datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Open-SWE-Traces
Open-SWE-Traces: Advancing Distillation for Software Engineering Agents
🚨 What's New
[09/26] Release v1.2: Added new agent trajectories generated by Qwen3.8-27B for
mini-swe-agent. Trajectories for OpenCode and Claude Code harnesses will be released soon.
[08/26] Release v1.1: Added new agent trajectories generated by DeepSeek-V4-Flash and
Qwen3.6-27B across OpenHands,
SWE-agent, and mini-swe-agent harnesses.
[06/21] Release v1.0: Released 207k agent… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Open-SWE-Traces.funes-nvidia-Open-SWE-Traces
Funes recall store — NVIDIA Open-SWE-Traces (resolved)
A funes recall store built by indexing the
resolved==1 trajectories of
nvidia/Open-SWE-Traces
(65244 sessions, across both harnesses — SWE-agent and OpenHands — and both models,
Minimax-M2.5 and Qwen3.5-122B).
What this is
This is not a raw trace dataset — it is a pre-built funes index: the source
trajectories chunked into content blocks and embedded, stored as a
Lance table (chunks.lance).
Source… See the full description on the dataset page: https://huggingface.co/datasets/dacorvo/funes-nvidia-Open-SWE-Traces.opentraces-capsulesOpenTracestracesglm52-datagen-r11-106-instruction-following-citation-tracesllm-verifier-freelancer-qwen3.5-122b-131k-opencode-traces
Agent trace dataset
Decoding the literal token IDs
The prompt_token_ids / completion_token_ids / logprobs columns are the
verbatim tokens the serving engine emitted, stored PER AGENT STEP as a
list-of-lists (one inner list per turn). To turn them back into text you MUST
use the exact tokenizer the model was served with — a generic same-family
tokenizer will decode word tokens to garbage.
Served model / tokenizer source: Qwen/Qwen3.5-122B-A10B-FP8
from transformers… See the full description on the dataset page: https://huggingface.co/datasets/open-athena/llm-verifier-freelancer-qwen3.5-122b-131k-opencode-traces.glm52-datagen-r11-100-agentic-function-calling-pivot-v2-tracesnemotron-gym-instruction-following-structured-qwen3.5-122b-131k-opencode-traces
Agent trace dataset
Decoding the literal token IDs
The prompt_token_ids / completion_token_ids / logprobs columns are the
verbatim tokens the serving engine emitted, stored PER AGENT STEP as a
list-of-lists (one inner list per turn). To turn them back into text you MUST
use the exact tokenizer the model was served with — a generic same-family
tokenizer will decode word tokens to garbage.
Served model / tokenizer source: Qwen/Qwen3.5-122B-A10B-FP8
from transformers… See the full description on the dataset page: https://huggingface.co/datasets/open-athena/nemotron-gym-instruction-following-structured-qwen3.5-122b-131k-opencode-traces.glm52-datagen-r11-safety-tracesglm52-datagen-r11-16-nemotron-instruction-tracesnemotron-gym-if-v2-qwen3.5-122b-32k-tracesnemotron-math-oracle-filtered-qwen3.5-122b-32k-tracesOpen-SWE-Traces
Open-SWE-Traces: Advancing Distillation for Software Engineering Agents
Data Overview
Open-SWE-Traces is an agentic instruction tuning dataset designed to advance the capabilities of LLMs in software engineering. This dataset comprises 200k+ agent
trajectories collected using the SWE-agent and OpenHands framework. The trajectories
were synthesized using Minimax-M2.5 (with thinking) and Qwen3.5-122B-A10B
(without thinking) and specifically curated for supervised… See the full description on the dataset page: https://huggingface.co/datasets/kshitijthakkar/Open-SWE-Traces.stackexchange-tezos-sandboxes-verified-qwen3.5-122b-131k-opencode-literal-rescue-traces
Agent trace dataset
Decoding the literal token IDs
The prompt_token_ids / completion_token_ids / logprobs columns are the
verbatim tokens the serving engine emitted, stored PER AGENT STEP as a
list-of-lists (one inner list per turn). To turn them back into text you MUST
use the exact tokenizer the model was served with — a generic same-family
tokenizer will decode word tokens to garbage.
Served model / tokenizer source: Qwen/Qwen3.5-122B-A10B-FP8
from transformers… See the full description on the dataset page: https://huggingface.co/datasets/open-athena/stackexchange-tezos-sandboxes-verified-qwen3.5-122b-131k-opencode-literal-rescue-traces.nemotron-gym-identity-following-v2-qwen3.5-122b-131k-opencode-traces
Agent trace dataset
Decoding the literal token IDs
The prompt_token_ids / completion_token_ids / logprobs columns are the
verbatim tokens the serving engine emitted, stored PER AGENT STEP as a
list-of-lists (one inner list per turn). To turn them back into text you MUST
use the exact tokenizer the model was served with — a generic same-family
tokenizer will decode word tokens to garbage.
Served model / tokenizer source: Qwen/Qwen3.5-122B-A10B-FP8
from transformers… See the full description on the dataset page: https://huggingface.co/datasets/open-athena/nemotron-gym-identity-following-v2-qwen3.5-122b-131k-opencode-traces.stackexchange-overflow-sandboxes-verified-qwen3.5-122b-131k-opencode-literal-rescue-traces
Agent trace dataset
Decoding the literal token IDs
The prompt_token_ids / completion_token_ids / logprobs columns are the
verbatim tokens the serving engine emitted, stored PER AGENT STEP as a
list-of-lists (one inner list per turn). To turn them back into text you MUST
use the exact tokenizer the model was served with — a generic same-family
tokenizer will decode word tokens to garbage.
