Top Papers
transformers
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
Open Vocabulary Object Detection
Real-Time Object Detection
+1
Fine-mixing: Mitigating Backdoors in Fine-tuned Language Models
huggingface/pytorch-transformers
•
•
In this work, we take the first step to exploit the pre-trained (unfine-tuned) weights to mitigate backdoors in fine-tuned language models.
pytorch
pytorch/pytorch
•
•
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Adapting Large Language Model with Speech for Fully Formatted End-to-End Speech Recognition
openai/whisper
•
•
Most end-to-end (E2E) speech recognition models are composed of encoder and decoder blocks that perform acoustic and language modeling functions.
GPT4All: An Ecosystem of Open Source Compressed Language Models
nomic-ai/gpt4all
•
•
It is our hope that this paper acts as both a technical overview of the original GPT4All models as well as a case study on the subsequent growth of the GPT4All open source ecosystem.
Highly Optimized Kernels and Fine-Grained Codebooks for LLM Inference on Arm CPUs
Large language models (LLMs) have transformed the way we think about language understanding and generation, enthralling both researchers and developers.
Training language models to follow instructions with human feedback
In this paper, we show an avenue for aligning language models with user intent on a wide range of tasks by fine-tuning with human feedback.
Language Models are Few-Shot Learners
ggerganov/llama.cpp
•
•
NeurIPS 2020
By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do.
Ranked #1 on
Question Answering
on CoQA
(Overall metric)
