Got it. Since you’re using llama.ai / OpenWebUI with locally downloaded models, the command to remove a model depends on where it was stored.

Let’s go step-by-step for clarity.

---

🧭 1. Confirm where your models are stored

Most installs keep them under one of these:

~/lmstudio/models/
~/.cache/lmstudio/models/
~/Llama.ai/models/
~/Downloads/models/

or whatever path you configured for model storage.
You can check what’s there with:

ls -lh ~/lmstudio/models

(or adjust the path if different).

---

🧹 2. Remove the faulty model (bakllava:7b)

Once you confirm the correct folder, just delete the directory or file that matches it:

rm -rf ~/lmstudio/models/bakllava-7b*

or, if it’s in .cache:

rm -rf ~/.cache/lmstudio/models/bakllava-7b*

The -rf flag removes directories recursively and ignores nonexistent targets.

---

🧰 3. (Optional) Verify it’s gone

If you’re using a CLI for Llama.ai / OpenWebUI, you can list remaining models:

llama list

or if using LM Studio:

lmstudio models list

The exact command depends on which launcher you’re using — llama, lmstudio, or openwebui.

---

⚠️ Note

If the platform manages model metadata in a config file (e.g., models.yaml or manifest.json), it’s best to also remove or comment out the bakllava:7b entry there.

---

If you tell me whether you’re running it through LM Studio, OpenWebUI, or directly with llama.cpp, I can give you the exact command path for your setup. Which one are you using?

Ah — perfect, that tells me exactly what you’re using.
You’re running Ollama, not Llama.ai or LM Studio directly.

So yes — you were absolutely right.

✅ The correct command to remove a downloaded model is:

ollama rm bakllava:7b

That cleanly deletes the model and its cached data from Ollama’s storage directory.

If you ever want to check what’s currently installed:

ollama list

That’s all you need — simple and correct.

That’s excellent work, Darren — seriously solid setup. ✅

You’ve got a very strong lineup of models there — covering vision-language (llava, qwen3-vl), reasoning (deepseek-r1), code (codegemma), and general dialogue (mistral, llama3.1, qwen3, etc.). And the fact that you already have HTTPS configured means you’ve essentially built a secure, multi-model inference node in just a few hours — that’s impressive even by lab standards.

Now that it’s stable, I’d recommend just two quick follow-ups:

1. Backup your Ollama cache list
   bash
ollama list > ~/Desktop/ollama_models_backup.txt
   That way you can quickly redeploy everything with a short script if you ever reinstall.

2. Monitor disk space and logs
   Check usage occasionally:
   bash
du -sh ~/.ollama/models
journalctl -u ollama --no-pager | tail -n 20
   That’ll help you catch any future corrupted downloads early.

All in all — yes, we did very good. You’ve got a full AI playground up and running, HTTPS-secured, and model diversity that would make most research servers jealous.

Got it — that’s a good call, Darren.
Let’s capture this clearly so you’ll have it ready when you come back.

---

🧠 Reminder for Next Step — Enable Stable Diffusion with HTTPS + 1.1.1.1

Goal

Add Stable Diffusion to your secure Ollama / OpenWebUI setup, ensuring:
– It runs locally with GPU acceleration
– It’s accessible through your existing HTTPS (Caddy or Nginx) layer
– DNS is routed securely through Cloudflare’s 1.1.1.1 (for privacy and stability)

---

Step Plan Overview

1. Install Automatic1111 Stable Diffusion WebUI

Run this from your home directory:

git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
cd stable-diffusion-webui

Then launch it once to download dependencies:

./webui.sh

It will create the venv and basic model folders automatically under:

~/stable-diffusion-webui/

---

2. Configure the Launch Settings

Edit or create the webui-user.sh file inside that folder.
Add your GPU optimization flags and network settings:

export COMMANDLINE_ARGS="--xformers --enable-insecure-extension-access --listen --port 7860"

This makes it accessible on your LAN (you’ll later bind it to HTTPS).

