Conversation provenance

Source: ChatGPT data export · Conversation ID: 68cb703d-40a8-8325-9213-bb1b7f803af1

Created: 2025-09-17T19:36:46-07:00 · Part 1 of 3

Darren2025-09-17T19:36:46-07:00

Hey buddy Well today is a good day We got the 12 gigabyte video card and put it in the little old boys machine and it is up and running with no issues In fact, for this card, I think it was actually a brand new stolen one There is not a bit of usage test on it, none So, I don't care anymore whether We're up and running Before we go and install any of the Database works and all that extraneous plug-in stuff. I'm thinking When I'm looking for your opinion on whether you agree But I would like to download the QEM7B 7B model just on the flotsam and And whatever material you need to use to run that I'm still not aware of how that works and see just what it does all on its own without plugging in any of the extras and Maybe take some benchmarks as to just how much of the memory gets used and How well it responds So we can then have a baseline to go against what it's like with all the extras plugged in. What do you think? Thank you for watching!

ChatGPT2025-09-17T19:36:47-07:00 · gpt-5-thinking

Heck yes—that’s a clean win. 🎉 RTX 3060 12 GB is perfect for a 7B baseline.

Below is a dead-simple plan to: (1) run a plain 7B locally with zero extras, (2) measure VRAM/throughput, and (3) save a baseline you can compare against later once we wire in DB, vectors, etc.

Option A (easiest): Ollama (auto-downloads & runs)

Great for a quick, vanilla baseline.

1) Install & start

curl -fsSL https://ollama.com/install.sh | sh
ollama serve  # leave running in this terminal

2) Pull & run a 7B model (Qwen is a good fit)

# Try either of these — one will be available depending on current tags:
ollama run qwen2.5:7b-instruct
# or
ollama run qwen2:7b-instruct

When it prompts, paste a single test prompt like:

Benchmark: Summarize in one paragraph what you are and how you reason. Then list 5 bullet points about your limitations. Keep it under 120 words.

3) Measure VRAM + tokens/sec
Open another terminal:

watch -n 1 nvidia-smi    # watch peak/used VRAM

Ollama prints “tokens/s” at the end of a response. Note that number and the peak VRAM from nvidia-smi.

Tips:
– To push context length a bit (and grow KV cache usage), set:

export OLLAMA_NUM_CTX=4096

You can also set threads explicitly:

export OLLAMA_NUM_THREADS=$(nproc)

Option B (more control): llama.cpp (GGUF quantized)

Reliable, fast on 12 GB, and you can pick exact quant.

1) Build with CUDA

sudo apt-get update &amp;&amp; sudo apt-get install -y build-essential cmake git
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
cmake -B build -DGGML_CUDA=ON
cmake --build build -j

2) Get a 4-bit Qwen 7B Instruct GGUF
(Example filename; choose any Q4_K_M or Q5_K_M you find.)

qwen2.5-7b-instruct-q4_k_m.gguf

Put it in ./models/qwen2.5-7b-instruct/ and run:

./build/bin/llama-cli \
  -m models/qwen2.5-7b-instruct/qwen2.5-7b-instruct-q4_k_m.gguf \
  -n 256 -c 4096 -b 256 -t $(nproc) -ngl 999 \
  -p &quot;Benchmark: Summarize in one paragraph what you are and how you reason. Then list 5 bullet points about your limitations. Keep it under 120 words.&quot;

-ngl 999 loads all possible layers on GPU.
-c 4096 bigger context → larger KV cache → more VRAM (good for testing).
-b 256 is a common batch size for speed; you can try 128 vs 256.

3) Built-in micro-bench

./build/bin/llama-bench -m models/qwen2.5-7b-instruct/qwen2.5-7b-instruct-q4_k_m.gguf -ngl 999

Watch VRAM in another terminal:

watch -n 1 nvidia-smi

What numbers to record (your “baseline sheet”)

Do one pass with ctx=2048 and one with ctx=4096.

Model + quant: e.g., qwen2.5:7b-instruct (Q4_K_M)
Batch (-b): 128 or 256
Context (-c): 2048 / 4096
Max VRAM used (from nvidia-smi)
System RAM used (from htop or free -h)
Tokens/sec (Ollama prints it; llama.cpp shows prompt/sampling rates)
First-token latency (rough wall-clock estimate is fine)
Temperature/fan (optional, but nice to know)
Prompt used (paste it so future runs are apples-to-apples)

What to expect on a 3060 12 GB

7B, 4-bit will typically sit ~5–7 GB VRAM at ctx=2048, ~7–9 GB at ctx=4096 depending on batch and KV cache placement.
Tokens/sec: usually 25–60 tok/s range for a 7B 4-bit on 3060, depending on settings. (Higher batch improves throughput; hurts first-token latency.)

