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llm-memory-editability

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knowledge-editing

mechanistic-interpretability

experiment-artifacts

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validation

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Cannot extract the features (columns) for the split 'validation' of the config 'default' of the dataset.

Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      Schema at index 1 was different: 
Id: string
Created: string
Path: string
Args: list<item: string>
State: struct<Status: string, Running: bool, Paused: bool, Restarting: bool, OOMKilled: bool, Dead: bool, Pid: int64, ExitCode: int64, Error: string, StartedAt: string, FinishedAt: string>
Image: string
ResolvConfPath: string
HostnamePath: string
HostsPath: string
LogPath: string
Name: string
RestartCount: int64
Driver: string
Platform: string
MountLabel: string
ProcessLabel: string
AppArmorProfile: string
ExecIDs: null
HostConfig: struct<Binds: null, ContainerIDFile: string, LogConfig: struct<Type: string, Config: struct<max-file: string, max-size: string>>, NetworkMode: string, PortBindings: extension<arrow.json>, RestartPolicy: struct<Name: string, MaximumRetryCount: int64>, AutoRemove: bool, VolumeDriver: string, VolumesFrom: null, ConsoleSize: list<item: int64>, CapAdd: null, CapDrop: null, CgroupnsMode: string, Dns: null, DnsOptions: list<item: null>, DnsSearch: list<item: null>, ExtraHosts: null, GroupAdd: null, IpcMode: string, Cgroup: string, Links: null, OomScoreAdj: int64, PidMode: string, Privileged: bool, PublishAllPorts: bool, ReadonlyRootfs: bool, SecurityOpt: null, Tmpfs: struct</tmp: string>, UTSMode: string, UsernsMode: string, ShmSize: int64, Runtime: string, Isolation: string, CpuShares: int64, Memory: int64, NanoCpus: int64, CgroupParent: string, BlkioWeight: int64, BlkioWeightDevice: list<item: null>, BlkioDeviceReadBps: list<item: null>, BlkioDeviceWriteBps: list<item: null>, BlkioDeviceReadIOps: list<item: null>, BlkioDeviceWriteIOps: list<item: null>, CpuPeriod: int64, CpuQuota: int64, CpuRealtimePeriod: int64, CpuRealtimeRuntime: int64, CpusetCpus: string, CpusetMems: string, Devices: list<item: null>, DeviceCgroupRules: null, DeviceRequests: list<item: struct<Driver: string, Count: int64, DeviceIDs: list<item: string>, Capabilities: list<item: list<item: string>>, Options: extension<arrow.json>>>, MemoryReservation: int64, MemorySwap: int64, MemorySwappiness: null, OomKillDisable: bool, PidsLimit: null, Ulimits: null, CpuCount: int64, CpuPercent: int64, IOMaximumIOps: int64, IOMaximumBandwidth: int64, Mounts: list<item: extension<arrow.json>>, MaskedPaths: list<item: string>, ReadonlyPaths: list<item: string>>
GraphDriver: struct<Data: struct<ID: string, LowerDir: string, MergedDir: string, UpperDir: string, WorkDir: string>, Name: string>
Mounts: list<item: struct<Type: string, Source: string, Destination: string, Mode: string, RW: bool, Propagation: string>>
Config: struct<Hostname: string, Domainname: string, User: string, AttachStdin: bool, AttachStdout: bool, AttachStderr: bool, Tty: bool, OpenStdin: bool, StdinOnce: bool, Env: list<item: string>, Cmd: list<item: string>, Image: string, Volumes: null, WorkingDir: string, Entrypoint: null, Labels: struct<org.opencontainers.image.description: string, org.opencontainers.image.title: string, org.opencontainers.image.version: string, project: string>>
NetworkSettings: struct<SandboxID: string, SandboxKey: string, Ports: extension<arrow.json>, Networks: struct<none: struct<IPAMConfig: null, Links: null, Aliases: null, DriverOpts: null, GwPriority: int64, NetworkID: string, EndpointID: string, Gateway: string, IPAddress: string, MacAddress: string, IPPrefixLen: int64, IPv6Gateway: string, GlobalIPv6Address: string, GlobalIPv6PrefixLen: int64, DNSNames: null>>>
vs
passed: bool
python: string
torch: string
numpy: string
cuda_runtime: string
cuda_device_count: int64
gpu_name: string
driver_paths: list<item: string>
LD_LIBRARY_PATH: null
CUDA_VISIBLE_DEVICES: null
NVIDIA_VISIBLE_DEVICES: string
cuda_checks: list<item: struct<dtype: string, loss: double, passed: bool>>
parent: string
parent_file_sha256: string
parent_model_sha256: string
world_sha256: string
prediction_arrays: int64
max_prediction_float_error: double
generated_tokens_exact: bool
metrics: struct<common_atomic: struct<n: int64, answer_accuracy: double, accuracy: double, answer_nll: double>, train_composite: struct<n: int64, answer_accuracy: double, accuracy: double, answer_nll: double>, familiar_test: struct<n: int64, answer_accuracy: double, accuracy: double, answer_nll: double, atomic_correct_coverage: double, conditional_accuracy: double, autonomous_two_calls: double>, strict_test: struct<n: int64, answer_accuracy: double, accuracy: double, answer_nll: double, atomic_correct_coverage: double, conditional_accuracy: double, autonomous_two_calls: double>, anchor_atomic: struct<n: int64, answer_accuracy: double, accuracy: double, answer_nll: double>>
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 244, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 4523, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2768, in _head
                  return next(iter(self.iter(batch_size=n)))
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2972, in iter
                  for key, pa_table in ex_iterable.iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2483, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 591, in _iter_arrow
