datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
in1k_clip_qwen25vl_3b_224res_64tokens_new_ptin1k_clip_qwen25vl_3b_448res_256tokens_new_merged_ptpubmedqa-recursive-llm-degradation-qwen2.5-0.5b
PubMedQA Recursive LLM Degradation — Qwen2.5-3B
This repository contains synthetic biomedical question-answering data
and model predictions generated as part of a study of recursive
fine-tuning and model degradation.
Base Model
Qwen/Qwen2.5-3B
Source Dataset
The experiments use the PubMedQA dataset:
qiaoxin/PubMedQA
This repository contains generated/derived research artifacts and does
not redistribute the original PubMedQA dataset in its entirety.… See the full description on the dataset page: https://huggingface.co/datasets/chrislimbe/pubmedqa-recursive-llm-degradation-qwen2.5-0.5b.pubmedqa-recursive-llm-degradation-qwen2.5-3b
PubMedQA Recursive LLM Degradation — Qwen2.5-3B
This repository contains synthetic biomedical question-answering data
and model predictions generated as part of a study of recursive
fine-tuning and model degradation.
Base Model
Qwen/Qwen2.5-3B
Source Dataset
The experiments use the PubMedQA dataset:
qiaoxin/PubMedQA
This repository contains generated/derived research artifacts and does
not redistribute the original PubMedQA dataset in its entirety.… See the full description on the dataset page: https://huggingface.co/datasets/chrislimbe/pubmedqa-recursive-llm-degradation-qwen2.5-3b.Qwen2.5-7B-Instruct-em-evalqwen2.5-snmf-features
Qwen2.5 SNMF features for unlearning
MLP Semi-NMF factorizations and (when present) LLM interpretations for
SNMF concept unlearning on Qwen/Qwen2.5-3B-Instruct (rank 100, seed 42).
These files are the SNMF track only: per-layer MLP directions used to project
concept features out of up_proj / down_proj. There is no embedding-matrix
factorization in this dataset.
Qwen/Qwen3.5-2B features live in
shirasko/qwen-snmf-features.
Layout (same directory scheme used by the… See the full description on the dataset page: https://huggingface.co/datasets/shirasko/qwen2.5-snmf-features.resid-xprmt-generate-qwen2.5-7bwikifacts-sents-qwen2.5-32b-qrelsCrosscoder-Qwen2.5-1.5B-vs-DeepScaleR-1.5B_max_activating_examplesSee Files and versions for pickled dictionaries and database versions of of max activating examples organized per available layer, as well as dataframes of available features.
SIGNAL-Dataset-Hiddens-Qwen-Qwen2.5-7BSIGNAL-Dataset-Hiddens-Qwen-Qwen2.5-7B-Instructqwen2.5-3b-blind-spots
Qwen2.5-3B Factual Recall Blind Spots
Model Tested
Qwen/Qwen2.5-3B
A 3.09B parameter base causal language model, pretrained only.
How I Loaded the Model
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_name = "Qwen/Qwen2.5-3B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float16,
device_map="auto"
)
Loaded on Google Colab.… See the full description on the dataset page: https://huggingface.co/datasets/suhanii23/qwen2.5-3b-blind-spots.T2G-1k-Qwen2.5-3B
T2G
Overview
T2G is a synthetic data consisting of text-graph pairs designed to finetune LLMs on information extraction tasks, specifically text-to-graph conversion.
Dataset Structure
The dataset is organized into the following main components:
Train Set: 800 instances for training models.
Validation Set: 100 for validating model performance.
Test Set: 100 instances for final evaluation.
