# Low memory usage

FastVLM-1.5B-Stage3-MNN is a text generation model based on the Transformer architecture. It is an 8-bit quantized version of FastVLM-1.5B-Stage3, suitable for text generation scenarios such as chatting.

Quantized version of Qwen3-8B, quantized using the imatrix option of llama.cpp, suitable for text generation tasks.

EXAONE-3.5-32B-Instruct is a large language model with 32B parameters, supporting instruction following and dialogue tasks.

Quantized version based on SicariusSicariiStuff/Impish_Mind_8B model, processed with llama.cpp tools for various quantization methods, suitable for text generation tasks.

ESM++ is a faithful implementation of ESMC, supporting batch processing and compatible with the standard Huggingface interface without requiring the ESM Python package. The small version corresponds to the 300-million-parameter version of ESMC.

Flux.1 Lite is a distilled 8-billion parameter Transformer model derived from FLUX.1-dev, optimized for text-to-image generation tasks, reducing memory usage while maintaining accuracy and improving speed.