role: user

type: text

text:
<attached_files>

<code_selection path="\1774404094299\1" lines="1-10">
L1:ubuntu@0321-dsm2-nvdgxa100-prxmx70052:~$ df -h
L2:Filesystem      Size  Used Avail Use% Mounted on
L3:tmpfs            12G  1.5M   12G   1% /run
L4:efivarfs         56K   17K   34K  34% /sys/firmware/efi/efivars
L5:/dev/sda2       984G  861G   83G  92% /
L6:tmpfs            60G   17M   59G   1% /dev/shm
L7:tmpfs           5.0M     0  5.0M   0% /run/lock
L8:/dev/sda1       1.1G  6.1M  1.1G   1% /boot/efi
L9:tmpfs            12G   36K   12G   1% /run/user/1001
L10:ubuntu@0321-dsm2-nvdgxa100-prxmx70052:~$
</code_selection>

<terminal_selection title="Terminal" path="\home\ubuntu\.cursor\projects\1774404094299\terminals\1.txt" lines="7-16">
ubuntu@0321-dsm2-nvdgxa100-prxmx70052:~$ df -h
Filesystem      Size  Used Avail Use% Mounted on
tmpfs            12G  1.5M   12G   1% /run
efivarfs         56K   17K   34K  34% /sys/firmware/efi/efivars
/dev/sda2       984G  861G   83G  92% /
tmpfs            60G   17M   59G   1% /dev/shm
tmpfs           5.0M     0  5.0M   0% /run/lock
/dev/sda1       1.1G  6.1M  1.1G   1% /boot/efi
tmpfs            12G   36K   12G   1% /run/user/1001
ubuntu@0321-dsm2-nvdgxa100-prxmx70052:~$
</terminal_selection>

</attached_files>
<user_query>
i want you to delete all big dataset files and model ckpoints in all files recursivelyt. delete all of them. leave the code files as is. and make some storage. @\home\ubuntu\.cursor\projects\1774404094299\terminals\1.txt:7-16
</user_query>


role: assistant

text:
I’m going to scan `/home/ubuntu` for large non-code artifacts first, focusing on dataset-like files and model checkpoint extensions so I can delete only storage-heavy data and leave source files untouched.

**Clarifying destructive deletions**

The user wants to delete all big dataset files and model checkpoints recursively, which is potentially dangerous. I should probably ask for clarification before proceeding, especially about the workspace and if they mean the entire /home/ubuntu. It’s important to confirm the scope and maybe inspect disk usage first. I might need to switch to a planning mode or ask clarifying questions, as this involves destructive deletes across many files that could lead to ambiguity.
**Determining tool usage**

I need to figure out the best tools for file operations, including searching and finding large files. While it's noted that shell commands shouldn’t be used for reading or writing files, I think using `du` for inspecting directory sizes might still be acceptable since it doesn't read file contents. Before executing any command, I should check current terminal activities, even if it's not necessary. I'll consider using tools like Glob and `rg` for file name patterns and extensions to assist in this process.
**Planning safe file deletion**

I’m considering how to handle the user’s request to delete certain file types like `.zip`, `.pkl`, and more without removing important code or binaries. It might be best to gather candidate files and their sizes, then ask the user for confirmation. I should suggest a safe approach by scanning recursively under `/home/ubuntu`, but I also need to be cautious since they mentioned deleting all files recursively. I need to clarify this before moving forward.
**Considering tools for file operations**

I’m thinking about using multi_tool_use.parallel with multiple tools, but first, I need to check if the virtual environment (venv) is activated before running any commands related to the application. However, since I’m not using specific application commands right now, I might be able to ignore that. I should gather information from the root README or progress files but without creating new markdown files. Instead, I’ll look for existing docs and consider using Shell commands to list directories or search for files.
**Exploring file operations**

I'm trying to identify large files without focusing on patterns, and I'm debating whether Shell commands could be acceptable since it’s a terminal command rather than dealing with file contents. I might also consider using Subagent explore for a broader exploration, but direct commands like `rg` or Glob could work too. The Shell instructions say not to use certain operations for reading or searching files, but specialized tools don’t handle metadata searches. Still, I think Shell for disk usage info might be okay.
**Running parallel checks**

I’m thinking of running parallel checks, starting with a Shell command like `ls /home/ubuntu` or even `ls -a /home/ubuntu` to see if there's a virtual environment. I could also use Glob to match checkpoint and dataset patterns, but that might yield too many results. I want to specifically target model/checkpoint extension patterns and dataset archives. Additionally, using `python` to compute the largest directories with `du -xhd1 /home/ubuntu` seems like a good idea. I'll aim to utilize multi_tool_use.parallel for these tasks.


text:
The initial scan shows most space is in data-like directories rather than source trees, so I’m narrowing to actual dataset/checkpoint candidates with sizes before deleting anything destructive.

