You fixed the typos, but the script still tries to load from /mnt/storage/models/nomic-embed-768, which doesn’t exist yet. Let’s create that path (as a symlink to the actual model). If the model isn’t downloaded, we’ll fetch it once, then wire everything up.

Paste this whole block (it’s safe & idempotent)

# 0) Paths we’ll use
DOCS_MODEL="$HOME/Documents/models/nomic-embed-768"
STOR_MODELS="/mnt/storage/models"
STOR_MODEL_LINK="$STOR_MODELS/nomic-embed-768"

# 1) Try to find an existing cached Nomic embedder snapshot
SNAP=""
for base in "/mnt/storage/hf-cache/hub" "$HOME/.cache/huggingface/hub"; do
  CAND=$(ls -1d "$base"/models--nomic-ai--nomic-embed-text-v1.5/snapshots/* 2>/dev/null | head -n1)
  if [ -n "$CAND" ]; then SNAP="$CAND"; break; fi
  CAND=$(ls -1d "$base"/models--nomic-ai--nomic-bert-2048/snapshots/* 2>/dev/null | head -n1)
  if [ -n "$CAND" ]; then SNAP="$CAND"; break; fi
done

# 2) If not found, fetch once (temporarily disable offline)
if [ -z "$SNAP" ]; then
  echo "[info] No local Nomic snapshot found — fetching nomic-ai/nomic-embed-text-v1.5 once..."
  unset TRANSFORMERS_OFFLINE HF_HUB_OFFLINE
  python3 - << 'PY'
from sentence_transformers import SentenceTransformer
# Download to the storage cache so it lives on the big drive
SentenceTransformer("nomic-ai/nomic-embed-text-v1.5",
                    trust_remote_code=True,
                    cache_folder="/mnt/storage/hf-cache")
print("download_ok")
PY
  # locate the new snapshot
  SNAP=$(ls -1d /mnt/storage/hf-cache/hub/models--nomic-ai--nomic-embed-text-v1.5/snapshots/* 2>/dev/null | head -n1)
fi

# 3) Sanity check
if [ -z "$SNAP" ]; then
  echo "[error] Still no Nomic snapshot located. Abort."
  exit 1
fi
echo "[info] Using snapshot: $SNAP"

# 4) Create friendly paths in BOTH places your script might reference
mkdir -p "$HOME/Documents/models" "$STOR_MODELS"

ln -sfn "$SNAP" "$DOCS_MODEL"
ln -sfn "$SNAP" "$STOR_MODEL_LINK"

echo "[info] Linked:"
ls -la "$DOCS_MODEL" || true
ls -la "$STOR_MODEL_LINK" || true

# 5) (Re)enable offline if you want
export TRANSFORMERS_OFFLINE=1 HF_HUB_OFFLINE=1 HF_HUB_DISABLE_TELEMETRY=1
echo "[info] Offline mode re-enabled."

# 6) Kick off ingestion
CUDA_VISIBLE_DEVICES=0 \
python3 "$HOME/Documents/tokenspace/ingest_pile.py" \
  --root_dir "/mnt/storage/pile_uncopyrighted"

What this does:
– Finds (or downloads) nomic-ai/nomic-embed-text-v1.5 into /mnt/storage/hf-cache.
– Exposes two stable symlinks:
– ~/Documents/models/nomic-embed-768
– /mnt/storage/models/nomic-embed-768
– Your script will succeed whether it reads MODEL_DIR from Documents or Storage.

If it still errors, paste the new traceback (it’ll be different now), and we’ll knock out the next blocker.

Perfect—that’s the snapshot we need. Two quick notes and we’re done:

1) Your path needs a leading slash:
/mnt/storage/hf-cache/hub/models--nomic-ai--nomic-embed-text-v1.5/snapshots/e5cf08aadaa33385f5990def41f7a23405aec398

2) Your script is already trying to load from /mnt/storage/models/nomic-embed-768, so let’s point that path at the snapshot with a symlink.

