datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
luna-4B
lunaXL
Please see discussion (including announcement)
CloudSurf-4B-FC-bfcl-results
CloudSurf-4B-FC — raw BFCL V4 result files
Raw, unmodified BFCL V4 evaluation outputs backing the leaderboard submission
PR ShishirPatil/gorilla#1357
for CloudSurf-4B-FC
(a google/gemma-4-E4B-it fine-tune, Apache-2.0).
Both sides are included: our tuned runs and the stock gemma-4-E4B-it
baselines re-measured on the identical rig, so every number in the PR can be
recomputed from primary files.
Whiskers are the min–max across the three runs on each side. Stock wins
Irrelevance… See the full description on the dataset page: https://huggingface.co/datasets/cloudsurf-software/CloudSurf-4B-FC-bfcl-results.SIGNAL-Dataset-Hiddens-Qwen-Qwen3-4B-Instruct-FP8This dataset contains hidden states of Qwen3-4B-Instruct model generated using SIGNAL Dataset.
Sentence tokenization
from transformers import AutoTokenizer
from datasets import load_dataset
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-4B-Instruct-2507")
# TBD
financial-man-4bbc37
financial-man-4bbc37
Synthetic weather test data: 52 rows in data.csv.
All values are randomly generated fictional examples, not real observations, products, or user activity. Intended only for CSV loading and pipeline tests; not suitable for scientific or business conclusions. Columns are sampled independently and do not model real-world correlations.
Fields
sample_id: random identifier for this generated sample.
row_id: sequential row number starting at 1.… See the full description on the dataset page: https://huggingface.co/datasets/Patricia-Davis/financial-man-4bbc37.project_20260813_014231_4b7f50d9
Oceania Tech Monthly Efficiency Dataset
Monthly operational efficiency metrics for technology & internet companies across Australia and New Zealand, maintained by the APAC R&D data analytics team based in Sydney.
1. Business Background
The Australia & New Zealand (ANZ) region is home to a fast-growing technology and internet ecosystem spanning SaaS, e-commerce, online education (EdTech), fintech, healthtech, martech, data & AI, logistics tech, cybersecurity, HR… See the full description on the dataset page: https://huggingface.co/datasets/toolathon123/project_20260813_014231_4b7f50d9.spiritual-development-4b59d7
spiritual-development-4b59d7
Synthetic products test data: 34 rows in data.csv.
All values are randomly generated fictional examples, not real observations, products, or user activity. Intended only for CSV loading and pipeline tests; not suitable for scientific or business conclusions. Columns are sampled independently and do not model real-world correlations.
Fields
sample_id: random identifier for this generated sample.
row_id: sequential row number starting… See the full description on the dataset page: https://huggingface.co/datasets/Karen-Williams/spiritual-development-4b59d7.raw-penalty-4bcb9e
raw-penalty-4bcb9e
Synthetic weather test data: 48 rows in data.csv.
All values are randomly generated fictional examples, not real observations, products, or user activity. Intended only for CSV loading and pipeline tests; not suitable for scientific or business conclusions. Columns are sampled independently and do not model real-world correlations.
Fields
sample_id: random identifier for this generated sample.
row_id: sequential row number starting at 1.… See the full description on the dataset page: https://huggingface.co/datasets/aikeda6/raw-penalty-4bcb9e.SIGNAL-Dataset-Hiddens-RefalMachine-RuadaptQwen3-4B-Instructmedgemma-4b-hematologic-oncology-blind-spots
MedGemma Blind Spots: Hematologic Oncology & CAR-T Immunotherapy
A 13-probe red-team evaluation showing how Google's MedGemma-4B confidently hallucinates clinical-trial statistics, fabricates non-existent treatment regimens, and misdiagnoses lymphoma in hematologic oncology — a clinical domain absent from its documented training data.
Summary
This dataset documents failures of Google's MedGemma-4B on hematologic oncology prompts — a clinical subspecialty absent… See the full description on the dataset page: https://huggingface.co/datasets/Mateenah/medgemma-4b-hematologic-oncology-blind-spots.Qwen3-4B_Prompt_Response_Benchmark
🏆 Qwen3-4B Prompt Response Benchmark
This benchmarking dataset contains 10 diverse data points where the Qwen3-4B model makes reasoning and cultural errors in English and Bengali.
🤖 Model: Qwen3-4B
Base model with reasoning capabilities. One of the well known open source Large Language Models.
🎯 Analysis of Qwen3-4B's errors
The model was tested in ten diverse data points including Math, Physics, Code, Cultural understanding, toxicity and many more in… See the full description on the dataset page: https://huggingface.co/datasets/Rabius/Qwen3-4B_Prompt_Response_Benchmark.qwen35_4b_blindspots
Blind Spots of Qwen3.5-4B-Base
10 cases where Qwen/Qwen3.5-4B-Base gets things wrong.
Model
Name: Qwen/Qwen3.5-4B-Base
Type: pretrained base model (not instruction-tuned)
Params: ~4B
Architecture: hybrid Gated DeltaNet + Gated Attention
Released: March 2, 2026
Context: 262k tokens
Languages: 201
The model card says it's intended for "fine-tuning, in-context learning experiments, and other research or development purposes, not direct interaction."
How I… See the full description on the dataset page: https://huggingface.co/datasets/aliraza9/qwen35_4b_blindspots.AIME-2024-Gemma-3-4btest_read_csv_4bc82d26qwen3.5-4b-base-blindspots
Qwen3.5-4B-Base Blind Spot Dataset
Model Tested
Qwen/Qwen3.5-4B-Base
How I Loaded the Model
Platform: Modal (Jupyter Notebook), A10G GPU, 4 vCPUs, 16GB RAM
%uv pip install -q transformers torch accelerate huggingface_hub pillow
%uv pip install git+https://github.com/huggingface/transformers.git
from huggingface_hub import login
login(token="your_hf_token_here")
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
MODEL_ID =… See the full description on the dataset page: https://huggingface.co/datasets/Ni3SinghR/qwen3.5-4b-base-blindspots.test-f3a3c123-9ecd-4ba1-8ac3-3fd8d64863ddcommentsc0c87fb6-bdfd-4b21-9450-8792162bc026-libreoffice_writer-v1c0c87fb6-bdfd-4b21-9450-8792162bc026