Served model / tokenizer source: Qwen/Qwen3.5-122B-A10B-FP8
from transformers… See the full description on the dataset page: https://huggingface.co/datasets/open-athena/stackexchange-overflow-sandboxes-verified-qwen3.5-122b-131k-opencode-literal-rescue-traces.exp_rpt_pymethods2test-large-qwen3.5-122b-131k-opencode-traces
Agent trace dataset
Decoding the literal token IDs
The prompt_token_ids / completion_token_ids / logprobs columns are the
verbatim tokens the serving engine emitted, stored PER AGENT STEP as a
list-of-lists (one inner list per turn). To turn them back into text you MUST
use the exact tokenizer the model was served with — a generic same-family
tokenizer will decode word tokens to garbage.
Served model / tokenizer source: Qwen/Qwen3.5-122B-A10B-FP8
from transformers… See the full description on the dataset page: https://huggingface.co/datasets/open-athena/exp_rpt_pymethods2test-large-qwen3.5-122b-131k-opencode-traces.WorldTrace
WorldTrace Dataset
🗺️ Overview
WorldTrace is a large-scale, high-quality, globally covering GPS trajectory dataset.
Trajectory data provides an important data source for understanding human mobility patterns and transforming urban intelligence. However, existing trajectory modeling methods have limitations in terms of task specificity, regional dependency, and data sensitivity. The construction of the WorldTrace dataset aims to address these challenges by… See the full description on the dataset page: https://huggingface.co/datasets/OpenTrace/WorldTrace.glm52-datagen-r11-02-codecontests-tracesnemotron-gym-instruction-following-structured-minimax-m27-131k-tracesglm52-datagen-r11-42-ghactions-retry-r2-tracesnemotron-gym-identity-following-v2-qwen3.5-122b-32k-tracesstackexchange-superuser-sandboxes-verified-qwen3.5-122b-131k-opencode-literal-rescue-traces
Agent trace dataset
Decoding the literal token IDs
The prompt_token_ids / completion_token_ids / logprobs columns are the
verbatim tokens the serving engine emitted, stored PER AGENT STEP as a
list-of-lists (one inner list per turn). To turn them back into text you MUST
use the exact tokenizer the model was served with — a generic same-family
tokenizer will decode word tokens to garbage.
Served model / tokenizer source: Qwen/Qwen3.5-122B-A10B-FP8
from transformers… See the full description on the dataset page: https://huggingface.co/datasets/open-athena/stackexchange-superuser-sandboxes-verified-qwen3.5-122b-131k-opencode-literal-rescue-traces.nemotron-gym-agent-calendar-qwen3.5-122b-131k-opencode-traces
Agent trace dataset
Decoding the literal token IDs
The prompt_token_ids / completion_token_ids / logprobs columns are the
verbatim tokens the serving engine emitted, stored PER AGENT STEP as a
list-of-lists (one inner list per turn). To turn them back into text you MUST
use the exact tokenizer the model was served with — a generic same-family
tokenizer will decode word tokens to garbage.
Served model / tokenizer source: Qwen/Qwen3.5-122B-A10B-FP8
from transformers… See the full description on the dataset page: https://huggingface.co/datasets/open-athena/nemotron-gym-agent-calendar-qwen3.5-122b-131k-opencode-traces.nemotron-gym-competitive-coding-qwen3.5-122b-131k-opencode-traces
Agent trace dataset
Decoding the literal token IDs
The prompt_token_ids / completion_token_ids / logprobs columns are the
verbatim tokens the serving engine emitted, stored PER AGENT STEP as a
list-of-lists (one inner list per turn). To turn them back into text you MUST
use the exact tokenizer the model was served with — a generic same-family
tokenizer will decode word tokens to garbage.
Served model / tokenizer source: Qwen/Qwen3.5-122B-A10B-FP8
from transformers… See the full description on the dataset page: https://huggingface.co/datasets/open-athena/nemotron-gym-competitive-coding-qwen3.5-122b-131k-opencode-traces.exp_rle_adversarial-qwen3.5-122b-131k-opencode-traces
Agent trace dataset
Decoding the literal token IDs
The prompt_token_ids / completion_token_ids / logprobs columns are the
verbatim tokens the serving engine emitted, stored PER AGENT STEP as a
list-of-lists (one inner list per turn). To turn them back into text you MUST
use the exact tokenizer the model was served with — a generic same-family
tokenizer will decode word tokens to garbage.
Served model / tokenizer source: Qwen/Qwen3.5-122B-A10B-FP8
from transformers… See the full description on the dataset page: https://huggingface.co/datasets/open-athena/exp_rle_adversarial-qwen3.5-122b-131k-opencode-traces.exp_rpt_pr-qwen3.5-122b-131k-opencode-traces
Agent trace dataset
Decoding the literal token IDs
The prompt_token_ids / completion_token_ids / logprobs columns are the
verbatim tokens the serving engine emitted, stored PER AGENT STEP as a
list-of-lists (one inner list per turn). To turn them back into text you MUST
use the exact tokenizer the model was served with — a generic same-family
tokenizer will decode word tokens to garbage.
Served model / tokenizer source: Qwen/Qwen3.5-122B-A10B-FP8
from transformers… See the full description on the dataset page: https://huggingface.co/datasets/open-athena/exp_rpt_pr-qwen3.5-122b-131k-opencode-traces.selfinstruct-naive-sandboxes-2-verified-qwen3.5-122b-131k-opencode-tracesglm52-datagen-r11-15-ghactions-traces