Noble Numbat update command — Part 1

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Source: ChatGPT data export · Conversation ID: 68d4964f-b1c8-832b-8999-076ad8fd51d4

Created: 2025-09-24T18:09:40-07:00 · Part 1 of 2

Darren2025-09-24T18:09:39-07:00

So, let's start with the basics. I have a brand new install of Noble Lombat. I need to do that pseudo-apt-update stuff. You want to put that on the screen for me so I can copy and paste it, please?

ChatGPT2025-09-24T18:09:40-07:00 · gpt-5

Here you go — the standard first step on a fresh Ubuntu 24.04 Noble Numbat install:

sudo apt update &amp;&amp; sudo apt upgrade -y

That will:
– apt update → refresh your package list from the repos.
– apt upgrade -y → upgrade all installed packages to the newest versions automatically.

If you also want to make sure new dependencies get pulled in when needed (more thorough than plain upgrade), you can use:

sudo apt full-upgrade -y

Would you like me to also include the command to remove any outdated/unused packages (autoremove)?

Darren2025-09-24T18:12:35-07:00

Great! Now we'd like to install the NVIDIA drivers for the video card.

ChatGPT2025-09-24T18:12:37-07:00 · gpt-5

Got it 👍 Since you’re on a clean Ubuntu 24.04 (Noble Numbat), here’s the safe, copy-paste way to get NVIDIA drivers installed:

1. Make sure your system is up to date

(You already did this, but good to repeat if you rebooted since.)

sudo apt update &amp;&amp; sudo apt upgrade -y

2. Enable the graphics drivers PPA (optional but recommended)

This gets you the latest tested NVIDIA drivers.

sudo add-apt-repository ppa:graphics-drivers/ppa -y
sudo apt update

3. Detect recommended NVIDIA driver

This command shows you which driver Ubuntu thinks is best:

ubuntu-drivers devices

Look for a line like:
driver : nvidia-driver-550 - distro non-free recommended

4. Install the recommended driver

For example, if it says nvidia-driver-550, then run:

sudo apt install -y nvidia-driver-550

5. Reboot

sudo reboot

After reboot, you can check if it’s loaded correctly:

nvidia-smi

👉 Do you want me to give you the exact install command for your card (3060, right?), or do you want to run ubuntu-drivers devices first and paste me the output? That way I can lock it to the correct driver version.

Darren2025-09-24T18:14:13-07:00

Well, the recommended video driver is now 580. Please note that for the records. I'll be rebooting momentarily.

ChatGPT2025-09-24T18:14:14-07:00 · gpt-5

Noted ✅ — your system is recommending NVIDIA driver 580 as the correct one for your card on Ubuntu 24.04 Noble Numbat.

After you reboot, just confirm with:

nvidia-smi

and you should see your GPU listed with driver 580.xx.

Do you want me to also log the exact install command you ran for future reference, so you don’t have to retrace steps later?

Darren2025-09-24T18:22:39-07:00

Okay, so we're up and running and we've got the latest version of NVIDIA SMI580 drivers as is recommended in their list. Now what I'd like to do is add in all the non-Ubuntu stuff for like MP3, MP4, FFmpeg, that kind of stuff. Is there a package that delivers all of that at once or?

ChatGPT2025-09-24T18:22:41-07:00 · gpt-5

Yep — Ubuntu has a “one-shot” package for that. On Noble Numbat (24.04) it’s still called:

sudo apt install -y ubuntu-restricted-extras

That pulls in:
– MP3, AAC, H.264, etc. codecs
– Microsoft fonts
– Java runtime bits
– Flash (legacy stub, but harmless)

For video & encoding (extra tools)

If you want FFmpeg and the GStreamer plugins that cover more formats, also run:

sudo apt install -y ffmpeg gstreamer1.0-libav gstreamer1.0-plugins-bad gstreamer1.0-plugins-ugly

That combo gives you:
– FFmpeg (for command-line conversion/encoding)
– “bad” and “ugly” GStreamer plugins (extra codecs like MPEG-2, H.265/HEVC, etc.)

👉 Question for you: do you want just the desktop playback stack (for watching/listening), or do you also want the full creator stack (encoding, transcoding, DVD/BluRay support, etc.)?

Darren2025-09-24T18:24:05-07:00

On this particular machine, I don't think I'm worried about the full creator stack. So if all we got was a desktop playback stack, that's okay.

Darren2025-09-24T18:26:21-07:00

Okay, so now I'd like to create the virtual environment, and I'm probably going to want to locate that folder under the Desktop folder.Probably stick with the same name, oUGWAY underscore VENV, ougway_venv.

ChatGPT2025-09-24T18:26:22-07:00 · gpt-5

Perfect 👍 Let’s set up a Python virtual environment right under your Desktop, with the name ougway_venv.

Here’s the step-by-step:

1. Make sure Python venv is available

sudo apt install -y python3-venv python3-pip

2. Navigate to Desktop

cd ~/Desktop

3. Create the virtual environment

python3 -m venv ougway_venv

4. Activate the virtual environment

source ougway_venv/bin/activate

When it’s active, your prompt will look something like:

(ougway_venv) darren@...

