# English Q&A

This model is a fine-tuned version of Llama-3.2-1B-Instruct using LoRA (Low-Rank Adaptation) weights, primarily designed to assist in answering various topic-related questions and providing relevant information.

Doge 160M is a small language model based on dynamic masked attention mechanism, trained with supervised fine-tuning (SFT) and direct preference optimization (DPO).

An instruction generation model fine-tuned on the deduplicated version of Pythia-2.8B, optimized for synthetic instruction datasets

A Q&A model fine-tuned on the TriviaQA dataset based on bert-base-uncased

This is a RoBERTa-based extractive question answering model, specifically trained on the SQuAD 2.0 dataset, suitable for English Q&A tasks.

A Q&A model fine-tuned on the SQuAD v2 dataset based on DistilRoBERTa-base, featuring lightweight and efficient characteristics.

BERT-large English Q&A model pre-trained with whole word masking and fine-tuned on SQuAD v1.1, quantized to INT8 precision

A Q&A model fine-tuned on the SQuAD2.0 dataset based on the BERT-Medium architecture, designed for resource-constrained environments

A question-answering model developed based on the roberta-large architecture, specifically trained for the SQuAD 2.0 dataset

A compact Q&A model fine-tuned on the SQuAD2.0 dataset based on Google's BERT-Tiny architecture, with a size of only 16.74MB

BERT-Mini is a small BERT model developed by Google Research, fine-tuned on the SQuAD 2.0 dataset for question-answering tasks, suitable for environments with limited computational resources.

A T5-base fine-tuned joint model for question generation and answer extraction, supporting English text processing

This is a Q&A model optimized via nn_pruning library, retaining 50% of original weights, fine-tuned on SQuAD v1 with F1 score reaching 88.72

A Q&A system model fine-tuned on SQuAD v1 based on the BERT-base uncased model, optimized via the nn_pruning library, achieving 2.01x faster inference speed and a 0.69 improvement in F1 score.

This is a Q&A model based on the RoBERTa architecture, specifically trained on the SQuAD 2.0 dataset, suitable for English Q&A tasks.