Do this exactly

# 1) Make sure the target models dir exists
sudo mkdir -p /mnt/storage/models

# 2) Link the snapshot to the path your script expects
sudo ln -sfn "/mnt/storage/hf-cache/hub/models--nomic-ai--nomic-embed-text-v1.5/snapshots/e5cf08aadaa33385f5990def41f7a23405aec398" \
             /mnt/storage/models/nomic-embed-768

# 3) Verify the link resolves
ls -la /mnt/storage/models/nomic-embed-768

You should see nomic-embed-768 -> …/snapshots/e5cf0….

Run ingestion

CUDA_VISIBLE_DEVICES=0 \
python3 ~/Documents/tokenspace/ingest_pile.py \
  --root_dir "/mnt/storage/pile_uncopyrighted"

That should clear the “Path not found” error, since /mnt/storage/models/nomic-embed-768 now exists and points to your snapshot. If it throws a different error, paste it and we’ll knock that one down next.

Absolutely. Two ways—pick the one you like:

Option A — Quick one-liner (point to your known snapshot)

Since you already know the exact path, just set MODEL_DIR to it and be done.

# 1) backup
cp ~/Documents/tokenspace/ingest_pile.py ~/Documents/tokenspace/ingest_pile.py.bak

# 2) replace any hard-coded MODEL_DIR line with your snapshot path
sed -i 's#^MODEL_DIR *= *".*"#MODEL_DIR = "/mnt/storage/hf-cache/hub/models--nomic-ai--nomic-embed-text-v1.5/snapshots/e5cf08aadaa33385f5990def41f7a23405aec398"#' \
  ~/Documents/tokenspace/ingest_pile.py

# 3) run
CUDA_VISIBLE_DEVICES=0 \
python3 ~/Documents/tokenspace/ingest_pile.py \
  --root_dir "/mnt/storage/pile_uncopyrighted"

Option B — Make it robust (accept --model_dir and env override)

Paste this small block once near the top of ingest_pile.py (right after the imports), and delete any older lines that hard-code MODEL_DIR/ROOT_DIR.

# --- begin configurable args ---
import os, argparse, pathlib

# Your known-good local default (edit if you ever change snapshots)
DEFAULT_MODEL_DIR = "/mnt/storage/hf-cache/hub/models--nomic-ai--nomic-embed-text-v1.5/snapshots/e5cf08aadaa33385f5990def41f7a23405aec398"

parser = argparse.ArgumentParser()
parser.add_argument("--root_dir", required=True, help="Folder with .txt/.md docs")
parser.add_argument("--model_dir",
    default=os.environ.get("MODEL_DIR", DEFAULT_MODEL_DIR),
    help="Local path to SentenceTransformer model OR HF repo id")
args = parser.parse_args()

ROOT_DIR = args.root_dir
MODEL_DIR = args.model_dir

print(f"[cfg] ROOT_DIR={ROOT_DIR}")
print(f"[cfg] MODEL_DIR={MODEL_DIR}")

# helpful checks
if not os.path.exists(ROOT_DIR):
    raise FileNotFoundError(f"ROOT_DIR not found: {ROOT_DIR}")
if not (os.path.isdir(MODEL_DIR) or "/" not in MODEL_DIR):
    # if it looks like a local path and doesn't exist, fail early with a clear message
    raise FileNotFoundError(f"MODEL_DIR not found: {MODEL_DIR}")
# --- end configurable args ---

Now you can run with any of these:

# use the built-in default (your snapshot path)
CUDA_VISIBLE_DEVICES=0 \
python3 ~/Documents/tokenspace/ingest_pile.py \
  --root_dir "/mnt/storage/pile_uncopyrighted"

# or point somewhere else at runtime
CUDA_VISIBLE_DEVICES=0 \
python3 ~/Documents/tokenspace/ingest_pile.py \
  --root_dir "/mnt/storage/pile_uncopyrighted" \
  --model_dir "/home/darren/Documents/models/nomic-embed-768"

# or via env var
export MODEL_DIR="/home/darren/Documents/models/nomic-embed-768"
CUDA_VISIBLE_DEVICES=0 \
python3 ~/Documents/tokenspace/ingest_pile.py \
  --root_dir "/mnt/storage/pile_uncopyrighted"

If you want, I can also give you a tiny patch to replace all old MODEL_DIR = "..." definitions automatically while inserting the argparse block.