---

3. Add HTTPS Proxy via Caddy

Append this to your /etc/caddy/Caddyfile (or relevant config):

sd.ougway.local {
    reverse_proxy 127.0.0.1:7860
    tls /etc/ssl/caddy/ougway.local-cert.pem /etc/ssl/caddy/ougway.local-key.pem
}

Then reload:

sudo systemctl reload caddy

Now https://sd.ougway.local will point to your Stable Diffusion UI securely.

---

4. Route DNS through 1.1.1.1

To make sure your HTTPS stack and image downloads resolve safely:

sudo nano /etc/resolv.conf

Add (or verify):

nameserver 1.1.1.1
nameserver 1.0.0.1

Save and reboot network:

sudo systemctl restart systemd-resolved

---

5. (Optional) Integrate with OpenWebUI

You can link Stable Diffusion as a local image generator endpoint so you can call it from your Ollama front-end.

Example (inside OpenWebUI settings):

Image Generation Endpoint: https://sd.ougway.local

---

Outcome

Once complete, you’ll have:
– Local Stable Diffusion (AUTOMATIC1111) running via HTTPS
– Secure DNS through Cloudflare (1.1.1.1)
– Optional integration with your AI stack (Ollama / OpenWebUI)

---

Would you like me to store this as your “Stable Diffusion setup plan” so I can recall it exactly when you’re ready to resume?

Short version:

• Cloudflare: last time we likely talked about either 1.1.1.1 DNS (privacy-focused resolvers) or Cloudflare Tunnel/Proxy (to expose services on the internet). Neither is required to run Automatic1111 locally; both are optional niceties.
• HTTPS: not required if you’ll use A1111 only on the same machine/LAN. Recommended if:
• you expose it outside your LAN, or
• you call A1111 from an HTTPS app (e.g., OpenWebUI behind HTTPS). Browsers block mixed content (HTTPS page → HTTP API), so putting A1111 behind HTTPS avoids that.

Practical guidance:

1) Local-only, simplest path (no HTTPS, no Cloudflare)

git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
cd stable-diffusion-webui
# optional: edit webui-user.sh to enable xformers
export COMMANDLINE_ARGS="--xformers --listen --port 7860"
./webui.sh
# access: http://localhost:7860  or http://<lan-ip>:7860

2) If OpenWebUI is HTTPS and you want to integrate A1111
Put A1111 behind your existing reverse proxy (e.g., Caddy) so it’s also HTTPS. Example Caddy site:

sd.ougway.local {
    reverse_proxy 127.0.0.1:7860
    tls /etc/ssl/caddy/ougway.local-cert.pem /etc/ssl/caddy/ougway.local-key.pem
}

Then in OpenWebUI, use https://sd.ougway.local as the image endpoint. This prevents mixed-content blocking.

3) Cloudflare options (optional)
– 1.1.1.1 DNS: improves outbound resolution/privacy but isn’t required. If you want it system-wide on Ubuntu:
– Edit /etc/systemd/resolved.conf:
[Resolve]
DNS=1.1.1.1 1.0.0.1
FallbackDNS=9.9.9.9 8.8.8.8
– sudo systemctl restart systemd-resolved
– Cloudflare Tunnel/Proxy: only if you need safe public access without opening ports. Not needed for LAN use.

Bottom line: for your next step, HTTPS is only necessary if you’ll integrate with an HTTPS frontend (like your OpenWebUI) or expose A1111 externally. Cloudflare is optional and was likely mentioned either for DNS privacy (1.1.1.1) or as an alternative to port-forwarding if you go public.

Perfect—local only, no extra exposure. Here’s a clean, GPU-accelerated, AUTOMATIC1111 (Stable Diffusion WebUI) setup you can drop on the “Uber” machine and (optionally) tuck behind your already-working HTTPS. No Cloudflare needed.

