datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
train_Qwen_Qwen2_5_32B_Instruct_inferencetrain_Qwen_Qwen2_5_7B_Instruct_inferencegaia_127_Qwen2_5_Coder_32B_Instruct_20260430_044424aime_historical_qwen2_5_7b_instruct_rewritegaia_127_Qwen2_5_Coder_32B_Instruct_20260425_083514m194k_response_qwen2_5_32b_instruct_gradedcomputers-says-no-kurtis-e1-1-qwen2-5-3b-instructmmlu-hsPhysics-qwen-qwen2-5-7b-instructaider_polyglot_Qwen2_5_Coder_32B_Instruct_20260430_044311-tracesterminal_bench_2_Qwen2_5_Coder_32B_Instruct_20260315_175308bfcl_parity_Qwen2_5_Coder_32B_Instruct_20260430_044332financeagent_terminal_Qwen2_5_Coder_32B_Instruct_20260504_163908terminal_bench_2_Qwen2_5_Coder_32B_Instruct_20260315_175308-546871d8eval-qwen2_5-coder-7b-instructswebench_verified_Qwen2_5_Coder_32B_Instruct_20260427_232252-traceseval-qwen2_5-math-7b-instructterminal_bench_2_Qwen2_5_Coder_32B_Instruct_20260424_011846terminal_bench_2_Qwen2_5_Coder_32B_Instruct_20260426_005704cemig_caseregulatorio_100semfilt_4erradas_enem_eval_qwen2_5_7b_instructdev_set_v2_Qwen2_5_Coder_32B_Instruct_20260426_005641-tracesdev_set_v2_Qwen2_5_Coder_32B_Instruct_20260424_011817filtered_superior_5000_qwen2_5_3b_instruct_rewritem194k_graded_r1_correct_qwen2_5_xb_instruct_wrongMMLU_LLM_judge_fewshot_Qwen_Qwen2_5-7B-Instruct-Turboconnection_queries_jan12_natural_iterate1_1_None_0.7_4096_Qwen2_5-32B-Instruct
Dataset: connections-dev/connection_queries_jan12_natural_original_1_None_0.7_4096_qwen25-32b
This dataset was generated using the inference script with the following configuration:
Inference Parameters
Model Configuration
Model Name: Qwen/Qwen2.5-32B-Instruct
Server URL: http://localhost:9000
API Key: Not provided
Request Timeout: 30 seconds
Query Configuration
Query Type: natural
Query Column: query
Sampling Type: iterate1
Generation… See the full description on the dataset page: https://huggingface.co/datasets/connections-dev/connection_queries_jan12_natural_iterate1_1_None_0.7_4096_Qwen2_5-32B-Instruct.dev_set_v2_Qwen2_5_Coder_32B_Instruct_20260317_045728openr1-math-220k-hard-qwen2-5-7b-instruct-1k-with-successful-trajmedagentbench_Qwen2_5_Coder_32B_Instruct_20260425_083459AgentTraj-L-latent-states-Qwen2-5-0-5B-InstructSee build_data.py for swapping models and build your own.
Example usage:
import numpy as np
import torch
from datasets import load_dataset
import faiss
def load_latent_states(model_name="Qwen2-5-0-5B-Instruct", cache_dir="./cache"):
repo_name = f"BarryFutureman/AgentTraj-L-latent-states-{model_name}"
dataset = load_dataset(repo_name, split="train", cache_dir=cache_dir)
return dataset
def build_index_from_dataset(dataset):
vectors = np.array(dataset["latent_vector"]… See the full description on the dataset page: https://huggingface.co/datasets/BarryFutureman/AgentTraj-L-latent-states-Qwen2-5-0-5B-Instruct.swebench_verified_random_100_folders_Qwen2_5_Coder_32B_Instruct_20260424_011832