Heard. I rewrote your script with the features you asked for (numeric ordering, clear progress, per-file timing, and cumulative doc/chunk counts, plus resume behavior). Grab it here:

Download the rewritten script (ingest_pile_v2.py)

Key upgrades vs. your original fileciteturn0file0:

• Natural numeric ordering for files (so you can tell what’s next).
• Loud progress logs: “START file…”, existing-chunk count, and a final “[ok] … cum: docs=… chunks=…”.
• Timings per phase: read+chunk, metadata insert, embed, total.
• Idempotent resume: if a document already has chunks and FORCE_REEMBED=0, it skips re-embedding (still upserts text/token counts). Set FORCE_REEMBED=1 to recompute.
• Console + optional logfile ($HOME/Documents/tokenspace/ingest.log by default).

How to run (copy/paste)

# (optional) put logs here; empty LOG_FILE disables file logging
export LOG_FILE="$HOME/Documents/tokenspace/ingest.log"

# (optional) only re-embed if you really want to recompute all vectors for existing docs
export FORCE_REEMBED=0

# your usual offline flags can stay on if the model dir is local
export TRANSFORMERS_OFFLINE=1 HF_HUB_OFFLINE=1 HF_HUB_DISABLE_TELEMETRY=1

# paths (adjust if needed)
export ROOT_DIR="/mnt/storage/pile_uncopyrighted"
export MODEL_DIR="/home/darren/Documents/models/nomic-embed-768"   # or your snapshot path

# DB via unix socket (adjust if your port/DB differ)
export DB_DSN="postgresql:///tokenspace?host=/var/run/postgresql&port=5433"

# run it
CUDA_VISIBLE_DEVICES=0 python3 /path/to/ingest_pile_v2.py

What you’ll see for each file

• START <path>
• existing chunks for doc_id=…: <N>
• Final line like:
  [ok 17] /…/file_00123.txt -> 42 chunk(s) |
read+chunk=2.31s meta=0.78s embed=11.42s total=14.57s |
cum: docs=31 chunks=1,277,343

Notes / knobs

• Batch size: BATCH_SIZE env (default 64).
• Chunking: CHUNK_SIZE_CHARS (1500) and CHUNK_OVERLAP_CHARS (200) envs.
• Skip vs re-embed: toggle with FORCE_REEMBED=0/1.
• Counts: It queries actual content.documents / content.chunks counts after each file, so your “where am I?” is always precise.
• Model path: make sure MODEL_DIR points at your local Nomic snapshot or symlink; the script prints it at start.

If you’d like, I can also make a matching llama.cpp/Qwen launcher script for your Desktop, same style (echo settings, clear timings).

Got it — the script is at:
/home/darren/Documents/tokenspace/ingest_pile_v2.py

and your local model is really at:
/mnt/storage/hf-cache/hub/models--nomic-ai--nomic-embed-text-v1.5/snapshots/e5cf08aadaa33385f5990def41f7a23405aec398

So let’s point directly there. Run this:

CUDA_VISIBLE_DEVICES=0 \
MODEL_DIR="/mnt/storage/hf-cache/hub/models--nomic-ai--nomic-embed-text-v1.5/snapshots/e5cf08aadaa33385f5990def41f7a23405aec398" \
python3 /home/darren/Documents/tokenspace/ingest_pile_v2.py \
  --root_dir "/mnt/storage/pile_uncopyrighted"

That way:
– The script path is correct (your v2 file).
– MODEL_DIR env overrides the default, so it doesn’t look for /home/darren/Documents/models/nomic-embed-768.

