Datasets:

Kai9987kai
/

supermix-conversation-datasets

Tasks:

Text Generation

Languages:

English

Size:

100K<n<1M

License:

Dataset card Data Studio Files Files and versions

xet

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The full dataset viewer is not available (click to read why). Only showing a preview of the rows.

The dataset generation failed

Error code:   DatasetGenerationError
Exception:    CastError
Message:      Couldn't cast
user: string
assistant: string
category: string
topic: string
to
{'user': Value('string'), 'assistant': Value('string'), 'topic': Value('string')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1872, in _prepare_split_single
                  for key, table in generator:
                                    ^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 265, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 120, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              user: string
              assistant: string
              category: string
              topic: string
              to
              {'user': Value('string'), 'assistant': Value('string'), 'topic': Value('string')}
              because column names don't match
              
              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 1347, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 980, in convert_to_parquet
                  builder.download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 884, in download_and_prepare
                  self._download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 947, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1739, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1922, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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user string	assistant string	topic string
How do I handle python module not found?	Recommended approach: Activate the correct virtual environment, run `python -m pip install <package>`, and verify `which python` and `python -m pip --version` point to the same environment.	coding_knowledge
Best practice for python module not found?	Do this: Activate the correct virtual environment, run `python -m pip install <package>`, and verify `which python` and `python -m pip --version` point to the same environment.	coding_knowledge
Quick guidance on python module not found please.	Do this: Activate the correct virtual environment, run `python -m pip install <package>`, and verify `which python` and `python -m pip --version` point to the same environment.	coding_knowledge
Can you explain python module not found in a practical way?	Do this: Activate the correct virtual environment, run `python -m pip install <package>`, and verify `which python` and `python -m pip --version` point to the same environment.	coding_knowledge
Quick guidance on python import cycles please.	Practical answer: Break circular imports by moving shared logic to a third module or importing lazily inside functions.	coding_knowledge
I keep hitting issues with python import cycles. What should I do?	Practical answer: Break circular imports by moving shared logic to a third module or importing lazily inside functions.	coding_knowledge
Can you explain python import cycles in a practical way?	Do this: Break circular imports by moving shared logic to a third module or importing lazily inside functions.	coding_knowledge
How do I handle python import cycles?	Do this: Break circular imports by moving shared logic to a third module or importing lazily inside functions.	coding_knowledge
Can you explain python async performance in a practical way?	Short guide: Use `asyncio` for IO-bound concurrency, avoid blocking calls inside coroutines, and wrap blocking work with executors.	coding_knowledge
How do I handle python async performance?	Short guide: Use `asyncio` for IO-bound concurrency, avoid blocking calls inside coroutines, and wrap blocking work with executors.	coding_knowledge
Best practice for python async performance?	Do this: Use `asyncio` for IO-bound concurrency, avoid blocking calls inside coroutines, and wrap blocking work with executors.	coding_knowledge
I keep hitting issues with python async performance. What should I do?	Recommended approach: Use `asyncio` for IO-bound concurrency, avoid blocking calls inside coroutines, and wrap blocking work with executors.	coding_knowledge
I keep hitting issues with python memory leak debugging. What should I do?	Recommended approach: Use `tracemalloc`, inspect object growth snapshots, and check long-lived global caches and reference cycles.	coding_knowledge
How do I handle python memory leak debugging?	Practical answer: Use `tracemalloc`, inspect object growth snapshots, and check long-lived global caches and reference cycles.	coding_knowledge
Can you explain python memory leak debugging in a practical way?	Do this: Use `tracemalloc`, inspect object growth snapshots, and check long-lived global caches and reference cycles.	coding_knowledge
Quick guidance on python memory leak debugging please.	Do this: Use `tracemalloc`, inspect object growth snapshots, and check long-lived global caches and reference cycles.	coding_knowledge
Quick guidance on python typing usage please.	Recommended approach: Add type hints to public APIs first, run a static checker, and gradually tighten strictness per module.	coding_knowledge