5. Deactivate (when you’re done)

deactivate

Now you’ve got an isolated Python environment ready to go under ~/Desktop/ougway_venv.

👉 Do you want me to also give you the one-liner that both creates and activates it in one shot, so you can skip a step?

Darren2025-09-24T18:27:21-07:00

Actually, I think I'm going to change that to the Documents folder, so I'm just going to add CD Documents and then proceed with the rest of the commands.

ChatGPT2025-09-24T18:27:21-07:00 · gpt-5

Exactly 👍 — just switch into ~/Documents before creating the venv. Here’s the adjusted sequence:

sudo apt install -y python3-venv python3-pip

cd ~/Documents
python3 -m venv ougway_venv
source ougway_venv/bin/activate

That will land your virtual environment in ~/Documents/ougway_venv instead of Desktop.

When you’re finished working inside it, just run:

deactivate

Do you want me to also set up a requirements.txt skeleton in there, so you can start dropping packages in right away?

Darren2025-09-24T18:35:22-07:00

Okay, now we need to download some stuff. The first thing being, I'd like to download the QN3 8B Q6 underscore K model. And where will we be downloading that model to? Right now I've got the VIM under documents. Should it go in the virtual environment folder? Say in a folder called models.

ChatGPT2025-09-24T18:35:23-07:00 · gpt-5

Good question. You generally don’t want to put large model files inside the virtual environment folder — venvs are meant for Python packages and dependencies, and mixing in multi-GB model weights makes them messy (and harder to reset/recreate later).

Here’s a cleaner layout that works well:

~/Documents/
├── ougway_venv/         ← your Python virtual environment
├── models/              ← store all your model files here
│   └── Qwen3-8B-Q6_K.gguf   (example model file)
└── projects/            ← (optional) for scripts, configs, etc.

So the workflow would be:

1. Make a models directory

mkdir -p ~/Documents/models

2. Move into it

cd ~/Documents/models

3. Download the model

If you’re grabbing the Qwen3 8B Q6_K (GGUF) file from Hugging Face, for example:

wget https://huggingface.co/Qwen/Qwen2.5-8B-Instruct-GGUF/resolve/main/Qwen2.5-8B-Instruct-Q6_K.gguf

(This URL may differ depending on the exact variant you want, but that’s the usual Hugging Face path.)

4. Reference the model when running llama.cpp or your loader

You’ll then point to it explicitly, e.g.:

./llama-cli -m ~/Documents/models/Qwen2.5-8B-Instruct-Q6_K.gguf ...

👉 That way, your virtual environment stays lightweight and portable, and your models live in their own space.

Do you want me to grab you the exact current Hugging Face link for Qwen3 8B Q6_K so you can paste it right in?

Darren2025-09-24T18:36:55-07:00

Okay, so it's telling me authentication failed. Why don't we just do the manual download? What's the link to their web page you gave me last time?

ChatGPT2025-09-24T18:36:57-07:00 · gpt-5

Here’s a page you can use to manually download Qwen3-8B-Q6_K:
→ https://huggingface.co/Qwen/Qwen3-8B-GGUF citeturn0search0

Also a direct variant file page:
→ Qwen3-8B-Q6_K.gguf citeturn0search3turn0search8turn0search5

If you like, I can grab you the direct wget link (or curl) to paste into your terminal (if authentication isn’t required) so you don’t have to click through. Do you want me to get that for you?

Darren2025-09-24T18:57:04-07:00

Okay, I've downloaded the Quantum 3 AP model, and I've placed it in the models folder on the main drive. I've also made a copy in a models folder on the storage drive, just in case this has to happen again. Better not, dammit. Now, we need to get the client. Can we download that directly, or do we need to do a manual?

ChatGPT2025-09-24T18:57:05-07:00 · gpt-5

Nice — good thinking keeping a backup copy of the model on your storage drive 👍.

For running Qwen3 (GGUF), the usual client is llama.cpp (fast C++ inference engine for GGUF models). You can grab and build it directly from GitHub — no need for manual download unless you prefer.

1. Install build tools

sudo apt install -y build-essential cmake git

2. Clone llama.cpp

cd ~/Documents
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp

3. Build it

make

That will create ./main and ./llama-cli binaries inside the folder.

4. Test with your Qwen3 model

Example (adjust path if your model is on the storage drive):

./llama-cli -m ~/Documents/models/Qwen3-8B-Q6_K.gguf -p &quot;Hello, world&quot;

👉 Question: do you want me to write this so the client binary lives inside your venv (so Python scripts can call it), or would you rather keep it as a standalone tool under ~/Documents/llama.cpp and just call it directly?

Darren2025-09-24T18:58:12-07:00

Well, I don't actually want anything in the Documents folder. I'd like it as a subfolder. So, why don't we create a subfolder in Documents called Client and store it in there.

ChatGPT2025-09-24T18:58:14-07:00 · gpt-5

Perfect — keeping it tidy makes life easier later. Let’s set that up with a dedicated Client folder inside ~/Documents:

1. Create the folder

mkdir -p ~/Documents/Client
cd ~/Documents/Client

2. Clone llama.cpp into it

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp

3. Build the client

make

This will produce binaries like llama-cli inside ~/Documents/Client/llama.cpp.