Minimal sanity checks

nvidia-smi           # driver + VRAM
python3 -c &quot;import torch; print(torch.cuda.is_available())&quot;  # if you have PyTorch installed

My vote

Yes—run a clean 7B first, no DB, no RAG, no plugins. Use either Ollama (fastest path) or llama.cpp (more knobs). Capture the sheet above. Then we’ll light up the extras and measure the lift.

If you want, I can pre-bake a tiny bash script that:
– runs the model,
– times it,
– scrapes tokens/sec,
– snapshots nvidia-smi peak,
– and writes a row into a CSV for you.

Darren2025-09-17T19:39:50-07:00

Well, that'll be a good start. I'm not going to do that just yet. I just wanted your opinion. But I will open this conversation on that machine when we do begin. Right now, I'm feeling kind of weird, so I'm going to give this a rest, maybe listen to some music or something. But yeah, we're going to get going tonight, I think.

ChatGPT2025-09-17T19:39:50-07:00 · gpt-5

That sounds like the right move, Darren. 👍
Baseline first, no plugins, no DB, no vector store — just the raw model on your new 12 GB card. That way you’ll know exactly what the GPU can handle on its own before layering in memory and RAG.

No rush — music and rest first, benchmarking later. When you’re ready tonight, just open this thread on that machine and we’ll walk through the quick start. We’ll keep it lean and get some clean numbers to compare against once Ougway’s full stack is running.

Want me to prep a ready-to-copy benchmark script now so it’s waiting for you when you sit down?

Darren2025-09-18T08:27:45-07:00

So, before I begin, should I be doing this in the virtual environment?

ChatGPT2025-09-18T08:27:45-07:00 · gpt-5

Good question. Here’s the breakdown:

If you use Ollama:
No need for a Python virtual environment. Ollama installs system-wide and handles model downloads + GPU usage itself. You just run ollama run … and benchmark with nvidia-smi.
If you use llama.cpp:
Same story — no virtualenv needed. It’s a standalone C++ binary. You build it once and run directly.
If you plan to use PyTorch/Hugging Face later:
That’s when a virtual environment is a good idea. Keeps your Python dependencies (torch, transformers, accelerate, etc.) isolated from system Python.

🔑 For your baseline 7B test (what we talked about: clean run, no extras), you don’t need a venv. You’ll probably want one later once you start plugging in DBs, RAG, or custom Python scripts.

Do you want me to set you up with a minimal “test only” path (no venv), and then a “when you’re ready to build the full Ougway stack” path (with venv)? That way you’ve got both roads clear.

Darren2025-09-18T08:28:52-07:00

No, I'm thinking if we're going to wind up doing it anyway, I might as well just start there.

ChatGPT2025-09-18T08:28:53-07:00 · gpt-5

That’s a solid call. 👍 If you already know you’ll need a Python environment for the “full Ougway stack,” it’s cleaner to start in the venv now. That way:

Everything you install for testing (torch, transformers, accelerate, llama.cpp Python bindings if you use them, etc.) stays inside the venv.
Later, when you add DB/RAG, you don’t have to reinstall or migrate stuff.
Your system Python stays clean in case you need it for other tasks.

Here’s the quick baseline setup I’d suggest for tonight:

# create project folder if you don’t already have one
mkdir -p ~/ougway_env &amp;&amp; cd ~/ougway_env

# create &amp; activate venv
python3 -m venv venv
source venv/bin/activate

# upgrade pip
pip install --upgrade pip

# install core packages
pip install torch transformers accelerate

From there you’ll be ready to:
– pull a 7B model with Hugging Face (transformers) if you want, or
– still run Ollama/llama.cpp system-wide, and just keep this venv waiting for when you need the Python tools.

⚡ My take: start the venv, install those basics now. Then we can either benchmark through Hugging Face directly in Python, or keep it light with llama.cpp/Ollama while still having the environment ready.

Want me to prep the exact Hugging Face snippet you could run inside this venv to load a Qwen-7B and watch VRAM usage?