👉 If you want to avoid typing the long snapshot path every time, I recommend making a clean symlink once:

mkdir -p /home/darren/Documents/models
ln -sfn "/mnt/storage/hf-cache/hub/models--nomic-ai--nomic-embed-text-v1.5/snapshots/e5cf08aadaa33385f5990def41f7a23405aec398" \
  /home/darren/Documents/models/nomic-embed-768

Then you can run the shorter command:

CUDA_VISIBLE_DEVICES=0 \
MODEL_DIR="/home/darren/Documents/models/nomic-embed-768" \
python3 /home/darren/Documents/tokenspace/ingest_pile_v2.py \
  --root_dir "/mnt/storage/pile_uncopyrighted"

Do you want me to also make you a little run_ingest.sh script on your Desktop so you just double-click or type ./run_ingest.sh instead of retyping this whole command each time?

You’re super close—the error means the folder at ~/Documents/models/nomic-embed-768 isn’t a complete, recognized model (missing a proper config.json / modules.json, etc.). Easiest fix: point the script to the repo id so sentence-transformers can fetch the right files once, cache them to your big drive, and then you’re fully offline again.

One-time “online” fetch (recommended)

# 1) Allow one fetch and make sure cache goes to the big SSD
unset TRANSFORMERS_OFFLINE HF_HUB_OFFLINE
export HF_HOME=/mnt/storage/hf-cache

# 2) Run the new script with the REPO ID (no slashes in a path = we treat it as a repo)
CUDA_VISIBLE_DEVICES=0 \
MODEL_DIR="nomic-ai/nomic-embed-text-v1.5" \
python3 /home/darren/Documents/tokenspace/ingest_pile_v2.py \
  --root_dir "/mnt/storage/pile_uncopyrighted"

• On first run it will download to /mnt/storage/hf-cache.
• After that, you can go fully offline again:
  bash
export TRANSFORMERS_OFFLINE=1 HF_HUB_OFFLINE=1 HF_HUB_DISABLE_TELEMETRY=1

If you really want to stick with a local path

Your symlinked snapshot looks partial. Check it:

ls -la /home/darren/Documents/models/nomic-embed-768
ls -la /mnt/storage/hf-cache/hub/models--nomic-ai--nomic-embed-text-v1.5/snapshots/e5cf08aadaa33385f5990def41f7a23405aec398 | head

If you don’t see a valid config.json (with a "model_type" key) and typical model files, just force a complete local download and relink:

unset TRANSFORMERS_OFFLINE HF_HUB_OFFLINE
python3 - << 'PY'
from huggingface_hub import snapshot_download
p = snapshot_download("nomic-ai/nomic-embed-text-v1.5", cache_dir="/mnt/storage/hf-cache", local_files_only=False)
print("Snapshot:", p)
PY

# update the link (now points to a complete snapshot)
SNAP=$(ls -1d /mnt/storage/hf-cache/hub/models--nomic-ai--nomic-embed-text-v1.5/snapshots/* | head -n1)
ln -sfn "$SNAP" /home/darren/Documents/models/nomic-embed-768

# re-enable offline if you want
export TRANSFORMERS_OFFLINE=1 HF_HUB_OFFLINE=1 HF_HUB_DISABLE_TELEMETRY=1

# run using the local path
CUDA_VISIBLE_DEVICES=0 \
MODEL_DIR="/home/darren/Documents/models/nomic-embed-768" \
python3 /home/darren/Documents/tokenspace/ingest_pile_v2.py \
  --root_dir "/mnt/storage/pile_uncopyrighted"

Either route will resolve the “Unrecognized model” error. The simplest is the first: use MODEL_DIR="nomic-ai/nomic-embed-text-v1.5" once to seed the cache, then flip offline back on.