4. Test it with your Qwen3 model

(Adjust the path if you want to use the copy on your storage drive instead.)

./llama-cli -m ~/Documents/models/Qwen3-8B-Q6_K.gguf -p &quot;Hello, world&quot;

👉 Do you want me to also add this client’s bin folder to your PATH, so you can just type llama-cli anywhere without cd-ing into the Client folder?

Darren2025-09-24T18:58:43-07:00

darren@ougway:~/Documents/Client$ git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
Command 'git' not found, but can be installed with:
sudo apt install git
bash: cd: llama.cpp: No such file or directory
darren@ougway:~/Documents/Client$

ChatGPT2025-09-24T18:58:43-07:00 · gpt-5

Right — Ubuntu doesn’t ship git by default on a fresh install. Let’s fix that first, then retry:

1. Install git

sudo apt install -y git

2. Clone llama.cpp (again)

cd ~/Documents/Client
git clone https://github.com/ggerganov/llama.cpp.git

3. Move into the folder

cd llama.cpp

4. Build

make

That’ll put the llama-cli binary in ~/Documents/Client/llama.cpp/.

👉 Do you want me to also include the cmake/build-essential install in case your system doesn’t have those yet, or did you already pull them in earlier?

Darren2025-09-24T19:01:08-07:00

darren@ougway:~/Documents/Client/llama.cpp$ make
Makefile:6: *** Build system changed:
The Makefile build has been replaced by CMake.

For build instructions see:
https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md

. Stop.
darren@ougway:~/Documents/Client/llama.cpp$

ChatGPT2025-09-24T19:01:13-07:00 · gpt-5-thinking

Yep—the build switched to CMake. Here’s the updated, copy-paste flow.

A) Install build deps

sudo apt update
sudo apt install -y build-essential cmake

> (Optional for faster NVIDIA GPU builds later)

# CUDA toolkit for cuBLAS build (large download)
sudo apt install -y nvidia-cuda-toolkit

B) Configure & build (CPU-only first — safest)

cd ~/Documents/Client/llama.cpp
mkdir -p build &amp;&amp; cd build
cmake ..
cmake --build . -j

The binaries land in ~/Documents/Client/llama.cpp/build/bin/

C) Quick test

~/Documents/Client/llama.cpp/build/bin/llama-cli \
  -m ~/Documents/models/Qwen3-8B-Q6_K.gguf \
  -p &quot;Hello from Qwen3&quot;

(Optional) NVIDIA/cuBLAS build for speed

Only do this if you installed the CUDA toolkit above.

cd ~/Documents/Client/llama.cpp
rm -rf build &amp;&amp; mkdir build &amp;&amp; cd build
cmake -DLLAMA_CUDA=ON ..
cmake --build . -j

Then run the same way, using the binary in build/bin/llama-cli.

(Nice to have) Add to PATH

echo &#x27;export PATH=&quot;$HOME/Documents/Client/llama.cpp/build/bin:$PATH&quot;&#x27; &gt;&gt; ~/.bashrc
source ~/.bashrc

Want me to tune a good default run command for your GPU (batch size/threads/context) once llama-cli is built?

Darren2025-09-24T19:03:21-07:00

Well, I suppose you're going to need to download everything for the CUDA toolkit um if if that doesn't come with what you just gave me to install like PyTorch and all the rest of that um You know

ChatGPT2025-09-24T19:03:22-07:00 · gpt-5

Good thinking — there are really two layers here:

llama.cpp itself (you just built it with cmake …).
– If you build it with -DLLAMA_CUDA=ON, it will use CUDA/cuBLAS directly for inference.
– That only requires the CUDA toolkit (so it can find the cuBLAS libraries).
PyTorch / TensorFlow ecosystems
– These are needed only if you want to train, fine-tune, or run Python frameworks with GPU acceleration.
– They aren’t needed for running GGUF models with llama.cpp — it’s self-contained.

Minimum for llama.cpp + GPU

sudo apt install -y nvidia-cuda-toolkit

Then rebuild:

cd ~/Documents/Client/llama.cpp
rm -rf build &amp;&amp; mkdir build &amp;&amp; cd build
cmake -DLLAMA_CUDA=ON ..
cmake --build . -j

That’s enough to run your Qwen3 model accelerated by your NVIDIA card. ✅

If you also want PyTorch (for other projects, LoRA, RAG, etc.)

Inside your ougway_venv virtual environment:

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124

(The cu124 wheel matches CUDA 12.4, which is what Ubuntu 24.04 provides with nvidia-cuda-toolkit.)

👉 So, question for you: do you want to keep this machine lean, just llama.cpp + CUDA, or should we also bring in PyTorch right now so it’s ready for later experiments?