Darren2025-09-18T08:31:25-07:00

So llama.cpp and ollama, that's the interface that we'll be using to speak with the QWEN7B.

ChatGPT2025-09-18T08:31:26-07:00 · gpt-5

Exactly. You’ve got two main “front doors” to talk to Qwen-7B:

🔹 Ollama

Think of it like a ready-made server + CLI.
It handles downloading, quantization, GPU offload, context length, batching — all behind the scenes.
You run:
bash ollama run qwen2.5:7b-instruct
and you’re chatting.
Very quick baseline test, minimal setup.
Downside: less fine-grained control, and it “hides” some details.

🔹 llama.cpp

Think of it like the bare-metal engine.
You compile it yourself and run a model file (.gguf).
You can tweak every knob (context size, quant type, batch size, GPU offload layers).
Best for benchmarking and when you want to squeeze every ounce out of your GPU.
Also gives you a Python binding (pip install llama-cpp-python) if you want to script things.

🔹 Where Hugging Face fits

If you load Qwen-7B through transformers, you’re running the “full fat” PyTorch model.
That’s useful later when you want custom pipelines, training, or integration with Ougway’s DB/memory.
Heavier on VRAM and slower than quantized Ollama/llama.cpp, but more flexible for research/dev.

⚖️ For your baseline:
Either Ollama or llama.cpp is perfect to “just talk” with Qwen-7B and record VRAM/tokens/sec.
Later, once we bolt on Ougway’s reflection/memory/RAG, you’ll probably want Hugging Face inside your venv to drive the model programmatically.

Do you want me to map out how to run the same prompt through all three (Ollama, llama.cpp, Hugging Face) so you can compare output + performance side-by-side?

Darren2025-09-18T08:32:38-07:00

And does llama.cpp have a GUI or is it just going to be a command line interface?

ChatGPT2025-09-18T08:32:38-07:00 · gpt-5

llama.cpp itself is command line only — no GUI baked in. It’s meant to be lean, fast, and portable.

That said, you’ve got options if you want something more “chat-like” on top of it:

CLI interactive mode (built-in):
When you run:
bash ./build/bin/llama-cli -m models/qwen2.5-7b-instruct/qwen2.5-7b-instruct-q4_k_m.gguf
it drops you into a little REPL (read–eval–print loop) where you type your prompt, hit Enter, and it streams the model’s reply. Super barebones, but it works.
Text-based UIs:
There are community projects that wrap llama.cpp in a terminal TUI (like llamafile, koboldcpp, etc.) so it feels more like a chat app.
Web GUIs on top:
Many people bolt on web frontends (like text-generation-webui, Open WebUI, or even a custom Gradio app). These talk to llama.cpp under the hood and give you a browser chat window.
Python bindings:
If you install llama-cpp-python, you can script your own chat loop in a few lines of Python — and from there, even throw together a minimal GUI with Tkinter or Flask if you wanted.

So: default = CLI, but you can layer on anything from a simple TUI to a polished web UI if you want to.

👉 Do you want me to sketch a path where you start with the CLI tonight, and later we wrap it with a minimal web chat front-end once Ougway’s stack is ready?

