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Current Best Practices for Training LLMs from Scratch 目录: 1、 数据收集 2、 数据预处理 3、 预训练 4、 指令微调 5、 基于人类反馈的强化学习(RLHF) 1、 数据收集, 高质量、高容量、多样化的数据集有助于下游任务中的模型性能以及模型收敛。数据集的多样性对于LLM来说尤其重要。这是因为多样性提高了模...
在NeurIPS 2024 ENSLP workshop这个专注于模型效率提升的研讨会上,微软亚洲研究院名为《Retrieval Attention: Accelerating Long-Context LLM Inference via Vector Retrieval》的论文荣获最佳论文奖(Best Paper Award)。 该研究创造性地提出使用向量索引来动态检索最关键的KV tokens,以充分利用注意力机制的稀疏性,加速大...
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1 NVIDIA T4 GPU, 16GB Memory Where’s the code? Evaluation notebooks for each of the above embedding models are available: voyage-lite-02-instruct text-embedding-3-large UAE-Large-V1 To run a notebook, click on the Open in Colab shield at the top of the notebook. The notebook will ...
以o1 为起点,由于模型推理能力的增强,以及软件公司用 LLM 开发新产品或进行自我改造的积极性提升,推理需求指数级增长让今年下半年以来 CSP ASIC 显著受益,CSP 离下游需要推理的客户群体更近,Amazon、Google、微软等大厂都在通过自有芯片研减少对 GPU 的依赖。
Hardware: GeForce RTX 4060 Laptop GPU with 60W maximum graphics power. Laptop without GeForce RTX: Intel Core i7 13th gen CPU with integrated graphics. Engineering: MATLAB - Geomean of single-precision directed tests | Artificial Intelligence: Training MLPerf-compliant TensorFlow/ResNet50 on WSL (im...
It provides an easy-to-use tool to reduce the serving cost of LLMs.Here we provide two examples of AWQ application: Vicuna-7B (chatbot) and LLaVA-13B (visual reasoning) under ./examples directory. AWQ can easily reduce the GPU memory of model serving and speed up token generation. It...
Powered by NVIDIA H100 GPUs with fourth-generation Tensor Cores and a Transformer Engine, delivering exceptional AI training and inference performance Flexible configurations from single-GPU to 8-GPU setups Pre-installed Python and Deep Learning software packages ...
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