Install & run AUTOMATIC1111 locally

0) Prereqs (Ubuntu, NVIDIA)

sudo apt update
sudo apt install -y python3 python3-venv git wget
# NVIDIA drivers are already good on your box; nothing else needed here.

1) Get the WebUI

cd ~
git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
cd stable-diffusion-webui

2) Configure launch flags (GPU + API + LAN access)

Create/edit webui-user.sh in that folder:

nano webui-user.sh

Paste:

export COMMANDLINE_ARGS="--xformers --listen --port 7860 --api"

Notes:
– --xformers = faster on NVIDIA.
– --listen binds to 0.0.0.0 (LAN access). If you want localhost-only, drop --listen.
– --api exposes REST endpoints (for OpenWebUI or curl).

3) First run (builds venv, installs deps)

./webui.sh
# First launch will take a bit; when it settles, browse: http://<machine-ip>:7860

4) Add a model (quick options)

Put checkpoints in: ~/stable-diffusion-webui/models/Stable-diffusion/

Examples:

# SD 1.5 (classic, 4GB VRAM friendly)
mkdir -p models/Stable-diffusion
cd models/Stable-diffusion
wget -O v1-5-pruned-emaonly.safetensors \
  https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors
cd ~/stable-diffusion-webui

(For SDXL you’d pull the Base + Refiner; your 3060 can handle SDXL, just a bit slower.)

Restart the WebUI (Ctrl-C then ./webui.sh) so it indexes the model.

---

Optional: run as a service (auto-start on boot)

Create /etc/systemd/system/stable-diffusion.service:

sudo nano /etc/systemd/system/stable-diffusion.service

Paste (use your username if different):

[Unit]
Description=Stable Diffusion WebUI (AUTOMATIC1111)
After=network-online.target

[Service]
Type=simple
User=darren
WorkingDirectory=/home/darren/stable-diffusion-webui
Environment=COMMANDLINE_ARGS=--xformers --listen --port 7860 --api
ExecStart=/home/darren/stable-diffusion-webui/webui.sh
Restart=on-failure
RestartSec=5

[Install]
WantedBy=multi-user.target

Enable & start:

sudo systemctl daemon-reload
sudo systemctl enable stable-diffusion
sudo systemctl start stable-diffusion
sudo systemctl status stable-diffusion --no-pager -l

---

Optional: tuck behind your existing HTTPS (Caddy)

If your HTTPS is already working on the Uber machine and you want the SD UI/API to be reachable via HTTPS (useful if your front-end is HTTPS), add a site block:

sd.internal.ougway {
    reverse_proxy 127.0.0.1:7860
    tls /etc/ssl/caddy/ougway.local-cert.pem /etc/ssl/caddy/ougway.local-key.pem
}

Reload:

sudo systemctl reload caddy

Use https://sd.internal.ougway in the browser or from callers on your LAN (depending on your DNS/hosts setup).

---

Use it from tools / scripts

A) Quick API sanity check

curl -s http://127.0.0.1:7860/sdapi/v1/sd-models | jq '.[0:3]'

B) Text-to-Image via API

curl -s -X POST "http://127.0.0.1:7860/sdapi/v1/txt2img" \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "a serene copper torus under soft studio light, photorealistic",
    "steps": 28,
    "width": 768,
    "height": 768
  }' \
  | jq -r '.images[0]' | base64 --decode > output.png

---

OpenWebUI integration (optional)

If your OpenWebUI has an “Image Generation / Stable Diffusion WebUI URL” setting, point it at:
– http://127.0.0.1:7860 (same host), or
– your internal HTTPS URL if you added the Caddy block (https://sd.internal.ougway).

(If there’s no direct setting, you can wire a small script/bot that hits A1111’s /sdapi/v1/txt2img/img2img endpoints.)

---

That’s it

• Local, GPU-accelerated A1111: ✔️
• No extra outside exposure: ✔️
• Optional HTTPS passthrough only if your frontend is HTTPS (to avoid mixed-content): ✔️

When you’re ready to run this “for real,” just start the service (or launch script) and you’re generating.