**Inspecting directory deletion**

I'm considering whether I should delete certain directories like `modi`, `modi_dataset`, and `transcripts`. The name `transcripts` feels like it could contain data rather than code, but I need more evidence before making a decision. I plan to inspect the sizes and names of large candidate files using shell commands. I think Python's os.walk could help me find files over 500MB or directories that match certain patterns. Let's proceed carefully with this evaluation!


text:
I’ve isolated this to data/checkpoint artifacts rather than whole folders so code won’t be touched; next I’m deleting large dataset/model files recursively, including cached model weights, while skipping `venv`/`site-packages` trees.
The structured transcript data and model caches alone are already massive, but I’m checking the biggest dataset directories for raw media types too so I don’t miss hidden space hogs like `.wav` shards.

**Considering deletion confirmation**

I see we have candidates for deletion and it might be wise to confirm this, as it could be risky and unclear. The user asked to "delete all," so maybe they don't want to discuss it further. I want to ensure I'm careful, especially with significant categories like `transcripts` and `soprano_data`, which may be active projects. Perhaps I should ask a targeted confirmation with options to clarify whether we should include caches and audio/video. It might be good to present what I found: over 350 GB in likely datasets and checkpoints.
**Clarifying deletion choices**

I need to confirm whether the user wants to remove all categories or exclude caches. It could be safe to delete only dataset and checkpoint directories while leaving caches untouched. However, caches consist of model files and can be quite large. If storage is a concern, deleting those could be beneficial. I think it makes sense to proceed with what aligns with the user's request, but I should also compute the total sizes per category to report back.
**Considering file deletion strategy**

I'm thinking about using an algorithm to remove files larger than 100 MB with specific extensions from the dataset/model list. I'll exclude files under certain directories like `site-packages` and `venv`, and avoid code file extensions like `.py` and `.js`. I also need to consider directories that mainly contain datasets or checkpoints, but the user mentioned removing "all files recursively." This makes me wonder whether I should include certain file types like `.arrow`, `.wav`, or `.mp4`, since they might be crucial data.
**Evaluating dataset file types**

I'm pondering the user’s instructions about focusing on dataset files and model checkpoints. However, I’m wondering if I should include audio and video files, as they could potentially count as datasets too. It feels risky to remove them because some of those outputs or media could be valuable. But then again, if they don't fit the user's criteria, perhaps it’s safer to leave them out. I’ll need to assess the importance of these files more carefully.
**Evaluating file management strategy**

I'm thinking about the dataset and model-related extensions and how to handle the directories. I should only delete files that don't contain code. This means I need to inspect each directory first, especially "transcripts," as it may have coding files mixed in. Deleting entire directories without checking is risky. Instead, I’ll aim to run a script to identify and delete only specific files, verifying totals with a dry run before taking action. I might need to confirm with the user afterward, though!
**Confirming deletion strategy**

I'm considering asking for one concise confirmation for deletion options: to either “delete all identified dataset/checkpoint files, including model caches” or “exclude caches.” I’m thinking that with disk usage at 92%, it’s reasonable to act. Since the Delete tool is impractical for so many files, I might want to use a Python script to handle deletes based on patterns, but I need to log this action due to its destructive nature. Also, I should check for any running processes that might be using the files before proceeding.
**Planning file deletion**

Since the instructions mention checking before starting long-running processes, I don't think that's necessary now. However, I want to ensure the user is aware of what will be deleted. They want to remove big dataset files and model checkpoints recursively. Our scan gave clear criteria, so we can delete those files while excluding code and environment/site-packages. I should update the user on what specific items are being removed, including cached model weights from `.cache`, before running the deletion script.
**Deciding on deletion script**

I need to design a careful deletion heuristic for files under /home/ubuntu, excluding specific directories like `.git`, `node_modules`, and various virtual environments. However, I’m uncertain about deleting model/data files in `.local/lib/python3.10/data` since they aren’t site-packages. I should recognize file extensions for models and data while considering thresholds for file sizes. Perhaps focusing on files over 100MB makes sense, but maybe I can lower that threshold to 50MB to avoid deleting important smaller files.
**Finalizing deletion criteria**

