Topic: flan-t5 Goto Github
Some thing interesting about flan-t5
Some thing interesting about flan-t5
flan-t5,My solutions to the lab assignments in the Generative AI with Large Language Models course offered by Amazon Web Services.
User: arasgungore
Home Page: https://www.coursera.org/learn/generative-ai-with-llms
flan-t5,Use AI to personify books, so that you can talk to them 🙊
User: batmanscode
flan-t5,Official Code for Analysis Done in the Paper "Frugal Prompting for Dialog Models"
User: bsantraigi
Home Page: https://arxiv.org/abs/2305.14919
flan-t5,Finetuned FLAN-T5 to translate English to Hawaiian Pidgin
User: claudiatang-git
Home Page: https://huggingface.co/claudiatang/flan-t5-base-eng-hwp
flan-t5,This repository contains code for extending the Stanford Alpaca synthetic instruction tuning to existing instruction-tuned models such as Flan-T5.
Organization: declare-lab
flan-t5, Performing Prompt engineering on a dialogue summarization task using Flan-T5 and the dialogsum dataset. Exploring how different prompts affect the output of the model, and compare zero-shot and few-shot inferences.
User: dhruvmiyani
flan-t5,The TABLET benchmark for evaluating instruction learning with LLMs for tabular prediction.
User: dylan-slack
Home Page: https://dylanslacks.website/Tablet/
flan-t5,In-context learning, Fine-Tuning, RLHF on Flan-T5
User: erionis
flan-t5,Toolkit for fine-tuning, ablating and unit-testing open-source LLMs.
Organization: georgian-io
flan-t5,Notebook for Flan-T5 – an alternative to large language models like GPT-3 & GPT-4 for NLP tasks like named entity recognition and text generation.
Organization: graphcore
flan-t5,Symbol Team model for PAN@AP 2023 shared task on Profiling Cryptocurrency Influencers with Few-shot Learning
User: hamedbabaei
flan-t5,LLMs4OL: Large Language Models for Ontology Learning
User: hamedbabaei
flan-t5,This repository is made for T5 model where user can train their model on any T5 model version.
Organization: highplainscomputing
flan-t5,This repository was commited under the action of executing important tasks on which modern Generative AI concepts are laid on. In particular, we focussed on three coding actions of Large Language Models. Extra and necessary details are given in the README.md file.
User: himanshuvnm
flan-t5,Revolutionizing open-world gaming, MergeX harnesses NLP advances to empower players with limitless dialogue interactions with NPCs. By imbuing each character with a unique biography, conversations authentically align with NPC personalities, transcending traditional limitations.
User: himidiri
flan-t5,Multiple LLM based models for NLP tasks. Starting with Question answering on custom data
User: kavlata
flan-t5,LLM projects
User: kharshit
flan-t5,Project based on PyTorch-lightning and Transformers for training Seq2SeqLM models, with a primary focus on MT5 and FLAN-T5, yet not limited to them
User: kkkravets
flan-t5,A preliminary investigation for ontology alignment (OM) with large language models (LLMs).
Organization: krr-oxford
flan-t5,Developed a generative large language model fine-tuned on Stack Overflow data for question answering.
User: laceymalarky
Home Page: https://huggingface.co/lmalarky/flan-t5-base-finetuned-python_qa
flan-t5,Text-To-Text Textbots to Demonstrate Output Differences Between Models Trained on Filtered/Unfiltered Datasets for HSS4 - The Modern Context: Select Figures and Topics
User: laniw
flan-t5,In this implementation, using the Flan T5 large language model, we performed the Text Classification task on the IMDB dataset and obtained a very good accuracy of 93%.
User: m-taghizadeh
Home Page: https://youtube.com/c/MohammadTaghizadeh
flan-t5,Discussed about 4 use-cases or case studies. Discussed about the approaches and significance of these use-cases as these are different from others. There are several approaches available which can be done using LLM but here the approaches and it's significance could bring insightful approaches towards it's execution.
