Supervised Fine-Tuning (SFT) is one of the most well-known methods for training Large Language Models (LLM). Essentially, it is similar to traditional language modeling, where the model learns certain knowledge through training data.
Read More »Supervised Fine-tuning Trainer (SFTTrainer) Note
In the fine-tuning tasks of Large Language Models (LLM), several methods such as Supervised Fine-Tuning (SFT), Reinforcement Learning from Human Feedback (RLHF), and Direct Preference Optimization (DPO) are all viable approaches. However, there are some differences among them.
Read More »LLM Fine-tuning Note – Differences Between SFT and DPO
I previously wrote a note introducing the vLLM accelerated inference framework (Using vLLM To Accelerate The Decoding Of Large Language Model), but due to space and time constraints, I couldn’t delve into more detailed features.
Read More »Using vLLM To Accelerate Inference Speed By Continuous Batching
HuggingFace’s Text Generation Inference (TGI) is a framework specifically designed to deploy and accelerate LLM inference services. Below is its architecture diagram:
Read More »Note Of HuggingFace Text Generation Inference (TGI)
Since last year, I have been filled with enthusiasm and curiosity about Multi-Modal AI models. As a staunch advocate of AGI, I believe that AI’s current potential has not yet reached its ceiling. One significant bottleneck and research direction in AI today is naturally the integration of various modalities (text, images, audio…) in model applications.
Read More »Use Text To Retrieve Images: Introduction Of Multi-Modals ColPali
Recently, Meta AI has released various versions of Llama 3.1 (405B, 70B, 8B), with the 405B model being particularly noteworthy. It’s the first time an open-source LLM has caught up with closed-source models like GPT-4 and Claude-3.5. At the same time, Meta AI has also released a smaller model called Prompt-Guard-86M.
Read More »Meta-llama–Prompt-Guard-86M: Open-Source Model for Prompt Protection, Detecting Malicious Attacks
HuggingFace Model Hub is now a widely recognized and essential open-source platform for every one. Every day, countless individuals and organizations upload their latest trained models (including those for text, images, speech, and other domains) to this platform. It can be said that anyone working in AI-related fields frequently browses the HuggingFace platform website.
Read More »Use `snapshot_download` To Download The Models Of HuggingFace Hub
Mistral 7B is a large language model (LLM) proposed on September 27, 2023, trained by the Mistral AI team, which also released its weights as open source. Interestingly, it uses the highly permissive Apache 2.0 license, unlike Llama 2, which has its own Llama license terms. Therefore, Mistral 7B is truly “open source” (Llama’s license requires discussion with Meta AI when the service volume reaches 700 million).
Read More »[Paper Reading] Mistral 7B
Recently, I have been exploring models used for Optical Character Recognition (OCR). In the past, OCR was a very popular research field as it was one of the earliest practical applications of computer vision. Today, OCR has become a very mature task, and you can easily find high-performance open-source models online.
Read More »PaddleOCR: A Framework and Model Specialized in Chinese Optical Character Recognition (OCR)
In today’s era of flourishing large language models, researchers and companies are racking their brains to apply these models to their work. However, speaking personally, the performance of current language models is still not strong enough, and their application scenarios are limited, often far less than that of humans.
But there is one type of task for which large language models are naturally quite suitable: information extraction in any scenario, which is what I want to introduce today, the NuExtract model.
Read More »NuExtract: A Large Language Model For Information Extraction