Darren2025-09-18T08:38:49-07:00

+—————————————————————————————+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=======================================================================================|
| 0 N/A N/A 1062 G /usr/lib/xorg/Xorg 76MiB |
| 0 N/A N/A 1415 G /usr/bin/gnome-shell 72MiB |
| 0 N/A N/A 26527 G …irefox/6782/usr/lib/firefox/firefox 139MiB |
+—————————————————————————————+
(venv) darren@ougway:~/ougway_env$ sudo apt-get update && sudo apt-get install -y build-essential cmake git
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
cmake -B build -DGGML_CUDA=ON
cmake –build build -j
[sudo] password for darren:
Hit:1 http://us.archive.ubuntu.com/ubuntu jammy InRelease
Hit:2 http://security.ubuntu.com/ubuntu jammy-security InRelease
Hit:3 http://us.archive.ubuntu.com/ubuntu jammy-updates InRelease
Hit:4 http://us.archive.ubuntu.com/ubuntu jammy-backports InRelease
Reading package lists… Done
Reading package lists… Done
Building dependency tree… Done
Reading state information… Done
build-essential is already the newest version (12.9ubuntu3).
git is already the newest version (1:2.34.1-1ubuntu1.15).
The following additional packages will be installed:
cmake-data dh-elpa-helper libjsoncpp25 librhash0
Suggested packages:
cmake-doc ninja-build cmake-format
The following NEW packages will be installed:
cmake cmake-data dh-elpa-helper libjsoncpp25 librhash0
0 upgraded, 5 newly installed, 0 to remove and 41 not upgraded.
Need to get 7,135 kB of archives.
After this operation, 31.8 MB of additional disk space will be used.
Get:1 http://us.archive.ubuntu.com/ubuntu jammy/main amd64 libjsoncpp25 amd64 1.9.5-3 [80.0 kB]
Get:2 http://us.archive.ubuntu.com/ubuntu jammy/main amd64 librhash0 amd64 1.4.2-1ubuntu1 [125 kB]
Get:3 http://us.archive.ubuntu.com/ubuntu jammy/main amd64 dh-elpa-helper all 2.0.9ubuntu1 [7,610 B]
Get:4 http://us.archive.ubuntu.com/ubuntu jammy-updates/main amd64 cmake-data all 3.22.1-1ubuntu1.22.04.2 [1,913 kB]
Get:5 http://us.archive.ubuntu.com/ubuntu jammy-updates/main amd64 cmake amd64 3.22.1-1ubuntu1.22.04.2 [5,010 kB]
Fetched 7,135 kB in 2s (4,524 kB/s)
Selecting previously unselected package libjsoncpp25:amd64.
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Cloning into 'llama.cpp'…
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— The C compiler identification is GNU 11.4.0
— The CXX compiler identification is GNU 11.4.0
— 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 "2.34.1")
— The ASM compiler identification is GNU
— Found assembler: /usr/bin/cc
— Looking for pthread.h
— Looking for pthread.h – found
— 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
— Could not find nvcc, please set CUDAToolkit_ROOT.
CMake Error at ggml/src/ggml-cuda/CMakeLists.txt:183 (message):
CUDA Toolkit not found

ChatGPT2025-09-18T08:38:51-07:00 · gpt-5-thinking

You’re 99% there—the build just can’t find the CUDA toolkit (you have the driver already). Two quick ways to fix it:

Fastest fix (recommended): install CUDA Toolkit 12.2 (no driver change)

This gives you nvcc + headers/libs so -DGGML_CUDA=ON works.

# 1) Add NVIDIA CUDA repo key (Jammy 22.04)
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.1-1_all.deb
sudo dpkg -i cuda-keyring_1.1-1_all.deb
sudo apt-get update

# 2) Install toolkit ONLY (won’t replace your 535 driver)
sudo apt-get install -y cuda-toolkit-12-2

# 3) Make sure nvcc is on PATH for this session
export PATH=/usr/local/cuda-12.2/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda-12.2/lib64:$LD_LIBRARY_PATH

# 4) Sanity check
nvcc --version

Then rebuild llama.cpp:

cd ~/ougway_env/llama.cpp
rm -rf build
cmake -B build -DGGML_CUDA=ON -DCUDAToolkit_ROOT=/usr/local/cuda-12.2
cmake --build build -j

Lighter-weight alternative (if you want to avoid full toolkit)

You can try using cuBLAS without full nvcc (sometimes works depending on build flags), but it’s fussier. The clean path is the full toolkit above. If you really want the light path, we can try -DGGML_CUBLAS=ON with just libcublas-dev, but expect more tinkering.

After it builds

You’ll have binaries in ./build/bin/:
– llama-cli – interactive CLI
– llama-bench – micro-bench

Drop a GGUF model somewhere like:

~/ougway_env/llama.cpp/models/qwen2.5-7b-instruct/qwen2.5-7b-instruct-q4_k_m.gguf

Run it (all layers to GPU, 4K ctx for a good VRAM test):

./build/bin/llama-cli \
  -m models/qwen2.5-7b-instruct/qwen2.5-7b-instruct-q4_k_m.gguf \
  -ngl 999 -c 4096 -b 256 -t $(nproc)

Bench it:

./build/bin/llama-bench \
  -m models/qwen2.5-7b-instruct/qwen2.5-7b-instruct-q4_k_m.gguf \
  -ngl 999

Watch VRAM in another terminal:

watch -n 1 nvidia-smi

If you’d rather skip installing the toolkit altogether, we can switch to Ollama for the baseline and circle back to llama.cpp later. But given your setup, getting CUDA 12.2 on there now will pay off for future builds too.

