Datasets:
Dataset Preview
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The dataset generation failed
Error code: DatasetGenerationError
Exception: ArrowInvalid
Message: Float value 0.500000 was truncated converting to int64
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1816, in _prepare_split_single
for key, table in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2303, in cast_table_to_schema
cast_array_to_feature(
~~~~~~~~~~~~~~~~~~~~~^
table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
feature,
^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1852, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
~~~~^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2143, in cast_array_to_feature
return array_cast(
array,
...<2 lines>...
allow_decimal_to_str=allow_decimal_to_str,
)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1854, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2006, in array_cast
return array.cast(pa_type)
~~~~~~~~~~^^^^^^^^^
File "pyarrow/array.pxi", line 1147, in pyarrow.lib.Array.cast
File "/usr/local/lib/python3.14/site-packages/pyarrow/compute.py", line 412, in cast
return call_function("cast", [arr], options, memory_pool)
File "pyarrow/_compute.pyx", line 604, in pyarrow._compute.call_function
File "pyarrow/_compute.pyx", line 399, in pyarrow._compute.Function.call
result = GetResultValue(
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowInvalid: Float value 0.500000 was truncated converting to int64
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1869, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
format string | schemaVersion int64 | boundary string | scenarioStatus string | request unknown | response unknown | trajectoryId string | agentId string | scenarioId string | batchId null | stepId string | callId string | stepIndex int64 | callIndex int64 | timestamp int64 | purpose string | stepType string | modelType string | provider string | metadata unknown | judgeScore int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
eliza_native_v1 | 1 | vercel_ai_sdk.generateText | passed | {
"messages": [
{
"role": "system",
"content": "user_role: OWNER\n\nprior_dialogue_policy: Prior chat is context only. For current, latest, live, filesystem, runtime, build, deploy, or verification requests, use the current turn's tools/context instead of answering from prior tool results or stale sub... | {
"text": "{\n \"contexts\": [\"general\"],\n \"intents\": [\"update ledger title\"],\n \"replyText\": \"On it.\",\n \"threadOps\": [],\n \"candidateActionNames\": []\n}",
"toolCalls": [
{
"toolName": "HANDLE_RESPONSE",
"input": {
"contexts": [
"general"
],
"in... | tj-d8720fd12c6334 | 546ac3ab-0468-01a2-9d5b-52dfa34bf9cc | deterministic-active-view-agent-surface | null | stage-msghandler-1783046369807 | tj-d8720fd12c6334:stage-msghandler-1783046369807 | 0 | 0 | 1,783,046,369,807 | messageHandler | messageHandler | RESPONSE_HANDLER | default | {
"task_type": "should_respond",
"source_dataset": "scenario_trajectory_boundary",
"trajectory_id": "tj-d8720fd12c6334",
"step_id": "stage-msghandler-1783046369807",
"call_id": "tj-d8720fd12c6334:stage-msghandler-1783046369807",
"agent_id": "546ac3ab-0468-01a2-9d5b-52dfa34bf9cc",
"source_run_id": "aa9f9b5... | null |
eliza_native_v1 | 1 | vercel_ai_sdk.generateText | passed | {
"messages": [
{
"role": "system",
"content": "user_role: OWNER\n\nselected_contexts: general\n\ncontexts:\n- general: Normal conversation and public agent behavior. Use when the reply needs general agent state but no tool work.\n\nprior_dialogue_policy: Prior chat is context only. For current, lates... | {
"text": "{\"success\":true,\"decision\":\"FINISH\",\"thought\":\"The ledger title was successfully updated to 'Close Issue 11355' via the VIEWS tool. No further action needed.\"}"
} | tj-d8720fd12c6334 | 546ac3ab-0468-01a2-9d5b-52dfa34bf9cc | deterministic-active-view-agent-surface | null | stage-eval-iter-1-1783046371764 | tj-d8720fd12c6334:stage-eval-iter-1-1783046371764 | 4 | 0 | 1,783,046,371,764 | evaluation | evaluation | RESPONSE_HANDLER | default | {
"task_type": "evaluation",
"source_dataset": "scenario_trajectory_boundary",
"trajectory_id": "tj-d8720fd12c6334",
"step_id": "stage-eval-iter-1-1783046371764",
