datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
lm-eval-results-Kukedlc-Ramakrishna-7b-v3-private
Dataset Card for Evaluation run of Kukedlc/Ramakrishna-7b-v3
Dataset automatically created during the evaluation run of model Kukedlc/Ramakrishna-7b-v3
The dataset is composed of 62 configuration(s), each one corresponding to one of the evaluated task.
The dataset has been created from 4 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An… See the full description on the dataset page: https://huggingface.co/datasets/nyu-dice-lab/lm-eval-results-Kukedlc-Ramakrishna-7b-v3-private.agentconflictbench
AgentConflictBench
AgentConflictBench is a research benchmark for evaluating silent semantic
conflicts among independently valid AI-generated code changes.
Most coding-agent benchmarks ask whether an agent can solve one task in
isolation. AgentConflictBench asks whether two independently valid patches
still work when composed.
Dataset Summary
Instances: 28
Positive silent semantic conflicts: 25
Clean-composition controls: 3
Upstream repositories: 7
Languages:… See the full description on the dataset page: https://huggingface.co/datasets/ramachandra1996/agentconflictbench.Wiki_Faiss_Indexes
dataset_info:
features:
- name: text
dtype: string
- name: embeddings
dtype: float32
shape: [384]
configs:
- config_name: default
data_files: "*.parquet"
Wikipedia IVF-OPQ-PQ Vector Database (GPU-Optimized)
A high-performance, GPU-accelerated FAISS vector database built from Wikipedia articles with pre-computed embeddings. This dataset contains approximately 35 million Wikipedia articles with 384-dimensional embeddings using the all-MiniLM-L6-v2… See the full description on the dataset page: https://huggingface.co/datasets/Ram-G/Wiki_Faiss_Indexes.code.evol.instruct.wiz.oss_python.jsonaligntune-testrun-alignment-auditdata-oss_instruct-decontaminated_python.jsonlfarmer-price-data-2026-jan-aprramanv-image-preferenceramanv-aesthetic-scoresmath500-bon-prm-replication
Best-of-N Weighted Baseline with PRM — Replicating DeepMind's Test-Time Compute Scaling
Replication of the Best-of-N Weighted baseline from:
"Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters"
(Snell, Lee, Xu, Kumar — 2024) — arxiv:2408.03314
Paper Summary
The paper studies how to optimally scale inference-time computation in LLMs. The key finding: using a compute-optimal test-time strategy can improve efficiency by 4× compared… See the full description on the dataset page: https://huggingface.co/datasets/ramu3405/math500-bon-prm-replication.atompack-upload-smoke