User: navneet1083
flan-t5,This project is based on fine-tuning LLM models (FLAN-T5) for text summarisation task using PEFT approach. All evaluation metrics being computed on ROUGE scoring and LoRA optimisation techniques being used for fine-tuning.
User: navneet1083
flan-t5,The official fork of THoR Chain-of-Thought framework, enhanced and adapted for Emotion Cause Analysis (ECAC-2024)
User: nicolay-r
Home Page: https://arxiv.org/abs/2404.03361
flan-t5,This is a project done for an assessment. I found it to be interesting and decided to share this. The idea is to create a scraper to scrap the Wikipedia page and generate question and answers
User: ong-zijian
flan-t5,Using Open-Source LLMs like FLAN-T5, built a Dialog Summarization model and did fine-tuning with DialogSum HF Dataset
User: pankajrawat9075
flan-t5,Summarize Long Document with Pretrained sequence-to-sequence LM with long-range attention!
User: purang2
flan-t5,Demonstration of LLM techniques such as prompt engineering, full finetuning, PEFT (LoRA) etc.
User: purrvaja
flan-t5,This repository contains the code to train flan t5 with alpaca instructions and low rank adaptation.
User: reason-wang
flan-t5,A template Next.js app for running language models like FLAN-T5 with Replicate's API
Organization: replicate
flan-t5,This repository contains the lab work for Coursera course on "Generative AI with Large Language Models".
User: rochitasundar
Home Page: https://www.coursera.org/account/accomplishments/certificate/8JAYVEUAQF56
flan-t5,Document Summarization App using large language model (LLM) and Langchain framework. Used a pre-trained T5 model and its tokenizer from Hugging Face Transformers library. Created a summarization pipeline to generate summary using model.
User: sahilichake
flan-t5,Performing the task of dialogue summarisation using Generative AI, whilst comparing the effects of zero shot, one shot and few shot prompt engineering. These steps are used to enhance the completion of Large Language Models (LLMs))
User: semaj87
flan-t5,AI Assistant for Customer Support
User: seyedsaeidmasoumzadeh
flan-t5, The LLM-based medical chatbot, powered by the Llama-2-7b-chat-hf model from Meta and implemented within the Langchain framework, offers personalized healthcare support.
User: shishir-dwi
flan-t5,A gradio frontend for Google's Flan-T5 Large language model, can also be adjusted for other sizes.
User: synkathairo
flan-t5,Tools and our test data developed for the HackAPrompt 2023 competition
User: terjanq
Home Page: https://hack-a-prompt.terjanq.me
flan-t5,Tutorial para treino de um modelo baseado Flan-T5 usando Flax no GCP-TPU
Organization: the-good-fellas
flan-t5,Code and data for the StarSem 2023 paper "Arithmetic-Based Pretraining -- Improvin Numeracy of Pretrained Language Models"
Organization: ukplab
Home Page: https://arxiv.org/pdf/2205.06733.pdf
flan-t5,Fine-tuning of Flan-5T LLM for text classification
User: vanekpetr
flan-t5,Official implementation of the paper "CoEdIT: Text Editing by Task-Specific Instruction Tuning" (EMNLP 2023)
User: vipulraheja
Home Page: https://aclanthology.org/2023.findings-emnlp.350/
flan-t5,Research POC on the mitigation of bias in large language models (FLAN-T5 and Bloomz) through fine-tuning.
User: wazzabeee
flan-t5,Replace OpenAI GPT with another LLM in your app by changing a single line of code. Xinference gives you the freedom to use any LLM you need. With Xinference, you're empowered to run inference with any open-source language models, speech recognition models, and multimodal models, whether in the cloud, on-premises, or even on your laptop.
Organization: xorbitsai
Home Page: https://inference.readthedocs.io
flan-t5,Rethinking Negative Instances for Generative Named Entity Recognition
User: yyding1
flan-t5,[Preprint] Learning to Filter Context for Retrieval-Augmented Generaton
User: zorazrw
Home Page: https://arxiv.org/pdf/2311.08377.pdf
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