Darren2025-09-24T19:42:40-07:00

arren@ougway:~/Documents/Client/llama.cpp$ cd ~/Documents/Client/llama.cpp
rm -rf build && mkdir build && cd build
cmake -DLLAMA_CUDA=ON ..
cmake –build . -j
— The C compiler identification is GNU 13.3.0
— The CXX compiler identification is GNU 13.3.0
— Detecting C compiler ABI info
— Detecting C compiler ABI info – done
— Check for working C compiler: /usr/bin/cc – skipped
— Detecting C compile features
— Detecting C compile features – done
— Detecting CXX compiler ABI info
— Detecting CXX compiler ABI info – done
— Check for working CXX compiler: /usr/bin/c++ – skipped
— Detecting CXX compile features
— Detecting CXX compile features – done
CMAKE_BUILD_TYPE=Release
— Found Git: /usr/bin/git (found version "2.43.0")
CMake Warning at CMakeLists.txt:125 (message):
LLAMA_CUDA is deprecated and will be removed in the future.

Use GGML_CUDA instead

Call Stack (most recent call first):
CMakeLists.txt:131 (llama_option_depr)

— The ASM compiler identification is GNU
— Found assembler: /usr/bin/cc
— Performing Test CMAKE_HAVE_LIBC_PTHREAD
— Performing Test CMAKE_HAVE_LIBC_PTHREAD – Success
— Found Threads: TRUE
— Warning: ccache not found – consider installing it for faster compilation or disable this warning with GGML_CCACHE=OFF
— CMAKE_SYSTEM_PROCESSOR: x86_64
— GGML_SYSTEM_ARCH: x86
— Including CPU backend
— Found OpenMP_C: -fopenmp (found version "4.5")
— Found OpenMP_CXX: -fopenmp (found version "4.5")
— Found OpenMP: TRUE (found version "4.5")
— x86 detected
— Adding CPU backend variant ggml-cpu: -march=native
— Found CUDAToolkit: /usr/include (found version "12.0.140")
— CUDA Toolkit found
— Using CUDA architectures: native
— The CUDA compiler identification is NVIDIA 12.0.140
— Detecting CUDA compiler ABI info
— Detecting CUDA compiler ABI info – done
— Check for working CUDA compiler: /usr/bin/nvcc – skipped
— Detecting CUDA compile features
— Detecting CUDA compile features – done
— CUDA host compiler is GNU 12.4.0
— Including CUDA backend
— ggml version: 0.9.0-dev
— ggml commit: 4ae88d07
— Could NOT find CURL (missing: CURL_LIBRARY CURL_INCLUDE_DIR)
CMake Error at common/CMakeLists.txt:85 (message):
Could NOT find CURL. Hint: to disable this feature, set -DLLAMA_CURL=OFF

— Configuring incomplete, errors occurred!
gmake: Makefile: No such file or directory
gmake: *** No rule to make target 'Makefile'. Stop.
darren@ougway:~/Documents/Client/llama.cpp/build$

ChatGPT2025-09-24T19:42:42-07:00 · gpt-5-thinking

You’re almost there—the build failed because the libcurl dev headers aren’t installed. Two quick fixes:

Option A (recommended): install libcurl dev

sudo apt update
sudo apt install -y libcurl4-openssl-dev ccache
cd ~/Documents/Client/llama.cpp
rm -rf build &amp;&amp; mkdir build &amp;&amp; cd build
cmake -DGGML_CUDA=ON ..
cmake --build . -j

Option B (skip curl feature)

If you don’t need llama.cpp’s built-in URL/download helpers:

cd ~/Documents/Client/llama.cpp
rm -rf build &amp;&amp; mkdir build &amp;&amp; cd build
cmake -DGGML_CUDA=ON -DLLAMA_CURL=OFF ..
cmake --build . -j

> Note: Use -DGGML_CUDA=ON (new flag); LLAMA_CUDA is deprecated.

Run test (after a successful build)

~/Documents/Client/llama.cpp/build/bin/llama-cli \
  -m ~/Documents/models/Qwen3-8B-Q6_K.gguf \
  -p &quot;Hello from Qwen3&quot;

If you want a faster compile, you can also add Ninja:

sudo apt install -y ninja-build
cd ~/Documents/Client/llama.cpp
rm -rf build &amp;&amp; cmake -S . -B build -DGGML_CUDA=ON -G Ninja
cmake --build build -j

Darren2025-09-24T19:45:49-07:00

Do I need to install PyTorch inside the virtual environment?darren@ougway:~/Desktop$ pip install torch torchvision torchaudio –index-url https://download.pytorch.org/whl/cu124
error: externally-managed-environment

× This environment is externally managed
╰─> To install Python packages system-wide, try apt install
python3-xyz, where xyz is the package you are trying to
install.

If you wish to install a non-Debian-packaged Python package,
create a virtual environment using python3 -m venv path/to/venv.
Then use path/to/venv/bin/python and path/to/venv/bin/pip. Make
sure you have python3-full installed.

If you wish to install a non-Debian packaged Python application,
it may be easiest to use pipx install xyz, which will manage a
virtual environment for you. Make sure you have pipx installed.