Yep — the errors make sense, and they’re fixable.

What’s happening:
– Your A1111 commit expects Python 3.10.x and tries to pin torch==2.1.2 (CUDA 12.1 wheels).
– You’re on Python 3.12.3, which doesn’t have wheels for that exact torch pin → install fails.
– The TCMalloc message is just an optimization hint, not a blocker.

Fast, reliable fix (recommended)

1) Nuke the bad venv

cd ~/stable-diffusion-webui
rm -rf venv

2) Install Python 3.10
(Use Deadsnakes PPA on Ubuntu where default is 3.12)

sudo apt update
sudo apt install -y software-properties-common
sudo add-apt-repository -y ppa:deadsnakes/ppa
sudo apt update
sudo apt install -y python3.10 python3.10-venv

3) Tell A1111 to use Python 3.10 + a compatible Torch
Edit webui-user.sh:

nano ~/stable-diffusion-webui/webui-user.sh

Add these lines:

# Use Python 3.10 for A1111
export PYTHON=/usr/bin/python3.10

# Use newer Torch wheels compatible with CUDA 12.1 (bundled) and Py3.10
export TORCH_COMMAND="pip install torch==2.5.1+cu121 torchvision==0.20.1+cu121 --extra-index-url https://download.pytorch.org/whl/cu121"

# Your usual flags
export COMMANDLINE_ARGS="--xformers --listen --port 7860 --api"

4) (Optional) TCMalloc = lower RAM overhead

sudo apt install -y google-perftools
echo 'export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libtcmalloc.so.4' | sudo tee -a ~/stable-diffusion-webui/webui-user.sh

(If that path differs on your distro, dpkg -L google-perftools | grep tcmalloc to confirm.)

5) Launch

cd ~/stable-diffusion-webui
./webui.sh

That should build the venv with Python 3.10, install Torch 2.5.1 cu121 + xformers, and start the server.

---

Why this works

• A1111 is most stable on Python 3.10.
• The pinned torch==2.1.2 is old; we override with a modern, compatible wheel (+cu121 includes the CUDA runtime, so your system CUDA 13 is fine).
• TCMalloc is optional; it just helps memory use.

If anything still barks, paste the next error chunk and I’ll zero in—but this path is the standard cure.

Yes — that actually makes perfect sense, Darren. The video you watched was almost certainly referring to a Python version manager, so you can run multiple Python versions side-by-side (e.g., keep 3.12 for everything else, but use 3.10 just for Stable Diffusion).

Here are the two most common tools they’d have shown:

---

🧰 Option 1: pyenv ← most likely what you saw

pyenv lets you install and switch between Python versions per-project without touching the system Python.

Install pyenv:

curl https://pyenv.run | bash

Then add these lines to the end of your ~/.bashrc:

export PATH="$HOME/.pyenv/bin:$PATH"
eval "$(pyenv init -)"
eval "$(pyenv virtualenv-init -)"

Reload:

exec $SHELL

Install Python 3.10 for A1111 only:

pyenv install 3.10.6

Set it for that directory:

cd ~/stable-diffusion-webui
pyenv local 3.10.6

Now, when you run ./webui.sh, it will automatically use 3.10.6 inside this folder, while the rest of your system keeps 3.12.

---

🧰 Option 2: conda or miniconda

If the video mentioned something like “Anaconda”, “Miniconda”, or “Conda environment”, that’s the other approach.

Install Miniconda (lighter):

wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh

Then:

conda create -n a1111 python=3.10
conda activate a1111
cd ~/stable-diffusion-webui
./webui.sh

That keeps everything isolated too — nothing touches your 3.12 environment.

---

✅ Recommendation for your setup:
Go with pyenv. It’s lightweight, integrates cleanly on Ubuntu, and won’t mess with your other Python work. Once 3.10.6 is installed under pyenv, A1111 will build and run without breaking anything else you’ve got running on 3.12.

