemented (dataloader, trainer class), but TTS model training scripts are incomplete. The project has migrated to a new repository (AmphionTeam/Diffusion-Speech-Tokenizer), and this repo is no longer the primary development branch, though recent commits indicate some ongoing activity.

This repository is an educational collection of minimal, from-scratch implementations of core LLM and generative AI systems, maintained by DataWhale. It is not a single production application; rather, it is a set of self-contained 'Tiny' sub-projects covering a small LLM trained on TinyStories, a RAG pipeline, a ReAct agent, an LLM evaluation harness, a DDPM diffusion model, a Transformer, a GraphRAG system, and an image-based RAG pipeline. Each sub-project has its own README, requirements, and runnable code or notebooks, with heavy Chinese-language comments explaining the underlying theory. The code is intentionally simple and readable rather than production-hardened, and it serves as a teaching resource for understanding how these systems work internally.

This is a research repository from the SCIR-HI lab at Harbin Institute of Technology, focused on instruction-tuning large language models (LLaMA, Alpaca, Bloom, Huozi) with Chinese medical knowledge. The core contribution is the construction of Chinese medical instruction datasets using structured knowledge bases (cMeKG) and medical literature via GPT3.5, followed by LoRA-based fine-tuning. The repository provides scripts for fine-tuning, inference, and model export, along with pre-trained LoRA weights. While it demonstrates a complete research pipeline (data generation, training, evaluation), it is primarily a research artifact rather than a production application. The code is functional and includes a clean, reusable Prompter class for template management, but lacks software engineering rigor (no tests, no CI). Reproducibility is moderate: dependencies are pinned, but the data generation process is not fully automated, and external base models and LoRA weights must be obtained separately.

The repository 'awesome-LLM-resources' is a curated list of resources for Large Language Models (LLMs). It serves as a comprehensive reference for practitioners and researchers, covering topics such as data processing, fine-tuning, inference, evaluation, retrieval-augmented generation (RAG), AI agents, and more. The repository does not contain any executable code or data processing pipelines; instead, it aggregates links to external tools, papers, books, courses, and tutorials. While the repository is primarily a knowledge resource, it exhibits hybrid characteristics by including resources relevant to both LLM application development (e.g., inference servers, agent frameworks) and data research (e.g., data extraction tools, evaluation benchmarks). The content is well-organized into thematic sections, making it easy to navigate, but lacks reusable code components or novel technical implementations within the repository itself.