Nice—thanks for the ls. That explains the crash:

• Your snapshot only has 1_Pooling/ and some symlinked configs.
• It’s missing the 0_Transformer/ directory (the actual encoder weights + tokenizer).
• When SentenceTransformer tries to load the Transformer block, it can’t find a proper config.json with a "model_type", so it errors.

Let’s fix it by pulling a complete copy of nomic-ai/nomic-embed-text-v1.5 into your HF cache and pointing your script at it.

Do this exactly

# 1) Temporarily allow online fetch and ensure cache is on the 4TB drive
unset TRANSFORMERS_OFFLINE HF_HUB_OFFLINE
export HF_HOME=/mnt/storage/hf-cache

# 2) Force a complete snapshot download into your cache
python3 - << 'PY'
from huggingface_hub import snapshot_download
p = snapshot_download(
    "nomic-ai/nomic-embed-text-v1.5",
    cache_dir="/mnt/storage/hf-cache",
    local_files_only=False,
    ignore_patterns=["*.pt"],      # keep it lean; safetensors is enough
)
print("Snapshot:", p)
PY

# 3) Update your friendly link to point at the *fresh* snapshot
SNAP=$(ls -1d /mnt/storage/hf-cache/hub/models--nomic-ai--nomic-embed-text-v1.5/snapshots/* | tail -n1)
ln -sfn "$SNAP" /home/darren/Documents/models/nomic-embed-768

# 4) Verify the structure now has BOTH modules
ls -la /home/darren/Documents/models/nomic-embed-768 | sed -n '1,120p'
ls -la /home/darren/Documents/models/nomic-embed-768/0_Transformer | head
ls -la /home/darren/Documents/models/nomic-embed-768/1_Pooling    | head

You should see a 0_Transformer/ directory with things like config.json, tokenizer.json, vocab.json/merges.txt (or spiece.model, depending on the base model), and model files.

Run ingest

# (optional) go back to offline if you like
export TRANSFORMERS_OFFLINE=1 HF_HUB_OFFLINE=1 HF_HUB_DISABLE_TELEMETRY=1

# kick off ingestion
CUDA_VISIBLE_DEVICES=0 \
MODEL_DIR="/home/darren/Documents/models/nomic-embed-768" \
python3 /home/darren/Documents/tokenspace/ingest_pile_v2.py \
  --root_dir "/mnt/storage/pile_uncopyrighted"

If anything still complains, paste the first 30 lines of:

ls -la /home/darren/Documents/models/nomic-embed-768/0_Transformer
cat /home/darren/Documents/models/nomic-embed-768/0_Transformer/config.json | head -n 30

…but I expect this will resolve it. The missing 0_Transformer was the culprit.

Got it. Reboot first. When you’re back up, here’s a tight checklist to resume cleanly without re-debugging everything:

After restart — quick sanity checks

# 1) Is the 4TB drive mounted where we expect?
ls -la /mnt/storage | head

# 2) Is your home symlink handy (optional GUI access)?
ls -la ~/Storage

# 3) Model snapshot looks complete? (should have 0_Transformer + 1_Pooling)
ls -la /home/darren/Documents/models/nomic-embed-768
ls -la /home/darren/Documents/models/nomic-embed-768/0_Transformer | head

Environment + venv

# 4) Activate your venv
source ~/Documents/ougway_venv/bin/activate

# 5) Offline/telemetry flags (set them how you like)
export TRANSFORMERS_OFFLINE=1 HF_HUB_OFFLINE=1 HF_HUB_DISABLE_TELEMETRY=1
export HF_HOME=/mnt/storage/hf-cache

# 6) (optional) log file location
export LOG_FILE="$HOME/Documents/tokenspace/ingest.log"

# 7) Paths
export MODEL_DIR="/home/darren/Documents/models/nomic-embed-768"
export ROOT_DIR="/mnt/storage/pile_uncopyrighted"
export DB_DSN="postgresql:///tokenspace?host=/var/run/postgresql&port=5433"