See /usr/share/doc/python3.12/README.venv for more information.

note: If you believe this is a mistake, please contact your Python installation or OS distribution provider. You can override this, at the risk of breaking your Python installation or OS, by passing –break-system-packages.
hint: See PEP 668 for the detailed specification.
darren@ougway:~/Desktop$

ChatGPT2025-09-24T19:45:55-07:00 · gpt-5

That error is Ubuntu’s PEP 668 safeguard: it stops you from installing Python packages system-wide with pip (to avoid breaking the system Python).

✅ The right move is to install PyTorch inside your virtual environment — not globally.

Since you already made ~/Documents/ougway_venv, just do this:

1. Activate your venv

source ~/Documents/ougway_venv/bin/activate

Your prompt should change to:

(ougway_venv) darren@...

2. Install PyTorch with CUDA 12.4 support

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124

3. Verify

python -c &quot;import torch; print(torch.__version__, torch.cuda.is_available())&quot;

You should see the version and True if CUDA is working.

👉 So yes: install PyTorch inside the virtual environment — that’s the safe and expected way on Ubuntu Noble.

Do you want me to also give you the command for installing transformers + accelerate in the same venv, in case you

Darren2025-09-24T19:47:20-07:00

Yes.

ChatGPT2025-09-24T19:47:24-07:00 · gpt-5

Alright 👍 — here’s the continuation so your venv has the full stack ready for inference / fine-tuning work:

1. Make sure your venv is active

source ~/Documents/ougway_venv/bin/activate

2. Install PyTorch (if not already done)

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124

3. Install Hugging Face + utilities

pip install transformers accelerate safetensors datasets

4. Optional extras (nice to have)

pip install bitsandbytes sentencepiece

bitsandbytes → lightweight 8-bit/4-bit quantization.
sentencepiece → tokenizer for models like LLaMA/Qwen.

5. Verify installation

python -c &quot;import torch, transformers; print(torch.__version__, torch.cuda.is_available(), transformers.__version__)&quot;

Should print PyTorch version, True for CUDA, and Transformers version. ✅

👉 Do you want me to also pin this environment to a requirements.txt file in your venv folder so you can recreate it later without typing all of this again?

Darren2025-09-24T19:59:25-07:00

(ougway_venv) darren@ougway:~/Desktop$ python -c "import torch, transformers; print(torch.version, torch.cuda.is_available(), transformers.version)"
2.6.0+cu124 True 4.56.2
(ougway_venv) darren@ougway:~/Desktop$ And the client is done building.

ChatGPT2025-09-24T19:59:26-07:00 · gpt-5

Nice — you’re in business 🎉

PyTorch: 2.6.0+cu124 ✅
CUDA available: True ✅
Transformers: 4.56.2 ✅
llama.cpp client: build complete ✅

At this point you’ve got:
– A working venv (ougway_venv) with PyTorch + Hugging Face tools.
– A GPU-enabled llama.cpp client in ~/Documents/Client/llama.cpp/build/bin/.
– Your Qwen3-8B-Q6_K model ready in ~/Documents/models/.

Quick sanity check with both paths

llama.cpp binary run

~/Documents/Client/llama.cpp/build/bin/llama-cli \
  -m ~/Documents/models/Qwen3-8B-Q6_K.gguf \
  -p &quot;Hello, world from Qwen3&quot;

Transformers/PyTorch run (Python)

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = &quot;Qwen/Qwen2.5-8B-Instruct&quot;  # HF repo
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map=&quot;auto&quot;, torch_dtype=torch.float16)

inputs = tokenizer(&quot;Hello, world from Qwen3&quot;, return_tensors=&quot;pt&quot;).to(model.device)
outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

👉 Do you want to keep going with a GPU-tuned llama.cpp run command (threads, batch, context) optimized for your RTX 3060, or do you want to test first with the defaults?

Darren2025-09-24T20:00:50-07:00

(ougway_venv) darren@ougway:~/Desktop$ from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "Qwen/Qwen2.5-8B-Instruct" # HF repo
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.float16)

inputs = tokenizer("Hello, world from Qwen3", return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Command 'from' not found, but can be installed with:
sudo apt install mailutils
Command 'import' not found, but can be installed with:
sudo apt install graphicsmagick-imagemagick-compat # version 1.4+really1.3.42-1, or
sudo apt install imagemagick-6.q16 # version 8:6.9.11.60+dfsg-1.6ubuntu1
sudo apt install imagemagick-6.q16hdri # version 8:6.9.11.60+dfsg-1.6ubuntu1
model_id: command not found
bash: syntax error near unexpected token (' bash: syntax error near unexpected token('
bash: syntax error near unexpected token (' bash: syntax error near unexpected token('
bash: syntax error near unexpected token `tokenizer.decode'
(ougway_venv) darren@ougway:~/Desktop$

ChatGPT2025-09-24T20:00:52-07:00 · gpt-5-thinking

You pasted Python into the bash shell—that’s why it tried to run from as a command. Run it inside Python, not bash.

Here are two clean, copy-paste ways. Use whichever you like.