Can you explain python typing usage in a practical way?	Short guide: Add type hints to public APIs first, run a static checker, and gradually tighten strictness per module.	coding_knowledge
Best practice for python typing usage?	Do this: Add type hints to public APIs first, run a static checker, and gradually tighten strictness per module.	coding_knowledge
How do I handle python typing usage?	Do this: Add type hints to public APIs first, run a static checker, and gradually tighten strictness per module.	coding_knowledge
Can you explain python dataclass patterns in a practical way?	Recommended approach: Use `@dataclass(frozen=True)` for immutable records and `default_factory` for mutable defaults.	coding_knowledge
I keep hitting issues with python dataclass patterns. What should I do?	Short guide: Use `@dataclass(frozen=True)` for immutable records and `default_factory` for mutable defaults.	coding_knowledge
How do I handle python dataclass patterns?	Practical answer: Use `@dataclass(frozen=True)` for immutable records and `default_factory` for mutable defaults.	coding_knowledge
Best practice for python dataclass patterns?	Recommended approach: Use `@dataclass(frozen=True)` for immutable records and `default_factory` for mutable defaults.	coding_knowledge
Quick guidance on python logging best practices please.	Practical answer: Use structured logs, include request IDs, and avoid logging secrets or raw credentials.	coding_knowledge
How do I handle python logging best practices?	Short guide: Use structured logs, include request IDs, and avoid logging secrets or raw credentials.	coding_knowledge
Best practice for python logging best practices?	Do this: Use structured logs, include request IDs, and avoid logging secrets or raw credentials.	coding_knowledge
Can you explain python logging best practices in a practical way?	Short guide: Use structured logs, include request IDs, and avoid logging secrets or raw credentials.	coding_knowledge
Best practice for python packaging?	Short guide: Prefer `pyproject.toml`, pin runtime dependencies, and build reproducible lock files for deploys.	coding_knowledge
How do I handle python packaging?	Recommended approach: Prefer `pyproject.toml`, pin runtime dependencies, and build reproducible lock files for deploys.	coding_knowledge
Can you explain python packaging in a practical way?	Short guide: Prefer `pyproject.toml`, pin runtime dependencies, and build reproducible lock files for deploys.	coding_knowledge
Quick guidance on python packaging please.	Short guide: Prefer `pyproject.toml`, pin runtime dependencies, and build reproducible lock files for deploys.	coding_knowledge
Quick guidance on javascript promise error handling please.	Recommended approach: Always return or await promises and centralize error handling with `try/catch` around awaited boundaries.	coding_knowledge
I keep hitting issues with javascript promise error handling. What should I do?	Do this: Always return or await promises and centralize error handling with `try/catch` around awaited boundaries.	coding_knowledge
Best practice for javascript promise error handling?	Do this: Always return or await promises and centralize error handling with `try/catch` around awaited boundaries.	coding_knowledge
Can you explain javascript promise error handling in a practical way?	Practical answer: Always return or await promises and centralize error handling with `try/catch` around awaited boundaries.	coding_knowledge
How do I handle javascript event loop basics?	Recommended approach: Microtasks run before macrotasks; promise callbacks may execute before timers scheduled in the same tick.	coding_knowledge
Can you explain javascript event loop basics in a practical way?	Practical answer: Microtasks run before macrotasks; promise callbacks may execute before timers scheduled in the same tick.	coding_knowledge
I keep hitting issues with javascript event loop basics. What should I do?	Recommended approach: Microtasks run before macrotasks; promise callbacks may execute before timers scheduled in the same tick.	coding_knowledge
Best practice for javascript event loop basics?	Recommended approach: Microtasks run before macrotasks; promise callbacks may execute before timers scheduled in the same tick.	coding_knowledge
Quick guidance on typescript strict mode please.	Practical answer: Enable `strict`, fix `any` hotspots, and model edge cases with union types plus exhaustive narrowing.	coding_knowledge
Can you explain typescript strict mode in a practical way?	Recommended approach: Enable `strict`, fix `any` hotspots, and model edge cases with union types plus exhaustive narrowing.	coding_knowledge
How do I handle typescript strict mode?	Short guide: Enable `strict`, fix `any` hotspots, and model edge cases with union types plus exhaustive narrowing.	coding_knowledge
I keep hitting issues with typescript strict mode. What should I do?	Recommended approach: Enable `strict`, fix `any` hotspots, and model edge cases with union types plus exhaustive narrowing.	coding_knowledge
I keep hitting issues with typescript generics. What should I do?	Short guide: Use generics to preserve input-output type relationships instead of weakening APIs with broad types.	coding_knowledge
Can you explain typescript generics in a practical way?	Short guide: Use generics to preserve input-output type relationships instead of weakening APIs with broad types.	coding_knowledge
Quick guidance on typescript generics please.	Practical answer: Use generics to preserve input-output type relationships instead of weakening APIs with broad types.	coding_knowledge