Run ingestion

CUDA_VISIBLE_DEVICES=0 \
python3 /home/darren/Documents/tokenspace/ingest_pile_v2.py \
  --root_dir "$ROOT_DIR"

If it throws another “giant error”

Grab these right away so we can pinpoint it fast:

# last ~60 lines of your ingest log (if set)
tail -n 60 "$HOME/Documents/tokenspace/ingest.log"

# terminal traceback already shows Python-side issues
# system/kernel hints (disk/GPU/driver)
dmesg | tail -n 100

# GPU sanity
nvidia-smi

If the model folder still lacks 0_Transformer, we’ll just do the one-time complete fetch again (2 commands), but the checklist above should get you rolling as soon as the machine is back.

Good question — here’s the straight answer:

• Yes, it’s supposed to stay.
  When you pull a model like nomic-embed-text-v1.5 with Hugging Face, it saves into your cache directory (HF_HOME=/mnt/storage/hf-cache). That includes all the big pieces:
  • config.json
  • pytorch_model.bin / model.safetensors
  • tokenizer files
  • pooling module

• Why it seems to re-download:
  1. If the cache folder isn’t consistent (wrong symlink, different env vars), transformers thinks the model isn’t there and goes back to Hugging Face.
  2. If the previous download got interrupted, you’ll see it fetching blobs again.
  3. Sometimes sentence-transformers also creates its own wrapper layer (that 1_Pooling folder you saw). That can trigger an extra fetch the first time.

• Once it’s cached, it stays — unless:
  • You clear ~/.cache/huggingface or /mnt/storage/hf-cache.
  • You reinstall the venv and wipe the cache path.
  • You use a different path in MODEL_DIR than the actual cached folder.

🔑 In your case: now that we pinned HF_HOME to /mnt/storage/hf-cache and symlinked nomic-embed-768, all future runs should reuse the same safetensors + config without pulling them again.

👉 Do you want me to show you how to do a quick verification step (ls -lh on the model folder + check file sizes) so you’ll know the cache is complete, and Hugging Face won’t try to redownload next time?

Exactly 👍

• If the full set of model files is in your cache (HF_HOME or ~/.cache/huggingface), it stays local.
• On load, transformers and sentence-transformers first check your cache.
• If everything’s there and the checksums match → it never goes out to Hugging Face.
• If something’s missing or corrupted → then it tries to fetch just that piece.

So once the cache is complete and your MODEL_DIR points to it correctly, all runs are offline, local-only.

Do you want me to give you the exact ls -lh command to confirm all the expected files (config, tokenizer, safetensors) are sitting in your cached nomic-embed-768 directory?

Yes — that’s the right approach. Don’t block Hugging Face; just make sure the cache is complete so it never needs to reach out.

Here’s how to confirm everything is already downloaded:

# Look inside your cached model directory
ls -lh /home/darren/Documents/models/nomic-embed-768

For a complete local model you should see at least these:
– config.json
– sentence_bert_config.json (specific to SentenceTransformers)
– config_sentence_transformers.json
– modules.json
– model.safetensors (the actual weights, often big, multiple GB)
– README.md (not critical, but usually present)
– any subdirs like 1_Pooling/ (for pooling layer info).

If all those are there and the safetensors file has a size in the GBs, then you’re good — the cache is complete.

👉 Next run won’t download again. It will just load from disk.

Do you want me to draft a little “sanity check” one-liner that not only lists but also verifies file sizes and highlights if any of those core files are missing?

That’s a solid plan 👍.

After your reboot, here’s the cleanest way to re-pull Hugging Face’s embedder and ensure the cache is fully populated:

# Reactivate venv if needed
source ~/Documents/ougway_venv/bin/activate

# Run once to force HF to fill the cache
python3 -c "from sentence_transformers import SentenceTransformer; \
SentenceTransformer('nomic-ai/nomic-embed-text-v1.5', cache_folder='/home/darren/Documents/models/nomic-embed-768')"

That command will:
– Go to Hugging Face if anything is missing,
– Download all required config + weights,
– Save them straight into /home/darren/Documents/models/nomic-embed-768.

