Qwen3-VL-32B-Instruct AWQ - INT4 is a 4-bit quantized version based on the Qwen3-VL-32B-Instruct base model. It uses the AWQ quantization method, significantly reducing storage and computing resource requirements while maintaining performance. This is the most powerful vision-language model in the Qwen series, with comprehensive upgrades in text understanding, visual perception, context length, etc.

MultimodalTransformers

Fanar-1-9B-Instruct is a powerful Arabic-English large language model developed by the Qatar Computing Research Institute (QCRI). It supports Modern Standard Arabic and multiple Arabic dialects, and is aligned with Islamic values and Arab culture.

Natural Language ProcessingTransformersMultiple Languages

ModularStarEncoder-300M is an encoder model fine-tuned on the SynthCoNL dataset based on the ModularStarEncoder-1B pre-trained model. It is specifically designed for code-to-code and text-to-code retrieval tasks. This model uses hierarchical self-distillation technology, allowing users to choose different layer versions according to their computing power.

Natural Language ProcessingTransformers

The Alpaca model replicated by the Tatsu team at Stanford University. This is a large language model that performs instruction fine-tuning based on LLaMA-7B. The model was trained on 4 A100 GPUs for 6 hours, with computing power donated by redmond.ai. It does not use LoRA technology and adopts the native fine-tuning method.

Natural Language ProcessingTransformers