In this article, we will learn how to leverage Llama-3 to implement an advanced RAG with a fully local infrastructure.This article provides a firsthand guide for the first day of implementing advanced RAG. ◆Introduction: In this article, we will create an advanced RAG that will answer user queries based on research papers provided as input … Read more
On April 18, 2024, Meta open-sourced the Llama 3 large models[1]. Although there are only 8B[2] and 70B[3] versions, the powerful capabilities demonstrated by Llama 3 have shocked the AI large model community. I personally tested the inference capabilities of the Llama3-70B version, which are very close to OpenAI’s GPT-4[4]. Moreover, a 400B super large … Read more
As we all know, Zuckerberg’s Meta has open-sourced Llama3 with two versions: the 8B and 70B pretrained and instruction-tuned models. There is also a larger 400B parameter version expected to be released this summer, which may be the first open-source model at the GPT-4 level! Let’s start with a preliminary understanding of Llama3. Model Architecture … Read more
Introduction Recently, while reviewing the papers I had previously studied in depth, I found that some notes were still very valuable. I made some minor adjustments and am publishing them for everyone to see. LLama3 is a paper from a few months ago, but each reading still brings new insights. This article discusses key points, … Read more
1. Test using the trained weights from transformers import AutoModelForCausalLM,AutoTokenizer,TextGenerationPipeline import torch tokenizer = AutoTokenizer.from_pretrained(r"E:\大模型AI开发\AI大模型\projects\gpt2\model\models–uer–gpt2-chinese-cluecorpussmall\snapshots\c2c0249d8a2731f269414cc3b22dff021f8e07a3") model = AutoModelForCausalLM.from_pretrained(r"E:\大模型AI开发\AI大模型\projects\gpt2\model\models–uer–gpt2-chinese-cluecorpussmall\snapshots\c2c0249d8a2731f269414cc3b22dff021f8e07a3") # Load our own trained weights (Chinese poetry) model.load_state_dict(torch.load("net.pt")) # Use the system's built-in pipeline tool to generate content pipline = TextGenerationPipeline(model,tokenizer,device=0) print(pipline("天高", max_length=24)) The performance is actually not good: 2. Post-process the AI-generated results # Customized … Read more
On April 18th, local time in the US, Meta released the highly anticipated open-source large language model Llama3, which offers two model sizes: 8B and 70B parameters, with a 400B version expected to be released in the future. Meta mentioned in their blog that thanks to improvements in training techniques, the Llama3 models, both 8B … Read more
1. Chatting If there is one industry that feels the most threatened by the arrival of AI, I believe e-commerce customer service should be on the list. Currently, platforms like Taobao and JD.com primarily use AI customer service for replies, only resorting to human agents if customers are dissatisfied, thus achieving cost reduction and efficiency … Read more