Option A — Quick one-file test (loads model in 8-bit to fit your 12 GB GPU)

cat &gt; ~/Documents/test_qwen.py &lt;&lt; &#x27;PY&#x27;
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
import torch

model_id = &quot;Qwen/Qwen2.5-8B-Instruct&quot;

# 8-bit to fit on RTX 3060 12GB; falls back to CPU for leftovers if needed
bnb = BitsAndBytesConfig(load_in_8bit=True)

tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map=&quot;auto&quot;,
    quantization_config=bnb
)

inp = tok(&quot;Hello, world from Qwen3&quot;, return_tensors=&quot;pt&quot;).to(model.device)
out = model.generate(**inp, max_new_tokens=60)
print(tok.decode(out[0], skip_special_tokens=True))
PY

# run it (make sure your venv is active)
python3 ~/Documents/test_qwen.py

Option B — Open a Python REPL and paste code

python3

Then paste:

from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
import torch

model_id = &quot;Qwen/Qwen2.5-8B-Instruct&quot;
tok = AutoTokenizer.from_pretrained(model_id)

bnb = BitsAndBytesConfig(load_in_8bit=True)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map=&quot;auto&quot;, quantization_config=bnb)

inp = tok(&quot;Hello, world from Qwen3&quot;, return_tensors=&quot;pt&quot;).to(model.device)
out = model.generate(**inp, max_new_tokens=60)
print(tok.decode(out[0], skip_special_tokens=True))

Exit with Ctrl+D when done.

Notes:
– You already installed bitsandbytes; the code above uses it so the 8B model fits your RTX 3060 12 GB. Plain FP16 would likely OOM.
– Keep your venv active before running (source ~/Documents/ougway_venv/bin/activate).