"call_id": "tj-d8720fd12c6334:stage-eval-iter-1-1783046371764",
"agent_id": "546ac3ab-0468-01a2-9d5b-52dfa34bf9cc",
"source_run_id": "aa9f9b5f-... | null |
eliza_native_v1 | 1 | vercel_ai_sdk.generateText | passed | {
"messages": [
{
"role": "system",
"content": "user_role: OWNER\n\nprior_dialogue_policy: Prior chat is context only. For current, latest, live, filesystem, runtime, build, deploy, or verification requests, use the current turn's tools/context instead of answering from prior tool results or stale sub... | {
"text": "{\"processMessage\":\"RESPOND\",\"thought\":\"\",\"plan\":{\"contexts\":[\"automation\"],\"reply\":\"On it.\",\"simple\":false,\"requiresTool\":true,\"candidateActions\":[\"CLICK_BUTTON\"]}}",
"toolCalls": [
{
"toolName": "HANDLE_RESPONSE",
"input": {
"contexts": [
"auto... | tj-d87ca46ae29dfc | 546ac3ab-0468-01a2-9d5b-52dfa34bf9cc | deterministic-active-view-agent-surface | null | stage-msghandler-1783046372516 | tj-d87ca46ae29dfc:stage-msghandler-1783046372516 | 0 | 0 | 1,783,046,372,516 | messageHandler | messageHandler | RESPONSE_HANDLER | default | {
"task_type": "should_respond",
"source_dataset": "scenario_trajectory_boundary",
"trajectory_id": "tj-d87ca46ae29dfc",
"step_id": "stage-msghandler-1783046372516",
"call_id": "tj-d87ca46ae29dfc:stage-msghandler-1783046372516",
"agent_id": "546ac3ab-0468-01a2-9d5b-52dfa34bf9cc",
"source_run_id": "aa9f9b5... | null |
eliza_native_v1 | 1 | vercel_ai_sdk.generateText | passed | {
"messages": [
{
"role": "system",
"content": "user_role: OWNER\n\nselected_contexts: automation\n\ncontexts:\n- automation: Automations, workflows, triggers, cron/heartbeat jobs, recurring runs, monitors, reminders that execute later, and proactive follow-up tasks.\n\nprior_dialogue_policy: Prior ch... | {
"text": "",
"toolCalls": [
{
"toolName": "VIEWS",
"input": {
"action": "interact",
"view": "scenario-active-ledger",
"capability": "agent-click",
"params": {
"id": "save-ledger"
}
}
}
],
"finishReason": "tool-calls",
"usage": {
... | tj-d87ca46ae29dfc | 546ac3ab-0468-01a2-9d5b-52dfa34bf9cc | deterministic-active-view-agent-surface | null | stage-planner-iter-1-1783046372982 | tj-d87ca46ae29dfc:stage-planner-iter-1-1783046372982 | 2 | 0 | 1,783,046,372,982 | planner | planner | ACTION_PLANNER | default | {
"task_type": "action_planner",
"source_dataset": "scenario_trajectory_boundary",
"trajectory_id": "tj-d87ca46ae29dfc",
"step_id": "stage-planner-iter-1-1783046372982",
"call_id": "tj-d87ca46ae29dfc:stage-planner-iter-1-1783046372982",
"agent_id": "546ac3ab-0468-01a2-9d5b-52dfa34bf9cc",
"source_run_id": ... | null |
eliza_native_v1 | 1 | vercel_ai_sdk.generateText | passed | {
"messages": [
{
"role": "system",
"content": "user_role: OWNER\n\nselected_contexts: automation\n\ncontexts:\n- automation: Automations, workflows, triggers, cron/heartbeat jobs, recurring runs, monitors, reminders that execute later, and proactive follow-up tasks.\n\nprior_dialogue_policy: Prior ch... | {
"text": "{\"success\":true,\"decision\":\"FINISH\",\"thought\":\"Clicked the save button in the active ledger view using the VIEWS tool, which reported the ledger was saved. The request is fulfilled.\"}"
} | tj-d87ca46ae29dfc | 546ac3ab-0468-01a2-9d5b-52dfa34bf9cc | deterministic-active-view-agent-surface | null | stage-eval-iter-1-1783046373535 | tj-d87ca46ae29dfc:stage-eval-iter-1-1783046373535 | 4 | 0 | 1,783,046,373,535 | evaluation | evaluation | RESPONSE_HANDLER | default | {
"task_type": "evaluation",
"source_dataset": "scenario_trajectory_boundary",
"trajectory_id": "tj-d87ca46ae29dfc",
"step_id": "stage-eval-iter-1-1783046373535",
"call_id": "tj-d87ca46ae29dfc:stage-eval-iter-1-1783046373535",
"agent_id": "546ac3ab-0468-01a2-9d5b-52dfa34bf9cc",
"source_run_id": "aa9f9b5f-... | null |
eliza_native_v1 | 1 | vercel_ai_sdk.generateText | passed | {"messages":[{"role":"system","content":"user_role: OWNER\n\nprior_dialogue_policy: Prior chat is co(...TRUNCATED) | {"text":"{\"processMessage\":\"RESPOND\",\"thought\":\"\",\"plan\":{\"contexts\":[\"code\"],\"reply\(...TRUNCATED) | tj-0633fff0425ffd | 546ac3ab-0468-01a2-9d5b-52dfa34bf9cc | live-background-actions | null | stage-msghandler-1783049368576 | tj-0633fff0425ffd:stage-msghandler-1783049368576 | 0 | 0 | 1,783,049,368,576 | messageHandler | messageHandler | RESPONSE_HANDLER | default | {"task_type":"should_respond","source_dataset":"scenario_trajectory_boundary","trajectory_id":"tj-06(...TRUNCATED) | 1 |
