#Llama

XTuner is a versatile toolkit for efficiently fine-tuning both language and vision models, compatible with a variety of GPU platforms. It offers support for models like InternLM, Mixtral, Llama, and VLMs such as LLaVA, ensuring flexibility and scalability. With features such as FlashAttention and Triton kernels, XTuner optimizes training processes and integrates seamlessly with DeepSpeed. It supports several training algorithms, including QLoRA and LoRA, and provides a structured data pipeline that accommodates diverse dataset formats. XTuner models are ready for deployment through systems like LMDeploy and can be evaluated with tools such as OpenCompass. Recent updates include support enhancements and installation guidance.

Torchtune supports the creation, finetuning, and experimentation with large language models (LLMs) like Llama, Gemma, and Mistral through PyTorch. It offers training recipes that improve memory efficiency and performance scaling. YAML configurations and diverse dataset support simplify model management. Integration with libraries such as Hugging Face ensures efficient collaboration and logging. The latest update includes support for Llama 3.2 Vision models, enhancing both vision and text processing.

SwiftInfer integrates TensorRT to enhance Streaming-LLM, enabling LLM inference with extended input length and mitigating model collapse through Attention Sink technology. Developed from TensorRT-LLM, it offers a flexible framework for the deployment of efficient, multi-turn conversational AI systems. The platform features detailed installation guidance, compatibility checks, and benchmarking data against the original PyTorch version. SwiftInfer persistently advances to lead in LLM technology, underlining effective integration and computational efficiency. Discover a solid solution for sophisticated AI inference.

Explore a curated list of open-source Korean LLMs, featuring models like Polyglot-Ko and KoAlpaca, built on architectures such as GPT-NeoX and Llama-2. This resource includes information on model sizes, creators, base models, and commercial usage conditions. It supports your exploration and potential implementation of Korean language processing tools. Access models available on platforms like Huggingface, and participate by sharing updates or new LLMs.

Llama Chinese Community focuses on optimizing Llama models for Chinese applications, supported by an experienced NLP engineering team. The community continually improves the models' capabilities, facilitating global collaboration among developers. Providing resources, networking, and opportunities for technical sharing, it recently launched the Llama 3.1 models along with tools for testing and deployment. Participants can join online events and collaborative activities to advance in Chinese NLP.

The Llama project offers accessible LLMs for developers and researchers, supporting responsible AI development with a wide ecosystem and millions of downloads. Fostering global collaboration, it offers licensed tools through platforms like Hugging Face, emphasizing trust and safety in AI advancements.

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