Darren2025-09-24T20:02:55-07:00

Response from the client test, and should I close this window before I run that Python code?arren@ougway:~/Documents/Client/llama.cpp/build$ ~/Documents/Client/llama.cpp/build/bin/llama-cli \
-m ~/Documents/models/Qwen3-8B-Q6_K.gguf \
-p "Hello, world from Qwen3"
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 1 CUDA devices:
Device 0: NVIDIA GeForce RTX 3060, compute capability 8.6, VMM: yes
build: 6570 (4ae88d07) with cc (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0 for x86_64-linux-gnu
main: llama backend init
main: load the model and apply lora adapter, if any
llama_model_load_from_file_impl: using device CUDA0 (NVIDIA GeForce RTX 3060) (0000:01:00.0) – 11466 MiB free
llama_model_loader: loaded meta data with 28 key-value pairs and 399 tensors from /home/darren/Documents/models/Qwen3-8B-Q6_K.gguf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: – kv 0: general.architecture str = qwen3
llama_model_loader: – kv 1: general.type str = model
llama_model_loader: – kv 2: general.name str = Qwen3 8B Instruct
llama_model_loader: – kv 3: general.finetune str = Instruct
llama_model_loader: – kv 4: general.basename str = Qwen3
llama_model_loader: – kv 5: general.size_label str = 8B
llama_model_loader: – kv 6: qwen3.block_count u32 = 36
llama_model_loader: – kv 7: qwen3.context_length u32 = 40960
llama_model_loader: – kv 8: qwen3.embedding_length u32 = 4096
llama_model_loader: – kv 9: qwen3.feed_forward_length u32 = 12288
llama_model_loader: – kv 10: qwen3.attention.head_count u32 = 32
llama_model_loader: – kv 11: qwen3.attention.head_count_kv u32 = 8
llama_model_loader: – kv 12: qwen3.rope.freq_base f32 = 1000000.000000
llama_model_loader: – kv 13: qwen3.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: – kv 14: qwen3.attention.key_length u32 = 128
llama_model_loader: – kv 15: qwen3.attention.value_length u32 = 128
llama_model_loader: – kv 16: tokenizer.ggml.model str = gpt2
llama_model_loader: – kv 17: tokenizer.ggml.pre str = qwen2
llama_model_loader: – kv 18: tokenizer.ggml.tokens arr[str,151936] = ["!", "\"", "#", "$", "%", "&", "'", …
llama_model_loader: – kv 19: tokenizer.ggml.token_type arr[i32,151936] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
llama_model_loader: – kv 20: tokenizer.ggml.merges arr[str,151387] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",…
llama_model_loader: – kv 21: tokenizer.ggml.eos_token_id u32 = 151645
llama_model_loader: – kv 22: tokenizer.ggml.padding_token_id u32 = 151643
llama_model_loader: – kv 23: tokenizer.ggml.bos_token_id u32 = 151643
llama_model_loader: – kv 24: tokenizer.ggml.add_bos_token bool = false
llama_model_loader: – kv 25: tokenizer.chat_template str = {%- if tools %}\n {{- '<|im_start|>…
llama_model_loader: – kv 26: general.quantization_version u32 = 2
llama_model_loader: – kv 27: general.file_type u32 = 18
llama_model_loader: – type f32: 145 tensors
llama_model_loader: – type q6_K: 254 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = Q6_K
print_info: file size = 6.26 GiB (6.56 BPW)
load: printing all EOG tokens:
load: – 151643 ('<|endoftext|>')
load: – 151645 ('<|im_end|>')
load: – 151662 ('<|fim_pad|>')
load: – 151663 ('<|repo_name|>')
load: – 151664 ('<|file_sep|>')
load: special tokens cache size = 26
load: token to piece cache size = 0.9311 MB
print_info: arch = qwen3
print_info: vocab_only = 0
print_info: n_ctx_train = 40960
print_info: n_embd = 4096
print_info: n_layer = 36
print_info: n_head = 32
print_info: n_head_kv = 8
print_info: n_rot = 128
print_info: n_swa = 0
print_info: is_swa_any = 0
print_info: n_embd_head_k = 128
print_info: n_embd_head_v = 128
print_info: n_gqa = 4
print_info: n_embd_k_gqa = 1024
print_info: n_embd_v_gqa = 1024
print_info: f_norm_eps = 0.0e+00
print_info: f_norm_rms_eps = 1.0e-06
print_info: f_clamp_kqv = 0.0e+00
print_info: f_max_alibi_bias = 0.0e+00
print_info: f_logit_scale = 0.0e+00
print_info: f_attn_scale = 0.0e+00
print_info: n_ff = 12288
print_info: n_expert = 0
print_info: n_expert_used = 0
print_info: causal attn = 1
print_info: pooling type = -1
print_info: rope type = 2
print_info: rope scaling = linear
print_info: freq_base_train = 1000000.0
print_info: freq_scale_train = 1
print_info: n_ctx_orig_yarn = 40960
print_info: rope_finetuned = unknown
print_info: model type = 8B
print_info: model params = 8.19 B
print_info: general.name = Qwen3 8B Instruct
print_info: vocab type = BPE
print_info: n_vocab = 151936
print_info: n_merges = 151387
print_info: BOS token = 151643 '<|endoftext|>'
print_info: EOS token = 151645 '<|im_end|>'
print_info: EOT token = 151645 '<|im_end|>'
print_info: PAD token = 151643 '<|endoftext|>'
print_info: LF token = 198 'Ċ'
print_info: FIM PRE token = 151659 '<|fim_prefix|>'
print_info: FIM SUF token = 151661 '<|fim_suffix|>'
print_info: FIM MID token = 151660 '<|fim_middle|>'
print_info: FIM PAD token = 151662 '<|fim_pad|>'
print_info: FIM REP token = 151663 '<|repo_name|>'
print_info: FIM SEP token = 151664 '<|file_sep|>'
print_info: EOG token = 151643 '<|endoftext|>'
print_info: EOG token = 151645 '<|im_end|>'
print_info: EOG token = 151662 '<|fim_pad|>'
print_info: EOG token = 151663 '<|repo_name|>'
print_info: EOG token = 151664 '<|file_sep|>'
print_info: max token length = 256
load_tensors: loading model tensors, this can take a while… (mmap = true)
load_tensors: offloading 36 repeating layers to GPU
load_tensors: offloading output layer to GPU
load_tensors: offloaded 37/37 layers to GPU
load_tensors: CUDA0 model buffer size = 5921.78 MiB
load_tensors: CPU_Mapped model buffer size = 486.86 MiB
……………………………………………………………………………
llama_context: constructing llama_context
llama_context: n_seq_max = 1
llama_context: n_ctx = 4096
llama_context: n_ctx_per_seq = 4096
llama_context: n_batch = 2048
llama_context: n_ubatch = 512
llama_context: causal_attn = 1
llama_context: flash_attn = auto
llama_context: kv_unified = false
llama_context: freq_base = 1000000.0
llama_context: freq_scale = 1
llama_context: n_ctx_per_seq (4096) < n_ctx_train (40960) — the full capacity of the model will not be utilized
llama_context: CUDA_Host output buffer size = 0.58 MiB
llama_kv_cache: CUDA0 KV buffer size = 576.00 MiB
llama_kv_cache: size = 576.00 MiB ( 4096 cells, 36 layers, 1/1 seqs), K (f16): 288.00 MiB, V (f16): 288.00 MiB
llama_context: Flash Attention was auto, set to enabled
llama_context: CUDA0 compute buffer size = 304.75 MiB
llama_context: CUDA_Host compute buffer size = 16.01 MiB
llama_context: graph nodes = 1267
llama_context: graph splits = 2
common_init_from_params: added <|endoftext|> logit bias = -inf
common_init_from_params: added <|im_end|> logit bias = -inf
common_init_from_params: added <|fim_pad|> logit bias = -inf
common_init_from_params: added <|repo_name|> logit bias = -inf
common_init_from_params: added <|file_sep|> logit bias = -inf
common_init_from_params: setting dry_penalty_last_n to ctx_size = 4096
common_init_from_params: warming up the model with an empty run – please wait … (–no-warmup to disable)
main: llama threadpool init, n_threads = 4
main: chat template is available, enabling conversation mode (disable it with -no-cnv)
*** User-specified prompt will pre-start conversation, did you mean to set –system-prompt (-sys) instead?
main: chat template example:
<|im_start|>system
You are a helpful assistant<|im_end|>
<|im_start|>user
Hello<|im_end|>
<|im_start|>assistant
Hi there<|im_end|>
<|im_start|>user
How are you?<|im_end|>
<|im_start|>assistant