Best practice for typescript generics?	Practical answer: Use generics to preserve input-output type relationships instead of weakening APIs with broad types.	coding_knowledge
Best practice for react rendering performance?	Short guide: Memoize expensive calculations, keep component props stable, and avoid unnecessary parent re-renders.	coding_knowledge
Quick guidance on react rendering performance please.	Do this: Memoize expensive calculations, keep component props stable, and avoid unnecessary parent re-renders.	coding_knowledge
Can you explain react rendering performance in a practical way?	Do this: Memoize expensive calculations, keep component props stable, and avoid unnecessary parent re-renders.	coding_knowledge
I keep hitting issues with react rendering performance. What should I do?	Short guide: Memoize expensive calculations, keep component props stable, and avoid unnecessary parent re-renders.	coding_knowledge
How do I handle react state bugs?	Practical answer: Never mutate state directly; use immutable updates and functional `setState` patterns for derived changes.	coding_knowledge
Quick guidance on react state bugs please.	Short guide: Never mutate state directly; use immutable updates and functional `setState` patterns for derived changes.	coding_knowledge
Can you explain react state bugs in a practical way?	Do this: Never mutate state directly; use immutable updates and functional `setState` patterns for derived changes.	coding_knowledge
Best practice for react state bugs?	Recommended approach: Never mutate state directly; use immutable updates and functional `setState` patterns for derived changes.	coding_knowledge
Quick guidance on react effect dependencies please.	Practical answer: Declare all external dependencies in `useEffect` and move unstable callbacks behind `useCallback` when needed.	coding_knowledge
I keep hitting issues with react effect dependencies. What should I do?	Practical answer: Declare all external dependencies in `useEffect` and move unstable callbacks behind `useCallback` when needed.	coding_knowledge
Can you explain react effect dependencies in a practical way?	Recommended approach: Declare all external dependencies in `useEffect` and move unstable callbacks behind `useCallback` when needed.	coding_knowledge
Best practice for react effect dependencies?	Practical answer: Declare all external dependencies in `useEffect` and move unstable callbacks behind `useCallback` when needed.	coding_knowledge
I keep hitting issues with node memory tuning. What should I do?	Practical answer: Profile heap usage, stream large payloads, and cap concurrency to prevent burst-driven memory spikes.	coding_knowledge
How do I handle node memory tuning?	Practical answer: Profile heap usage, stream large payloads, and cap concurrency to prevent burst-driven memory spikes.	coding_knowledge
Quick guidance on node memory tuning please.	Practical answer: Profile heap usage, stream large payloads, and cap concurrency to prevent burst-driven memory spikes.	coding_knowledge
Can you explain node memory tuning in a practical way?	Short guide: Profile heap usage, stream large payloads, and cap concurrency to prevent burst-driven memory spikes.	coding_knowledge
Can you explain node api timeout strategy in a practical way?	Short guide: Set connect/read timeouts, implement retries with backoff on safe operations, and enforce overall request deadlines.	coding_knowledge
How do I handle node api timeout strategy?	Recommended approach: Set connect/read timeouts, implement retries with backoff on safe operations, and enforce overall request deadlines.	coding_knowledge
Quick guidance on node api timeout strategy please.	Do this: Set connect/read timeouts, implement retries with backoff on safe operations, and enforce overall request deadlines.	coding_knowledge
I keep hitting issues with node api timeout strategy. What should I do?	Do this: Set connect/read timeouts, implement retries with backoff on safe operations, and enforce overall request deadlines.	coding_knowledge
Best practice for git rebase workflow?	Do this: Rebase local feature commits onto updated main, resolve conflicts carefully, then push with `--force-with-lease`.	coding_knowledge
Can you explain git rebase workflow in a practical way?	Recommended approach: Rebase local feature commits onto updated main, resolve conflicts carefully, then push with `--force-with-lease`.	coding_knowledge
I keep hitting issues with git rebase workflow. What should I do?	Do this: Rebase local feature commits onto updated main, resolve conflicts carefully, then push with `--force-with-lease`.	coding_knowledge
How do I handle git rebase workflow?	Recommended approach: Rebase local feature commits onto updated main, resolve conflicts carefully, then push with `--force-with-lease`.	coding_knowledge
Quick guidance on git cherry-pick usage please.	Do this: Cherry-pick focused commits when backporting fixes and reference original commit hashes for traceability.	coding_knowledge
Can you explain git cherry-pick usage in a practical way?	Practical answer: Cherry-pick focused commits when backporting fixes and reference original commit hashes for traceability.	coding_knowledge
Best practice for git cherry-pick usage?	Practical answer: Cherry-pick focused commits when backporting fixes and reference original commit hashes for traceability.	coding_knowledge