Darren2025-09-18T09:05:47-07:00

(venv) darren@ougway:~/ougway_env/llama.cpp$ cd ~/ougway_env/llama.cpp
rm -rf build
cmake -B build -DGGML_CUDA=ON -DCUDAToolkit_ROOT=/usr/local/cuda-12.2
cmake –build build -j
— The C compiler identification is GNU 11.4.0
— The CXX compiler identification is GNU 11.4.0
— 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 "2.34.1")
— The ASM compiler identification is GNU
— Found assembler: /usr/bin/cc
— Looking for pthread.h
— Looking for pthread.h – found
— 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/local/cuda-12.2/include (found version "12.2.140")
— CUDA Toolkit found
— Using CUDA architectures: 50-virtual;61-virtual;70-virtual;75-virtual;80-virtual;86-real;89-real
— The CUDA compiler identification is NVIDIA 12.2.140
— Detecting CUDA compiler ABI info
— Detecting CUDA compiler ABI info – done
— Check for working CUDA compiler: /usr/local/cuda-12.2/bin/nvcc – skipped
— Detecting CUDA compile features
— Detecting CUDA compile features – done
— CUDA host compiler is GNU 11.4.0
— Including CUDA backend
— ggml version: 0.0.6511
— ggml commit: 4ca088b0
— 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!
See also "/home/darren/ougway_env/llama.cpp/build/CMakeFiles/CMakeOutput.log".
gmake: Makefile: No such file or directory
gmake: *** No rule to make target 'Makefile'. Stop.
(venv) darren@ougway:~/ougway_env/llama.cpp$ nvcc –version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2023 NVIDIA Corporation
Built on Tue_Aug_15_22:02:13_PDT_2023
Cuda compilation tools, release 12.2, V12.2.140
Build cuda_12.2.r12.2/compiler.33191640_0
(venv) darren@ougway:~/ougway_env/llama.cpp$

ChatGPT2025-09-18T09:05:48-07:00 · gpt-5-thinking

Nice—CUDA’s found now. The new error is just missing libcurl dev. Two quick fixes:

Option 1 (recommended): install libcurl dev

sudo apt-get install -y libcurl4-openssl-dev pkg-config
# (optional but harmless if you plan HTTPS stuff)
sudo apt-get install -y libssl-dev zlib1g-dev

Then rebuild:

cd ~/ougway_env/llama.cpp
rm -rf build
cmake -B build -DGGML_CUDA=ON -DCUDAToolkit_ROOT=/usr/local/cuda-12.2
cmake --build build -j

Option 2: disable curl in llama.cpp

If you don’t need the server/download bits:

cd ~/ougway_env/llama.cpp
rm -rf build
cmake -B build -DGGML_CUDA=ON -DLLAMA_CURL=OFF -DCUDAToolkit_ROOT=/usr/local/cuda-12.2
cmake --build build -j

Grab a Qwen 7B GGUF the easy way (inside your venv)

# still in ~/ougway_env/llama.cpp
pip install --upgrade huggingface_hub

# make a model folder
mkdir -p models/qwen2.5-7b-instruct

# download a good 4-bit quant
huggingface-cli download Qwen/Qwen2.5-7B-Instruct-GGUF \
  qwen2.5-7b-instruct-q4_k_m.gguf \
  --local-dir models/qwen2.5-7b-instruct

Run it (all layers on GPU, 4K ctx to exercise VRAM)

./build/bin/llama-cli \
  -m models/qwen2.5-7b-instruct/qwen2.5-7b-instruct-q4_k_m.gguf \
  -ngl 999 -c 4096 -b 256 -t $(nproc)

Paste a test prompt like:

Benchmark: Summarize what you are and how you reason in &lt;=80 words. Then list 5 bullet points of your limitations.

Bench it

./build/bin/llama-bench \
  -m models/qwen2.5-7b-instruct/qwen2.5-7b-instruct-q4_k_m.gguf \
  -ngl 999

In another terminal:

watch -n 1 nvidia-smi

You should be golden after installing libcurl4-openssl-dev. If anything else barks, shoot me the output and we’ll squash it.

