Quantization is a technique utilized within large language models (LLMs) to convert weights and activation values of high precision data, usually 32-bit floating point (FP32) or 16-bit floating point (FP16), to a lower-precision data, like 8-bit integer (INT8). High precision data (refer...
It supports fine-tuning techniques such as full fine-tuning, LoRA (Low-Rank Adaptation), QLoRA (Quantized LoRA), ReLoRA (Residual LoRA), and GPTQ (GPT Quantization). Run LLM fine-tuning on Modal For step-by-step instructions on fine-tuning LLMs on Modal, you can follow the tutorial her...
However, PagedAttention is not the only capability that vLLM provides. Additional performance optimizations that vLLM can offer include: PyTorch Compile/CUDA Graph - for optimizing GPU memory. Quantization - for reducing memory space required to run models. Tensor parallelism - for breaking up the ...
LLMs are trained on huge sets of data— hence the name "large." LLMs are built on machine learning: specifically, a type of neural network called a transformer model. In simpler terms, an LLM is a computer program that has been fed enough examples to be able to recognize and interpret...
此笔记尝试从几篇比较经典的LLMs量化文章出发和从可解释性的角度去理解Transformer架构中存在的Outlier(离群值)问题 Understanding and Overcoming the Challenges of Efficient Transformer Quantization 高通发表在EMNLP2021上的一篇LLMs量化文章,当时研究的还是BERT的量化。作者发现,激活量化对BERT模型的精度影响很大,W8A32...
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Fine-tuning LLMs using techniques like LoRA and QLoRA Configuring settings for training, quantization, and evaluation of the models Prompt templates and dataset integration for more accessible training torchtune is integrated with popular machine learning platforms such as Hugging Face, Weights & Biases...
quantization_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_use_double_quant=True, bnb_4bit_compute_dtype=torch.bfloat16 )Copy Code Load Tokenizer and Model tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-9b-it", device="cuda") model =...
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