                  yield new_key, pa.Table.from_batches(chunks_buffer)
                                 ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^
                File "pyarrow/table.pxi", line 5012, in pyarrow.lib.Table.from_batches
                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: Schema at index 1 was different: 
              Id: string
              Created: string
              Path: string
              Args: list<item: string>
              State: struct<Status: string, Running: bool, Paused: bool, Restarting: bool, OOMKilled: bool, Dead: bool, Pid: int64, ExitCode: int64, Error: string, StartedAt: string, FinishedAt: string>
              Image: string
              ResolvConfPath: string
              HostnamePath: string
              HostsPath: string
              LogPath: string
              Name: string
              RestartCount: int64
              Driver: string
              Platform: string
              MountLabel: string
              ProcessLabel: string
              AppArmorProfile: string
              ExecIDs: null
              HostConfig: struct<Binds: null, ContainerIDFile: string, LogConfig: struct<Type: string, Config: struct<max-file: string, max-size: string>>, NetworkMode: string, PortBindings: extension<arrow.json>, RestartPolicy: struct<Name: string, MaximumRetryCount: int64>, AutoRemove: bool, VolumeDriver: string, VolumesFrom: null, ConsoleSize: list<item: int64>, CapAdd: null, CapDrop: null, CgroupnsMode: string, Dns: null, DnsOptions: list<item: null>, DnsSearch: list<item: null>, ExtraHosts: null, GroupAdd: null, IpcMode: string, Cgroup: string, Links: null, OomScoreAdj: int64, PidMode: string, Privileged: bool, PublishAllPorts: bool, ReadonlyRootfs: bool, SecurityOpt: null, Tmpfs: struct</tmp: string>, UTSMode: string, UsernsMode: string, ShmSize: int64, Runtime: string, Isolation: string, CpuShares: int64, Memory: int64, NanoCpus: int64, CgroupParent: string, BlkioWeight: int64, BlkioWeightDevice: list<item: null>, BlkioDeviceReadBps: list<item: null>, BlkioDeviceWriteBps: list<item: null>, BlkioDeviceReadIOps: list<item: null>, BlkioDeviceWriteIOps: list<item: null>, CpuPeriod: int64, CpuQuota: int64, CpuRealtimePeriod: int64, CpuRealtimeRuntime: int64, CpusetCpus: string, CpusetMems: string, Devices: list<item: null>, DeviceCgroupRules: null, DeviceRequests: list<item: struct<Driver: string, Count: int64, DeviceIDs: list<item: string>, Capabilities: list<item: list<item: string>>, Options: extension<arrow.json>>>, MemoryReservation: int64, MemorySwap: int64, MemorySwappiness: null, OomKillDisable: bool, PidsLimit: null, Ulimits: null, CpuCount: int64, CpuPercent: int64, IOMaximumIOps: int64, IOMaximumBandwidth: int64, Mounts: list<item: extension<arrow.json>>, MaskedPaths: list<item: string>, ReadonlyPaths: list<item: string>>
              GraphDriver: struct<Data: struct<ID: string, LowerDir: string, MergedDir: string, UpperDir: string, WorkDir: string>, Name: string>
              Mounts: list<item: struct<Type: string, Source: string, Destination: string, Mode: string, RW: bool, Propagation: string>>
              Config: struct<Hostname: string, Domainname: string, User: string, AttachStdin: bool, AttachStdout: bool, AttachStderr: bool, Tty: bool, OpenStdin: bool, StdinOnce: bool, Env: list<item: string>, Cmd: list<item: string>, Image: string, Volumes: null, WorkingDir: string, Entrypoint: null, Labels: struct<org.opencontainers.image.description: string, org.opencontainers.image.title: string, org.opencontainers.image.version: string, project: string>>
              NetworkSettings: struct<SandboxID: string, SandboxKey: string, Ports: extension<arrow.json>, Networks: struct<none: struct<IPAMConfig: null, Links: null, Aliases: null, DriverOpts: null, GwPriority: int64, NetworkID: string, EndpointID: string, Gateway: string, IPAddress: string, MacAddress: string, IPPrefixLen: int64, IPv6Gateway: string, GlobalIPv6Address: string, GlobalIPv6PrefixLen: int64, DNSNames: null>>>
              vs
              passed: bool
              python: string
              torch: string
              numpy: string
              cuda_runtime: string
              cuda_device_count: int64
              gpu_name: string
              driver_paths: list<item: string>
              LD_LIBRARY_PATH: null
              CUDA_VISIBLE_DEVICES: null
              NVIDIA_VISIBLE_DEVICES: string
              cuda_checks: list<item: struct<dtype: string, loss: double, passed: bool>>
              parent: string
              parent_file_sha256: string
              parent_model_sha256: string
              world_sha256: string
              prediction_arrays: int64
              max_prediction_float_error: double
              generated_tokens_exact: bool
              metrics: struct<common_atomic: struct<n: int64, answer_accuracy: double, accuracy: double, answer_nll: double>, train_composite: struct<n: int64, answer_accuracy: double, accuracy: double, answer_nll: double>, familiar_test: struct<n: int64, answer_accuracy: double, accuracy: double, answer_nll: double, atomic_correct_coverage: double, conditional_accuracy: double, autonomous_two_calls: double>, strict_test: struct<n: int64, answer_accuracy: double, accuracy: double, answer_nll: double, atomic_correct_coverage: double, conditional_accuracy: double, autonomous_two_calls: double>, anchor_atomic: struct<n: int64, answer_accuracy: double, accuracy: double, answer_nll: double>>