eliza_native_v1 | 1 | vercel_ai_sdk.generateText | passed | {"messages":[{"role":"system","content":"user_role: OWNER\n\nselected_contexts: code\n\ncontexts:\n-(...TRUNCATED) | {"text":"","toolCalls":[{"toolName":"BACKGROUND","input":{"op":"set","color":"teal"}}],"finishReason(...TRUNCATED) | tj-0633fff0425ffd | 546ac3ab-0468-01a2-9d5b-52dfa34bf9cc | live-background-actions | null | stage-planner-iter-1-1783049368964 | tj-0633fff0425ffd:stage-planner-iter-1-1783049368964 | 2 | 0 | 1,783,049,368,964 | planner | planner | ACTION_PLANNER | default | {"task_type":"action_planner","source_dataset":"scenario_trajectory_boundary","trajectory_id":"tj-06(...TRUNCATED) | 1 |
eliza_native_v1 | 1 | vercel_ai_sdk.generateText | passed | {"messages":[{"role":"system","content":"user_role: OWNER\n\nselected_contexts: code\n\ncontexts:\n-(...TRUNCATED) | {"text":"{\"success\":true,\"decision\":\"FINISH\",\"thought\":\"The BACKGROUND tool was invoked to (...TRUNCATED) | tj-0633fff0425ffd | 546ac3ab-0468-01a2-9d5b-52dfa34bf9cc | live-background-actions | null | stage-eval-iter-1-1783049369362 | tj-0633fff0425ffd:stage-eval-iter-1-1783049369362 | 4 | 0 | 1,783,049,369,362 | evaluation | evaluation | RESPONSE_HANDLER | default | {"task_type":"evaluation","source_dataset":"scenario_trajectory_boundary","trajectory_id":"tj-0633ff(...TRUNCATED) | 1 |
eliza_native_v1 | 1 | vercel_ai_sdk.generateText | passed | {"messages":[{"role":"system","content":"user_role: OWNER\n\nselected_contexts: code\n\ncontexts:\n-(...TRUNCATED) | {
"text": "The app background has been set to teal."
} | tj-0633fff0425ffd | 546ac3ab-0468-01a2-9d5b-52dfa34bf9cc | live-background-actions | null | stage-planner-iter-2-1783049369562 | tj-0633fff0425ffd:stage-planner-iter-2-1783049369562 | 5 | 0 | 1,783,049,369,562 | planner | planner | ACTION_PLANNER | default | {"task_type":"action_planner","source_dataset":"scenario_trajectory_boundary","trajectory_id":"tj-06(...TRUNCATED) | 1 |
eliza_native_v1 | 1 | vercel_ai_sdk.generateText | passed | {"messages":[{"role":"system","content":"user_role: OWNER\n\nselected_contexts: code\n\ncontexts:\n-(...TRUNCATED) | {"text":"{\"success\":false,\"decision\":\"CONTINUE\",\"thought\":\"The BACKGROUND tool was invoked (...TRUNCATED) | tj-0633fff0425ffd | 546ac3ab-0468-01a2-9d5b-52dfa34bf9cc | live-background-actions | null | stage-eval-iter-1-1783049369880 | tj-0633fff0425ffd:stage-eval-iter-1-1783049369880 | 6 | 0 | 1,783,049,369,880 | evaluation | evaluation | RESPONSE_HANDLER | default | {"task_type":"evaluation","source_dataset":"scenario_trajectory_boundary","trajectory_id":"tj-0633ff(...TRUNCATED) | 1 |
End of preview.
YAML Metadata Warning:The task_categories "conversational" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
elizaos/eliza-1-training-data
Training corpus for the eliza-1 model line. All records are in eliza_native_v1 format — the canonical training schema for elizaOS agents.
Format
Every record is a JSON object with this shape:
{
"format": "eliza_native_v1",
"boundary": "vercel_ai_sdk.generateText",
"request": {
"system": "...",
"messages": [...],
"tools": {...},
"settings": {}
},
"response": {
"text": "...",
"finishReason": "stop",
"toolCalls": []
}
}
Splits
| Split | File | Records |
|---|---|---|
| Train | converted/merged/train.jsonl |
6,570 |
| Val | converted/merged/val.jsonl |
365 |
| Test | converted/merged/test.jsonl |
365 |
Sources
| Source | Records | Task |
|---|---|---|
| NousResearch/hermes-function-calling-v1 | 1,892 | Function calling |
| glaiveai/glaive-function-calling-v2 | 18 | Function calling |
| awax1122/openclaw-opencode-dataset | 4,250 | Coding agent tasks |
| elizaOS runtime trajectories | 1,140 | Native agent tasks |
Quality
- All records validated against
eliza_native_v1schema - Trope filtering: 0% trope rate (Certainly!, As an AI, etc. removed)
- System prompts normalized to Eliza form
- Shuffled and split 90/5/5
Training
Use with packages/training/scripts/train_local.py using the APOLLO optimizer. See FINETUNING_PIPELINE.md for full pipeline docs.
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