I'm going to stick with the 100MB threshold because that's generally considered "big." I also want to delete directories named `checkpoint-*`, `checkpoints`, `ckpts`, and `.checkpoints`, but only if they’re empty after file deletions. It seems reasonable to remove any empty directories once we finish. I might need to compute the total size deleted, which could be hundreds of GB. I’m thinking about using Python's os.remove or shutil.rmtree for this task, keeping in mind potential issues with hardlinks and duplicates.
**Evaluating file size accounting**

I’m thinking about how the blob count and symlink size could affect our size calculations. Using `lstat` might help in identifying symlinks without miscounting their size. It seems like we need to delete symlinks without counting them towards our total. Actual large files that are blobs are more important, while snapshot symlinks can make our output look inflated. I should also consider identifying big directories and the impact of checkpoint directories with smaller files.
**Considering data deletion steps**

After file deletions, I’m thinking about running an empty directory cleanup to remove directories like `checkpoint-*`, `checkpoints`, `ckpts`, and `.checkpoints` if they're empty. I also need to consider deleting large raw dataset files, especially since `soprano_data` is 96GB, and it looks like a lot of audio files weren’t captured. It may help to develop an algorithm that identifies media files in dataset-like directories. I should include common media extensions for a comprehensive cleanup, while making sure to keep code files intact.
**Deciding on media inclusion**

I’m considering whether to include all media files over 100MB regardless of location. It seems like some top-level movie files might not be related to datasets, and the user hasn't specifically asked for those. So, I think it would be best to only include files if their path contains specific dataset-related keywords like `transcripts`, `soprano_data`, `processed`, or `output`. It's a tricky decision, as I want to ensure I’m filtering appropriately.
**Summarizing directories**

I need to use shell Python to summarize directories based on their extension and size. This sounds like it should be straightforward: I want to check the contents of those directories and gather useful information! I should write a script that looks through the files, captures their extensions, and then sums up their sizes. That way, I can provide a clear summary of the directory contents, and it’ll be super helpful for the user. Let's get started on this!


text:
**Checking terminals**

I'm thinking I might need to check the terminals. It seems like a straightforward task, but I want to make sure I approach it correctly. I should confirm what specific aspects of the terminals I need to look into, like their functions or connections. It's interesting how sometimes the simplest tasks can lead to more detailed inquiries! Let’s see how to organize that better and make sure I'm covering all necessary details.
**Refining data inclusion**

I think I could include `.wav` files under `soprano_data` because it has that keyword. It's possible to include them under `bob5_clean` and `bob5_output` well but not for "clean" since that might be too broad. I can definitely include `transcripts` in flac/wav since that keyword works. For the dataset arrows in `.local/lib/python3.10/data/modi_hindi_custom/raw`, it’s good to include those with `/data/` in the path and `.arrow` extension. The `modi_*` paths might be worth including too. Lastly, I should also consider files in `.cache/huggingface/datasets` with the arrow extension.
**Evaluating workspace management**

I found some worktree copies under `.cursor/worktrees/transcripts...` that could be large duplicates of transcript data. It's tempting to remove those, but I have to be cautious about deleting anything inside `.cursor/worktrees`, as it might affect the IDE's state. Retaining `.cursor` is wise since it's mostly worktrees with the duplicates. The user asked for everything recursively, but I'll exclude these system folders to avoid issues. I might suggest cleaning worktrees separately later.


text:
The main deletion pass finished; I’m verifying freed space and checking whether any large dataset/checkpoint artifacts are still left behind.

**Checking storage details**

I need to check the disk space after running some commands, especially if it’s still at 92%. The user mentioned something about making storage, so I might want to report the freed space and see if there are still large directories that haven't been dealt with.

I could run `df -h` to check the disk usage and identify the top directories again. Additionally, it might be useful to scan for large dataset or checkpoint files over 100MB. Since there are no file edits, lints aren't necessary, so I’ll run the shell commands in parallel.


text:
The targeted files are gone, but `.cache` is still huge because Hugging Face and similar model caches store the real weight blobs under extensionless filenames. I’m checking that cache breakdown now so I can clear the remaining model/data payloads safely.

