InternLM3 Open Source: Achieving High-Performance Models with 4T DataWritten by InternLM TeamJan 16Jan 16
Announcing XTuner: An Efficient Finetune Toolkit for LLMWith XTuner, 8GB GPU memory is all you need to create your own AI assistant.Sep 1, 2023Sep 1, 2023
The AI New Era: How Should Large Models “Rack Their Brains”How do we address the shortcomings of large language models in complex reasoning? By using the chain-of-thought prompting technique!Aug 24, 2023Aug 24, 2023
Benchmarking the multi-modal capability of Bard with MMBenchIn order to provide an overview of Bard’s multi-modal ability, we evaluate it on the test split of MMBench as below and compare it with…Aug 18, 2023Aug 18, 2023
Faster and More Efficient 4-bit quantized LLM Model InferenceLMDeploy has released an exciting new feature — 4-bit quantization and inference. This not only trims down the model’s memory overhead to…Aug 16, 20231Aug 16, 20231
Thoroughly evaluate AX620A from the perspective of security industryFrom the perspective of security business, we have conducted a comprehensive test of AX620A, covering various evaluation indicators such asAug 10, 2023Aug 10, 2023
Deploy Llama-2 models easily with LMDeploy!This article will guide you on how to quickly deploy the Llama-2 models with LMDeploy.Aug 2, 2023Aug 2, 2023
It’s 2023. Is PyTorch’s FSDP the best choice for training large models?Why codebase for training large models tends to use frameworks like DeepSpeed or ColossalAI ，with scant regard for PyTorch’s native FSDP…Aug 1, 20232Aug 1, 20232
Fine-tuning Llama2 takes less than 200 lines of code!This article will guide you on how to fine-tuning Llama2 takes less than 200 lines of code!Jul 27, 2023Jul 27, 2023
Join OpenMMLab Codecamp: Harness Your Coding Skills and Shape the Future of Open Source!Want to improve programming skills?Jul 24, 2023Jul 24, 2023