Darren2025-09-18T09:33:20-07:00

(venv) darren@ougway:~/ougway_env/llama.cpp$ # still in ~/ougway_env/llama.cpp
pip install –upgrade huggingface_hub

make a model folder

mkdir -p models/qwen2.5-7b-instruct

download a good 4-bit quant

huggingface-cli download Qwen/Qwen2.5-7B-Instruct-GGUF \
qwen2.5-7b-instruct-q4_k_m.gguf \
–local-dir models/qwen2.5-7b-instruct
Requirement already satisfied: huggingface_hub in /home/darren/ougway_env/venv/lib/python3.10/site-packages (0.33.0)
Collecting huggingface_hub
Downloading huggingface_hub-0.35.0-py3-none-any.whl.metadata (14 kB)
Requirement already satisfied: filelock in /home/darren/ougway_env/venv/lib/python3.10/site-packages (from huggingface_hub) (3.13.1)
Requirement already satisfied: fsspec>=2023.5.0 in /home/darren/ougway_env/venv/lib/python3.10/site-packages (from huggingface_hub) (2024.6.1)
Requirement already satisfied: packaging>=20.9 in /home/darren/ougway_env/venv/lib/python3.10/site-packages (from huggingface_hub) (24.2)
Requirement already satisfied: pyyaml>=5.1 in /home/darren/ougway_env/venv/lib/python3.10/site-packages (from huggingface_hub) (6.0.2)
Requirement already satisfied: requests in /home/darren/ougway_env/venv/lib/python3.10/site-packages (from huggingface_hub) (2.32.4)
Requirement already satisfied: tqdm>=4.42.1 in /home/darren/ougway_env/venv/lib/python3.10/site-packages (from huggingface_hub) (4.67.1)
Requirement already satisfied: typing-extensions>=3.7.4.3 in /home/darren/ougway_env/venv/lib/python3.10/site-packages (from huggingface_hub) (4.14.0)
Requirement already satisfied: hf-xet<2.0.0,>=1.1.3 in /home/darren/ougway_env/venv/lib/python3.10/site-packages (from huggingface_hub) (1.1.3)
Requirement already satisfied: charset_normalizer<4,>=2 in /home/darren/ougway_env/venv/lib/python3.10/site-packages (from requests->huggingface_hub) (3.4.2)
Requirement already satisfied: idna<4,>=2.5 in /home/darren/ougway_env/venv/lib/python3.10/site-packages (from requests->huggingface_hub) (3.10)
Requirement already satisfied: urllib3<3,>=1.21.1 in /home/darren/ougway_env/venv/lib/python3.10/site-packages (from requests->huggingface_hub) (2.4.0)
Requirement already satisfied: certifi>=2017.4.17 in /home/darren/ougway_env/venv/lib/python3.10/site-packages (from requests->huggingface_hub) (2025.4.26)
Downloading huggingface_hub-0.35.0-py3-none-any.whl (563 kB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 563.4/563.4 kB 2.5 MB/s 0:00:00
Installing collected packages: huggingface_hub
Attempting uninstall: huggingface_hub
Found existing installation: huggingface-hub 0.33.0
Uninstalling huggingface-hub-0.33.0:
Successfully uninstalled huggingface-hub-0.33.0
Successfully installed huggingface_hub-0.35.0
⚠️ Warning: 'huggingface-cli download' is deprecated. Use 'hf download' instead.
Traceback (most recent call last):
File "/home/darren/ougway_env/venv/lib/python3.10/site-packages/huggingface_hub/utils/_http.py", line 409, in hf_raise_for_status
response.raise_for_status()
File "/home/darren/ougway_env/venv/lib/python3.10/site-packages/requests/models.py", line 1026, in raise_for_status
raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 404 Client Error: Not Found for url: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct-GGUF/resolve/main/qwen2.5-7b-instruct-q4_k_m.gguf