**Reviewing cache cleanup**

I've deleted some files from `.cache/whisper/large-v2.pt` and certain arrow dataset caches, but not the HF hub blob files. Surprisingly, this freed up 338GB, mostly from transcript files. However, `.cache` is still large at 245GB. The user wants to delete all model checkpoints and dataset files, including HF blobs. Maybe additional cleanup in directories like `.cache/huggingface/hub`, `.cache/huggingface/datasets`, and others is needed. Inspecting the `.cache` breakdown could provide more insights.


text:
**Evaluating directory contents**

I need to check the top-level directories after some cleanup. The `modi` directory is 45.8GB and might have code mixed with datasets. I should review its extension summary and count the code files. If it's mostly media or non-code files, I could delete those. I also want to look at `soprano_data` and `maya3_transcribe`, and check `lewm-tts`, since it's still 20GB. The user wants model checkpoints and dataset files, not caches, so I’ll focus on that.


text:
There’s still a second layer of storage sitting in raw dataset folders and model cache directories. I’m removing those exact data/model locations next, while still preserving `.py`, `.js`, `.md`, shell scripts, and similar code/config files.

**Considering user preferences**

I'm thinking about whether I should ask the user if they want a more thorough cleanup of raw audio datasets and caches. However, they clearly stated they wanted everything deleted, so I might just proceed with that. It's always tricky to balance thoroughness with what the user has requested. I want to make sure I'm following their instructions while also considering if they might appreciate any additional options. But for now, I’ll stick with their request.


text:
**Checking disk usage quickly**

I think I need to check the disk usage and see which directories are taking up the most space. It seems like a quick task, but I want to ensure I'm thorough. I might want to focus on the top directories that are using the most space, just to get a clearer picture of my storage situation. Let's dive into that and see what I find!


text:
I deleted the large dataset/model artifacts recursively under `/home/ubuntu` while leaving code/config files in place. That included big parquet/csv/arrow/json dataset files, raw dataset media in data folders, training checkpoints/weights, and model caches under places like `.cache/huggingface`, `.cache/torch`, `maya3_transcribe/models`, `veenaModal/models`, `soprano_data`, `modi`, `modi_processed`, and `Speech_maker_pipeline`.

Disk usage is now much healthier: `/` went from `92%` used with `83G` free to `31%` used with `652G` free. I avoided deleting editor/internal state under `.cursor` and I did not touch source files like `.py`, `.js`, `.md`, shell scripts, or similar code files.

If you want one more cleanup pass, the biggest remaining easy wins are package caches in `.cache/pip` and `.cache/uv`, plus a few standalone large audio files like `jalsa_68wSTlrw_RQ.wav`.

**Considering file deletion**

I see there are large non-code files, like `.cursor/worktrees` dataset duplicates and standalone WAV files. The user asked for recursive deletion while keeping the code. I'm wondering if these `.cursor/worktrees` files, which are taking up 24GB and are copies of transcript data, should be deleted. While it could free up space, it might disrupt the IDE worktree. However, if the user desires more storage, we could clean those files, but it carries a risk. I’ll highlight these details before taking action.
**Evaluating storage and deletions**

I'm analyzing the `.cursor` directory, which is part of the internal IDE, and it's risky to touch it. I’m considering whether to remove the remaining lewm-tts checkpoints, but the top directory shows around 3.1GB, mostly from WAV outputs. We’ve deleted the model files already, so that's fine. However, the pip and uv caches are taking up 65.7GB. If the user wants more storage, I could suggest targeting those caches next. I should keep it concise, stating that no code files were altered and confirming that we freed about 611GB.
**Confirming storage changes**

I just checked the disk usage with `df`, and it shows that we’ve actually freed 570GB, going from 861GB used down to 291GB. Now, the available space is 652GB, which leads to a usage level of just 31%. That’s impressive! It seems the storage management was effective, considering some filesystem overhead. It’s reassuring to see the changes clearly reflected in the available storage. Overall, this looks like a successful effort!
**Planning task structure**

For larger tasks, I'm thinking of organizing with 2-4 sections, including short paragraphs and perhaps a bullet list. I want to keep everything concise.

I’m considering using GB instead of GiB for clarity, and tracking disk usage is crucial. It dropped from 92% to 31% on `/`, with free space increasing from 83G to 652G after removing dataset artifacts and model caches. I might also mention remaining large non-code items like cache files, audio files, and worktree data duplicates, but I’ll keep it brief unless the user asks for more specifics.