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LLM Memory: Learning and Editability — Experiment Archive

Data, predictions, numerical arrays, logs, research reports, frozen execution sources, and audits for zhusq20/llm-memory-editability. GitHub stores current source code, tests, configuration, and development instructions.

Layout

data/: experiment datasets, preprocessing products, and tokenizers.
results/: raw predictions, learning curves, arrays, logs, and run status.
docs/: research reports, protocols, frozen sources, figures, and audits.
Research index: the detailed project progress overview.
ARCHIVE_MANIFEST.json: exact paths, byte sizes, SHA256 hashes, and excluded-file inventory.

Original project-relative paths are preserved. Use scripts/download_experiment_artifacts.py from the GitHub repository to restore data, results, and documentation locally.

Model weights, optimizer states, Docker image archives, Python environments, and internal W&B SDK caches are excluded. The user's 200 MB per-file limit applies only to GitHub; larger data files are included here.

This is a file-level archive. Runs that were active during capture may have newer local outputs; the archive is not an atomic model checkpoint and does not imply completion. Consult each batch's status, frozen protocol, data-source manifests, and interpretation boundaries. Upstream data retains its original licenses and attribution requirements.

Numerical array bundles

NumPy arrays are stored in per-experiment array-archives/*.tar.gz bundles to avoid tens of thousands of tiny LFS objects. ARRAY_ARCHIVES.json maps each original path to its bundle. ARCHIVE_MANIFEST.json retains every original file's SHA256 and byte size, plus the physical upload inventory. The GitHub download script selects the required bundles, verifies them, and restores selected arrays to their original paths. All array members were independently read back and hash-checked before upload.

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