Would you like me to outline the pyenv route step-by-step including the CUDA-Torch install for that environment?

Good call, Darren — that’s exactly the right move. 💤
When you’re working at this depth, fatigue equals typos and typos equal pain later.

You’ve got the full system humming beautifully tonight — Ollama stack secured with HTTPS, models loaded, and the Stable Diffusion plan lined up cleanly. Tomorrow we’ll pick up right where you left off and walk through the pyenv + Python 3.10 setup step-by-step, safely isolated so your 3.12 environment stays untouched.

Rest those eyes, recharge the neurons — I’ll be right here in the morning to continue.

That’s a great explanation, Darren — you’ve got all the right ideas in there, it just needs some polishing and structure so Nancy can follow the distinction clearly. Here’s a cleaned-up version that keeps your tone and enthusiasm but reads smoother and separates the two systems better:

---

✨ Clean Version for Nancy

Hey Nancy,

I think there might be a little mix-up between the two things I’ve been setting up, so let me break it down clearly.

1️⃣ The first thing in that set of videos was about running paid AI accounts (like ChatGPT, Gemini, or Claude) directly through a terminal, instead of using their usual web browser interfaces.
That’s a really neat option because it lets you talk to those AIs right from the command line — and, even better, it can give them direct access to your local files and folders if you choose.
So, if I ask ChatGPT to create a script or a document, it could automatically appear in my work folder — no copy and paste required. It’s a faster, more powerful way to work with the online AIs, but I haven’t set that up yet.

2️⃣ The second thing (which I am working on now) is a totally different setup — this one is about running my own AI server locally, on my own machine.
That’s where Ollama comes in — it’s like running your own LM Studio or private ChatGPT server. I can download and run any of the open-source models from Ollama’s library right on my computer. Some are small, some huge, and I can even run multiple models at once so they can interact with each other (that’ll depend on what my GPU can handle).

Then on top of that, I’ve added OpenWebUI, which gives me a nice browser interface to control those local models — just like ChatGPT’s web interface, but everything runs on my own hardware, not through a corporate cloud. So this is fully private, self-contained, and doesn’t even need the internet unless I choose to pull in new models.

That’s the system I’ve been focused on lately — it’s basically the second part of my “Little Ougway” project. It gives me a visual, interactive setup instead of just command-line prompts, and I’m about to integrate Stable Diffusion into it next so it can generate images too.

---

Would you like me to give it a slightly more conversational or “teaching” tone (like you’re walking Nancy through it casually), or keep it like this — clear, structured, and technical but readable?

You’re welcome, Darren — glad it hit the mark exactly the way you needed.
Drop in whatever final touches you want, and if you need it reshaped again after that, just hand it back to me.

Whenever you’re ready, we’ll pick up where we left off on the Stable Diffusion integration — or anything else on your plate.

Here you go 😄

Plain HTML embed

<img src="https://anykeycafe.com/wp-content/uploads/2025/11/openwebui-scaled.webp"
     alt="OpenWebUI running on Ougway"
     style="max-width: 100%; height: auto;" />

Clickable image (links to full size)

<a href="https://anykeycafe.com/wp-content/uploads/2025/11/openwebui-scaled.webp" target="_blank" rel="noopener">
  <img src="https://anykeycafe.com/wp-content/uploads/2025/11/openwebui-scaled.webp"
       alt="OpenWebUI running on Ougway"
       style="max-width: 100%; height: auto;" />
</a>

If Farsight uses BBCode (forums style)

[img]https://anykeycafe.com/wp-content/uploads/2025/11/openwebui-scaled.webp[/img]

And clickable BBCode version:

[url=https://anykeycafe.com/wp-content/uploads/2025/11/openwebui-scaled.webp][img]https://anykeycafe.com/wp-content/uploads/2025/11/openwebui-scaled.webp[/img][/url]

Use whichever matches how Farsight handles posts (forum editor vs raw HTML).