Data Fields
Each instance in the dataset contains the… See the full description on the dataset page: https://huggingface.co/datasets/ESITime/T2G-1k-Qwen2.5-3B.DPO-bad-boy-chinese-for-Qwen2.5
BTF Chinese DPO Dataset
数据集描述
这是一个用于中文大语言模型DPO (Direct Preference Optimization) 训练的数据集。该数据集经过特殊处理，用于测试调整模型的语言风格和表达方式。
⚠️ 免责声明: 此数据集仅供研究目的使用。请负责任地使用，并遵守相关法律法规和道德准则。
数据集统计
训练集大小：80% 的总数据
测试集大小：20% 的总数据
数据格式：instruction-following 格式[INST] 问题 [/INST] 回答
使用方法
from datasets import load_dataset
dataset = load_dataset("your-username/dataset-name")
数据集结构
数据集包含两个分片：
train: 训练集
test: 测试集
每条数据包含以下字段：
text: 包含指令和回答的完整文本
局限性和注意事项… See the full description on the dataset page: https://huggingface.co/datasets/MLZoo/DPO-bad-boy-chinese-for-Qwen2.5.Qwen2-72bfeature-extract-qwen2-vlFINE_TUNING_LLM_QWEN2qwen_2.5_7b_soln_gpt_4o_verifyqwen2-fukellm-ragasT2G-Event-1k-Qwen2.5-3B-changed-formatqwen25-3b-medical-blindspots
Blind Spots of Qwen2.5-3B (Base Model) — Medical & Epidemiology Domain
Dataset Summary
This dataset documents 10 diverse failure cases of the Qwen/Qwen2.5-3B base language model, focusing on medical, epidemiological, and multilingual (Urdu) prompts. It was created as part of the Fatima Fellowship 2026 application technical challenge.
Each row contains:
input: the prompt given to the model
expected_output: the correct answer
model_output: what the model actually generated… See the full description on the dataset page: https://huggingface.co/datasets/asadkhn/qwen25-3b-medical-blindspots.T2G-Event-1k-Qwen2.5-3Bqwen2-1.5b-blindspotsimport torchfrom transformers import AutoTokenizer, AutoModelForCausalLMmodel_name = "Qwen/Qwen2-1.5B"device = "cuda" if torch.cuda.is_available() else "cpu"tokenizer = AutoTokenizer.from_pretrained(model_name)model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.bfloat16, device_map="auto")def generate_response(prompt): inputs = tokenizer(prompt, return_tensors="pt").to(device) outputs = model.generate(**inputs, max_new_tokens=50) response =… See the full description on the dataset page: https://huggingface.co/datasets/Abdou220/qwen2-1.5b-blindspots.qwen2.5-1.5b-blind-spots
Qwen2.5-1.5B Blind Spots Dataset
Model Tested
Model: Qwen/Qwen2.5-1.5B
Parameters: 1.5 Billion
Type: Base language model (not instruction-tuned)
Release date: Within the last 6 months
How I Loaded the Model
!pip install transformers accelerate -q
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_name = "Qwen/Qwen2.5-1.5B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(… See the full description on the dataset page: https://huggingface.co/datasets/hchrAsma/qwen2.5-1.5b-blind-spots.blind-spots-qwen2.5
Blind Spots of Qwen2.5-1.5B
Model Tested
Model: Qwen/Qwen2.5-1.5B
Link: https://huggingface.co/Qwen/Qwen2.5-1.5B
Type: Base language model (not finetuned)
Parameters: 1.5 Billion
How I Loaded the Model
I used Google Colab with a free T4 GPU.
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_name = "Qwen/Qwen2.5-1.5B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(… See the full description on the dataset page: https://huggingface.co/datasets/Maham789/blind-spots-qwen2.5.Qwen-2.5-14B-Hindi-Instruct-Databigmath-grpo-rollouts-qwen25-3bqwen2.5-1.5B-blind-spotsQwen2_13_Blind_Spots
Qwen-2 13 Blind Spots Dataset
This dataset contains 13 diverse blind spots of the Qwen2.5-3B base model (Hugging Face). Each row contains:
input: The prompt given to the model
expected_output: The correct answer
model_output: The answer the model actually produced
Model Tested
Model: Qwen2.5-3B (Base)
Link: https://huggingface.co/Qwen/Qwen2.5-3B
How the Model Was Loaded
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch… See the full description on the dataset page: https://huggingface.co/datasets/noorfatima67/Qwen2_13_Blind_Spots.qwen2b-blindspots
Qwen3.5-2B Blindspots Dataset
Overview
This dataset contains examples where the base language model Qwen3.5-2B-Base produces incorrect or unexpected outputs. The goal of this dataset is to identify blind spots in small frontier language models by testing them on tasks involving reasoning, counting, symbolic manipulation, and strict instruction following.
The dataset records:
The input prompt
The expected output
The actual model output
The error category
These examples… See the full description on the dataset page: https://huggingface.co/datasets/Sriyanshsh1805/qwen2b-blindspots.