main: interactive mode on.
sampler seed: 439790559
sampler params:
repeat_last_n = 64, repeat_penalty = 1.000, frequency_penalty = 0.000, presence_penalty = 0.000
dry_multiplier = 0.000, dry_base = 1.750, dry_allowed_length = 2, dry_penalty_last_n = 4096
top_k = 40, top_p = 0.950, min_p = 0.050, xtc_probability = 0.000, xtc_threshold = 0.100, typical_p = 1.000, top_n_sigma = -1.000, temp = 0.800
mirostat = 0, mirostat_lr = 0.100, mirostat_ent = 5.000
sampler chain: logits -> logit-bias -> penalties -> dry -> top-n-sigma -> top-k -> typical -> top-p -> min-p -> xtc -> temp-ext -> dist
generate: n_ctx = 4096, n_batch = 2048, n_predict = -1, n_keep = 0

== Running in interactive mode. ==
– Press Ctrl+C to interject at any time.
– Press Return to return control to the AI.
– To return control without starting a new line, end your input with '/'.
– If you want to submit another line, end your input with '\'.
– Not using system message. To change it, set a different value via -sys PROMPT

user
Hello, world from Qwen3
assistant
<think>
Okay, the user sent "Hello, world from Qwen3". I need to respond appropriately. First, I should acknowledge their message and confirm that they're interacting with Qwen3. Since the user might be testing or verifying the model's response, I should keep it friendly and offer further assistance. Let me make sure the response is in English and matches the tone of a helpful assistant. Also, check for any typos. Alright, something like "Hello! I'm Qwen3, and I'm here to help. How can I assist you today?" That should work.
</think>

Hello! I'm Qwen3, and I'm here to help. How can I assist you today? 😊

ChatGPT2025-09-24T20:02:56-07:00 · gpt-5

That’s a perfect test run ✅ — your RTX 3060 loaded the full Qwen3-8B-Q6_K quantized model into GPU memory and is responding interactively through llama.cpp.

CUDA detected and all layers offloaded → GPU acceleration working.
Prompt processed successfully → you got a coherent assistant-style reply.
Interactive mode is live → you can type directly into that window and keep chatting with the model.

About your question:

> “Should I close this window before I run that Python code?”

Not necessary.
– The llama.cpp client you just ran is independent of Python.
– You can leave it running in one terminal tab/window if you want to keep chatting with Qwen3 locally.
– To run the Python/Hugging Face code, just open another terminal tab/window, activate your venv, and run the script from there.

That way you can test both side by side:
– llama.cpp (fast GGUF runtime).
– Transformers + PyTorch (HF ecosystem, quantization, training hooks).

👉 Do you want me to give you a GPU-tuned llama.cpp command (threads, batch size, context window) optimized for the 3060, so you don’t need to rely on defaults each time?

Darren2025-09-24T20:04:03-07:00

(ougway_venv) darren@ougway:~/Desktop$ from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
import torch

model_id = "Qwen/Qwen2.5-8B-Instruct"
tok = AutoTokenizer.from_pretrained(model_id)

bnb = BitsAndBytesConfig(load_in_8bit=True)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", quantization_config=bnb)

inp = tok("Hello, world from Qwen3", return_tensors="pt").to(model.device)
out = model.generate(**inp, max_new_tokens=60)
print(tok.decode(out[0], skip_special_tokens=True))
Command 'from' not found, but can be installed with:
sudo apt install mailutils
Command 'import' not found, but can be installed with:
sudo apt install graphicsmagick-imagemagick-compat # version 1.4+really1.3.42-1, or
sudo apt install imagemagick-6.q16 # version 8:6.9.11.60+dfsg-1.6ubuntu1
sudo apt install imagemagick-6.q16hdri # version 8:6.9.11.60+dfsg-1.6ubuntu1
model_id: command not found
bash: syntax error near unexpected token (' bash: syntax error near unexpected token('
bash: syntax error near unexpected token (' bash: syntax error near unexpected token('
bash: syntax error near unexpected token (' bash: syntax error near unexpected tokentok.decode'
(ougway_venv) darren@ougway:~/Desktop$

ChatGPT2025-09-24T20:04:04-07:00 · gpt-5

I see what happened again 👍 — you pasted Python code directly into the bash shell. Bash doesn’t understand from or import.

Here’s how to run it correctly:

Option 1 — Run Python interactively

python3

Then paste the Python code inside the Python prompt (>>>):

from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
import torch

model_id = &quot;Qwen/Qwen2.5-8B-Instruct&quot;
tok = AutoTokenizer.from_pretrained(model_id)

bnb = BitsAndBytesConfig(load_in_8bit=True)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map=&quot;auto&quot;, quantization_config=bnb)

inp = tok(&quot;Hello, world from Qwen3&quot;, return_tensors=&quot;pt&quot;).to(model.device)
out = model.generate(**inp, max_new_tokens=60)
print(tok.decode(out[0], skip_special_tokens=True))