After that, your ingest script will always load locally without trying to fetch again.

⚠️ Key detail: make sure you don’t have both /mnt/storage/... and /home/darren/Documents/models/... fighting each other. Let’s keep one canonical path (~/Documents/models/nomic-embed-768) and stick with that everywhere.

Want me to also show you how to check your Hugging Face cache size so you’ll know for sure nothing is missing?

Got it — don’t panic yet, Darren. When Ubuntu freezes right after login, it’s usually one of these:

1. GPU driver crash (most common, especially with NVIDIA).
2. Corrupt config in your home directory (e.g. .Xauthority, .gnome, .config/…).
3. Filesystem errors on reboot.

Here’s how you can recover step-by-step:

1. Boot into a root shell (recovery mode)

1. Reboot, hold Shift (or Esc) to get the GRUB menu.
2. Choose Advanced options for Ubuntu → then pick the kernel with (recovery mode).
3. In the recovery menu, pick root – Drop to root shell prompt.

From there you’ll be in a root shell.

2. Check filesystem health

mount -o remount,rw /
fsck -f /dev/sdb   # check your storage drive
fsck -f /dev/sda1  # check your root partition (replace with actual)

Fix any reported errors.

3. Reset graphics if needed

Sometimes the NVIDIA driver update causes a hard lock. To back out:

apt purge nvidia-*
ubuntu-drivers autoinstall

That will reinstall a stable NVIDIA driver.

4. Clean up possible bad configs

Corrupted X/Wayland login files can also hang the session. From the root shell (or by pressing Ctrl+Alt+F3 to get a TTY after boot):

mv /home/darren/.Xauthority /home/darren/.Xauthority.bak
mv /home/darren/.config /home/darren/.config.bak

(Don’t worry, .config will regenerate — you won’t lose system files, only desktop/app preferences.)

5. Try logging in again

Reboot with:

reboot

Then see if it lets you into the desktop.

⚡ If it’s still locking up, we can temporarily force Ubuntu to boot into a text console only (no graphics) so you can work on it.

Do you want me to show you how to set that up, so you can at least boot in and repair without the GUI crashing?

Got it — that happens when GRUB is set to “hidden” on single-boot installs. We can still reach recovery mode, but it takes a slightly different trick. Here’s what you do:

1. Force GRUB to show

• Power on the machine.
• As soon as the BIOS splash screen disappears (before Ubuntu logo), hold down the Shift key (BIOS systems) or tap Esc repeatedly (UEFI systems).
• If nothing shows, try pressing Esc or Shift repeatedly right after powering on, not just once. Timing matters.

If GRUB still won’t show, we’ll temporarily force it:

1. Boot the machine. When it freezes at the login screen, press Ctrl+Alt+F3 to switch to a text console (TTY).
   – If that works, you can log in as your user and we can fix GRUB so it always shows.
   – If it doesn’t work and you’re truly locked out, we’ll need a Live USB.

2. If you can reach a TTY (Ctrl+Alt+F3)

Log in, then edit GRUB:

sudo nano /etc/default/grub

Change:

GRUB_TIMEOUT_STYLE=hidden

to:

GRUB_TIMEOUT_STYLE=menu

and make sure:

GRUB_TIMEOUT=10

Save, then run:

sudo update-grub

Next reboot, you’ll see the GRUB menu and can choose Advanced Options → Recovery Mode.

3. If TTY does not work

You’ll need to boot with a Ubuntu Live USB, mount your root partition, and then chroot in to fix drivers or configs. That’s a bit more involved, but doable.

👉 Question for you: when it freezes after login, can you still press Ctrl+Alt+F3 and get a black screen asking for your username? Or is the whole system truly frozen with no keyboard response?