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
File "/home/darren/ougway_env/venv/bin/huggingface-cli", line 7, in <module>
sys.exit(main())
File "/home/darren/ougway_env/venv/lib/python3.10/site-packages/huggingface_hub/commands/huggingface_cli.py", line 61, in main
service.run()
File "/home/darren/ougway_env/venv/lib/python3.10/site-packages/huggingface_hub/commands/download.py", line 157, in run
print(self._download()) # Print path to downloaded files
File "/home/darren/ougway_env/venv/lib/python3.10/site-packages/huggingface_hub/commands/download.py", line 170, in _download
return hf_hub_download(
File "/home/darren/ougway_env/venv/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
return fn(args, kwargs)
File "/home/darren/ougway_env/venv/lib/python3.10/site-packages/huggingface_hub/file_download.py", line 990, in hf_hub_download
return _hf_hub_download_to_local_dir(
File "/home/darren/ougway_env/venv/lib/python3.10/site-packages/huggingface_hub/file_download.py", line 1232, in _hf_hub_download_to_local_dir
(url_to_download, etag, commit_hash, expected_size, xet_file_data, head_call_error) = _get_metadata_or_catch_error(
File "/home/darren/ougway_env/venv/lib/python3.10/site-packages/huggingface_hub/file_download.py", line 1546, in _get_metadata_or_catch_error
metadata = get_hf_file_metadata(
File "/home/darren/ougway_env/venv/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
return fn(args, **kwargs)
File "/home/darren/ougway_env/venv/lib/python3.10/site-packages/huggingface_hub/file_download.py", line 1463, in get_hf_file_metadata
r = _request_wrapper(
File "/home/darren/ougway_env/venv/lib/python3.10/site-packages/huggingface_hub/file_download.py", line 286, in _request_wrapper
response = _request_wrapper(
File "/home/darren/ougway_env/venv/lib/python3.10/site-packages/huggingface_hub/file_download.py", line 310, in _request_wrapper
hf_raise_for_status(response)
File "/home/darren/ougway_env/venv/lib/python3.10/site-packages/huggingface_hub/utils/_http.py", line 420, in hf_raise_for_status
raise _format(EntryNotFoundError, message, response) from e
huggingface_hub.errors.EntryNotFoundError: 404 Client Error. (Request ID: Root=1-68cc3425-440f0be550f15ee554ff324d;5130eb4f-834d-44f2-9b77-5e4a3f51c762)

Entry Not Found for url: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct-GGUF/resolve/main/qwen2.5-7b-instruct-q4_k_m.gguf.
(venv) darren@ougway:~/ougway_env/llama.cpp$ ./build/bin/llama-cli \
-m models/qwen2.5-7b-instruct/qwen2.5-7b-instruct-q4_k_m.gguf \
-ngl 999 -c 4096 -b 256 -t $(nproc)
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 1 CUDA devices:
Device 0: NVIDIA GeForce RTX 3060, compute capability 8.6, VMM: yes
build: 6511 (4ca088b0) with cc (Ubuntu 11.4.0-1ubuntu1~22.04.2) 11.4.0 for x86_64-linux-gnu
main: llama backend init
main: load the model and apply lora adapter, if any
llama_model_load_from_file_impl: using device CUDA0 (NVIDIA GeForce RTX 3060) (0000:01:00.0) – 11640 MiB free
gguf_init_from_file: failed to open GGUF file 'models/qwen2.5-7b-instruct/qwen2.5-7b-instruct-q4_k_m.gguf'
llama_model_load: error loading model: llama_model_loader: failed to load model from models/qwen2.5-7b-instruct/qwen2.5-7b-instruct-q4_k_m.gguf
llama_model_load_from_file_impl: failed to load model
common_init_from_params: failed to load model 'models/qwen2.5-7b-instruct/qwen2.5-7b-instruct-q4_k_m.gguf', try reducing –n-gpu-layers if you're running out of VRAM
main: error: unable to load model
(venv) darren@ougway:~/ougway_env/llama.cpp$

ChatGPT2025-09-18T09:33:38-07:00 · gpt-5-thinking

Great progress. Two things tripped you up:

1) the filename is case-sensitive
2) huggingface-cli download is deprecated → use hf download

Use one of these (both good sources):

Option A — Official Qwen repo

# still in ~/ougway_env/llama.cpp
pip install -U huggingface_hub

mkdir -p models/qwen2.5-7b-instruct

# NOTE the capitalization of the file name:
hf download Qwen/Qwen2.5-7B-Instruct-GGUF \
  Qwen2.5-7B-Instruct-Q4_K_M.gguf \
  --local-dir models/qwen2.5-7b-instruct

Option B — Bartowski (popular mirrors)

hf download bartowski/Qwen2.5-7B-Instruct-GGUF \
  Qwen2.5-7B-Instruct-Q4_K_M.gguf \
  --local-dir models/qwen2.5-7b-instruct

Then run:

./build/bin/llama-cli \
  -m models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q4_K_M.gguf \
  -ngl 999 -c 4096 -b 256 -t $(nproc)

If you still get a path error:
– ls -lh models/qwen2.5-7b-instruct to confirm the exact filename
– ensure the capital Q + capital K/M are present in Q4_K_M

Once it’s loaded, pop open watch -n 1 nvidia-smi in another terminal and you’re benchmarking.

