conversational QA
gpt2-conversational-or-qa-i1-GGUFLocutusque_-_gpt2-conversational-or-qa-ggufgpt2-conversational-or-qa-GGUFgpt2-conversational-or-qagpt2-conversational-or-qa-GGUFpegasus-conversational-qaMistral-7B-Instruct-v0.3-4bit-fp16-finetuned-mental-health-conversational_120stepsLocutusque_-_gpt2-conversational-or-qa-4bits
aviation_qa_conversational_175kdetails_Locutusque__gpt2-conversational-or-qa
Dataset Card for Evaluation run of Locutusque/gpt2-conversational-or-qa
Dataset Summary
Dataset automatically created during the evaluation run of model Locutusque/gpt2-conversational-or-qa on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard-old/details_Locutusque__gpt2-conversational-or-qa.prop-trading-qa-conversational-ai
Prop Trading Q&A Dataset for Conversational AI
Description
This dataset contains 200+ curated question-answer pairs covering the domain of proprietary (prop) trading firms. It is designed to serve as training and retrieval data for building AI assistants, chatbots, and educational tools focused on prop trading knowledge.
Each entry consists of a natural-language question paired with a detailed, factual answer. The data spans ten thematic categories ranging from… See the full description on the dataset page: https://huggingface.co/datasets/propfirmkey/prop-trading-qa-conversational-ai.cot-conversational-qa-v1
CoT Conversational QA v1
Conversational QA pairs describing Qwen3-8B chain-of-thought reasoning traces. Generated by prompting Gemini 2.0 Flash to describe observable facts about CoT text with zero logical leaps.
Purpose
Training data for activation oracles — models that read their own activations and answer questions about their reasoning. The oracle sees strided activations at sentence boundaries, not the CoT text itself.
The training signal: simple question → factual… See the full description on the dataset page: https://huggingface.co/datasets/ceselder/cot-conversational-qa-v1.