# Quantization

This product is a 4-bit quantized language model based on Qwen2.5-32B, achieving efficient inference and low resource consumption through GPTQ technology. It significantly reduces the model's storage and computational demands while maintaining high performance, making it suitable for use in resource-constrained environments. The model primarily targets applications requiring high-performance language generation, including intelligent customer service, programming assistance, and content creation. Its open-source license and flexible deployment options offer broad prospects for application in both commercial and research fields.

InternLM3 is a series of high-performance language models developed by the InternLM team, specializing in text generation tasks. This model is optimized through various quantization techniques, allowing it to run efficiently across different hardware environments while maintaining excellent generation quality. Its primary advantages include efficient inference performance, diverse application scenarios, and optimization support for various text generation tasks. InternLM3 is designed for developers and researchers who require high-quality text generation, enabling them to迅速implement applications in the field of natural language processing.

1.58-bit FLUX is an advanced text-to-image generation model that employs 1.58-bit weights (values of {-1, 0, +1}) to quantify the FLUX.1-dev model while maintaining comparable performance for generating 1024x1024 images. This method does not require access to image data and relies entirely on the self-supervision of the FLUX.1-dev model. Additionally, a custom kernel has been developed to optimize 1.58-bit operations, achieving a 7.7x reduction in model storage, a 5.1x decrease in inference memory, and improved inference latency. Extensive evaluations in GenEval and T2I Compbench benchmarks show that 1.58-bit FLUX significantly enhances computational efficiency while maintaining generation quality.

Qwen2.5-Coder is a model specifically designed for code generation, significantly improving capabilities in this area, with a variety of parameter sizes and support for quantization. It is free and enhances efficiency and quality for developers.

The Llama model is a large language model developed by Meta. Through quantization technology, it reduces model size, increases speed, and maintains quality and security. These models are especially suitable for mobile devices and edge deployments, enabling fast on-device inference on resource-constrained devices while minimizing memory usage. The development of the Quantized Llama model marks an important advancement in mobile AI, allowing more developers to build and deploy high-quality AI applications without requiring extensive computational resources.

Torchao is a library for PyTorch focused on custom data types and optimization, supporting the quantization and sparsification of weights, gradients, optimizers, and activation functions for both inference and training. It is compatible with torch.compile() and FSDP2, enabling acceleration for most PyTorch models. Torchao aims to enhance model inference speed and memory efficiency while minimizing accuracy loss through techniques such as Quantization Aware Training (QAT) and Post Training Quantization (PTQ).

Cog inference for flux models is an inference engine for FLUX.1 [schnell] and FLUX.1 [dev] models, developed by Black Forest Labs. It supports compilation and quantization, sensitive content checks, and img2img support, aimed at enhancing the performance and safety of image generation models.

Nemotron-Mini-4B-Instruct is a compact language model developed by NVIDIA, optimized through distillation, pruning, and quantization for improved speed and ease of deployment on devices. It is a fine-tuned version of nvidia/Minitron-4B-Base, derived from Nemotron-4 15B via NVIDIA's large language model compression techniques. This instructional model is optimized for role-playing, retrieval-augmented question answering (RAG QA), and function invocation, supporting a context length of 4096 tokens and ready for commercial use.

ComfyUI-GGUF is a project that provides GGUF quantization support for native ComfyUI models. It allows model files to be stored in GGUF format, which is promoted by llama.cpp. Although standard UNET models (conv2d) are not suitable for quantization, transformer/DiT models like flux appear to be minimally affected by quantization. This enables them to run on low-end GPUs with lower bits per weight variable rate.

vLLM is a fast, easy-to-use, and efficient library for large language model (LLM) inference and service provision. By leveraging the latest service throughput technologies, efficient memory management, continuous batch processing requests, CUDA/HIP graph fast model execution, quantization techniques, and optimized CUDA kernels, it provides high-performance inference services. vLLM seamlessly integrates with popular HuggingFace models, supports various decoding algorithms including parallel sampling and beam search, supports tensor parallelism for distributed inference, supports streaming output, and is compatible with OpenAI API servers. Moreover, vLLM supports both NVIDIA and AMD GPUs, as well as experimental prefix caching and multi-lora support.