How do I handle git cherry-pick usage?	Do this: Cherry-pick focused commits when backporting fixes and reference original commit hashes for traceability.	coding_knowledge
Can you explain git conflict resolution in a practical way?	Recommended approach: Resolve conflicts file-by-file, run tests immediately, and prefer small rebases to reduce merge complexity.	coding_knowledge
I keep hitting issues with git conflict resolution. What should I do?	Do this: Resolve conflicts file-by-file, run tests immediately, and prefer small rebases to reduce merge complexity.	coding_knowledge
Quick guidance on git conflict resolution please.	Recommended approach: Resolve conflicts file-by-file, run tests immediately, and prefer small rebases to reduce merge complexity.	coding_knowledge
Best practice for git conflict resolution?	Short guide: Resolve conflicts file-by-file, run tests immediately, and prefer small rebases to reduce merge complexity.	coding_knowledge
Quick guidance on sql indexing please.	Do this: Index columns used in filters and joins, but avoid over-indexing high-write tables due to update overhead.	coding_knowledge
Can you explain sql indexing in a practical way?	Recommended approach: Index columns used in filters and joins, but avoid over-indexing high-write tables due to update overhead.	coding_knowledge
Best practice for sql indexing?	Practical answer: Index columns used in filters and joins, but avoid over-indexing high-write tables due to update overhead.	coding_knowledge
I keep hitting issues with sql indexing. What should I do?	Do this: Index columns used in filters and joins, but avoid over-indexing high-write tables due to update overhead.	coding_knowledge
How do I handle sql query optimization?	Practical answer: Inspect execution plans, reduce row scans, and push predicates close to data sources.	coding_knowledge
Quick guidance on sql query optimization please.	Practical answer: Inspect execution plans, reduce row scans, and push predicates close to data sources.	coding_knowledge
I keep hitting issues with sql query optimization. What should I do?	Short guide: Inspect execution plans, reduce row scans, and push predicates close to data sources.	coding_knowledge
Best practice for sql query optimization?	Practical answer: Inspect execution plans, reduce row scans, and push predicates close to data sources.	coding_knowledge
Quick guidance on sql transaction isolation please.	Do this: Pick isolation based on anomaly tolerance; stronger levels reduce anomalies but can lower concurrency.	coding_knowledge
How do I handle sql transaction isolation?	Short guide: Pick isolation based on anomaly tolerance; stronger levels reduce anomalies but can lower concurrency.	coding_knowledge
I keep hitting issues with sql transaction isolation. What should I do?	Practical answer: Pick isolation based on anomaly tolerance; stronger levels reduce anomalies but can lower concurrency.	coding_knowledge
Can you explain sql transaction isolation in a practical way?	Do this: Pick isolation based on anomaly tolerance; stronger levels reduce anomalies but can lower concurrency.	coding_knowledge
I keep hitting issues with postgres vacuum. What should I do?	Recommended approach: Tune autovacuum and monitor table bloat to keep index and query performance stable over time.	coding_knowledge
Quick guidance on postgres vacuum please.	Do this: Tune autovacuum and monitor table bloat to keep index and query performance stable over time.	coding_knowledge
Can you explain postgres vacuum in a practical way?	Practical answer: Tune autovacuum and monitor table bloat to keep index and query performance stable over time.	coding_knowledge
Best practice for postgres vacuum?	Short guide: Tune autovacuum and monitor table bloat to keep index and query performance stable over time.	coding_knowledge
I keep hitting issues with mysql slow queries. What should I do?	Recommended approach: Enable slow query logs, rank by total latency, and optimize highest cumulative-cost queries first.	coding_knowledge
Can you explain mysql slow queries in a practical way?	Short guide: Enable slow query logs, rank by total latency, and optimize highest cumulative-cost queries first.	coding_knowledge
How do I handle mysql slow queries?	Recommended approach: Enable slow query logs, rank by total latency, and optimize highest cumulative-cost queries first.	coding_knowledge
Best practice for mysql slow queries?	Recommended approach: Enable slow query logs, rank by total latency, and optimize highest cumulative-cost queries first.	coding_knowledge

End of preview.

Supermix Conversation Datasets

This dataset repo packages the share-ready conversation-training files that were prepared in the Supermix workspace for portable training runs.

Files

conversation_data.coding_knowledge_2026_02_19.jsonl
conversation_data.mega_creative_250k_v2.jsonl
conversation_data.mega_reasoning_creative_v25_75582.jsonl
conversation_data.quality_anchor_v2.jsonl
conversation_data.supermix_plus_v27_500k.jsonl
conversation_data.world_events_2026_02_19.jsonl

Format

Each file is JSONL. Rows are conversation/training examples intended for Supermix training pipelines.

Notes

This repo is the curated public bundle from output/kaggle-upload/supermix-datasets.
It does not include every intermediate or experimental dataset file from the local workspace.
Check each file's provenance and intended use before mixing it into downstream training.

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