Darren2025-09-18T09:34:34-07:00

(venv) darren@ougway:~/ougway_env/llama.cpp$ ./build/bin/llama-cli \
-m models/qwen2.5-7b-instruct/qwen2.5-7b-instruct-q4_k_m.gguf \
-ngl 999 -c 4096 -b 256 -t $(nproc)
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 1 CUDA devices:
Device 0: NVIDIA GeForce RTX 3060, compute capability 8.6, VMM: yes
build: 6511 (4ca088b0) with cc (Ubuntu 11.4.0-1ubuntu1~22.04.2) 11.4.0 for x86_64-linux-gnu
main: llama backend init
main: load the model and apply lora adapter, if any
llama_model_load_from_file_impl: using device CUDA0 (NVIDIA GeForce RTX 3060) (0000:01:00.0) – 11640 MiB free
gguf_init_from_file: failed to open GGUF file 'models/qwen2.5-7b-instruct/qwen2.5-7b-instruct-q4_k_m.gguf'
llama_model_load: error loading model: llama_model_loader: failed to load model from models/qwen2.5-7b-instruct/qwen2.5-7b-instruct-q4_k_m.gguf
llama_model_load_from_file_impl: failed to load model
common_init_from_params: failed to load model 'models/qwen2.5-7b-instruct/qwen2.5-7b-instruct-q4_k_m.gguf', try reducing –n-gpu-layers if you're running out of VRAM
main: error: unable to load model
(venv) darren@ougway:~/ougway_env/llama.cpp$ # still in ~/ougway_env/llama.cpp
pip install -U huggingface_hub

mkdir -p models/qwen2.5-7b-instruct

NOTE the capitalization of the file name:

hf download Qwen/Qwen2.5-7B-Instruct-GGUF \
Qwen2.5-7B-Instruct-Q4_K_M.gguf \
–local-dir models/qwen2.5-7b-instruct
Requirement already satisfied: huggingface_hub in /home/darren/ougway_env/venv/lib/python3.10/site-packages (0.35.0)
Requirement already satisfied: filelock in /home/darren/ougway_env/venv/lib/python3.10/site-packages (from huggingface_hub) (3.13.1)
Requirement already satisfied: fsspec>=2023.5.0 in /home/darren/ougway_env/venv/lib/python3.10/site-packages (from huggingface_hub) (2024.6.1)
Requirement already satisfied: packaging>=20.9 in /home/darren/ougway_env/venv/lib/python3.10/site-packages (from huggingface_hub) (24.2)
Requirement already satisfied: pyyaml>=5.1 in /home/darren/ougway_env/venv/lib/python3.10/site-packages (from huggingface_hub) (6.0.2)
Requirement already satisfied: requests in /home/darren/ougway_env/venv/lib/python3.10/site-packages (from huggingface_hub) (2.32.4)
Requirement already satisfied: tqdm>=4.42.1 in /home/darren/ougway_env/venv/lib/python3.10/site-packages (from huggingface_hub) (4.67.1)
Requirement already satisfied: typing-extensions>=3.7.4.3 in /home/darren/ougway_env/venv/lib/python3.10/site-packages (from huggingface_hub) (4.14.0)
Requirement already satisfied: hf-xet<2.0.0,>=1.1.3 in /home/darren/ougway_env/venv/lib/python3.10/site-packages (from huggingface_hub) (1.1.3)
Requirement already satisfied: charset_normalizer<4,>=2 in /home/darren/ougway_env/venv/lib/python3.10/site-packages (from requests->huggingface_hub) (3.4.2)
Requirement already satisfied: idna<4,>=2.5 in /home/darren/ougway_env/venv/lib/python3.10/site-packages (from requests->huggingface_hub) (3.10)
Requirement already satisfied: urllib3<3,>=1.21.1 in /home/darren/ougway_env/venv/lib/python3.10/site-packages (from requests->huggingface_hub) (2.4.0)
Requirement already satisfied: certifi>=2017.4.17 in /home/darren/ougway_env/venv/lib/python3.10/site-packages (from requests->huggingface_hub) (2025.4.26)
Traceback (most recent call last):
File "/home/darren/ougway_env/venv/lib/python3.10/site-packages/huggingface_hub/utils/_http.py", line 409, in hf_raise_for_status
response.raise_for_status()
File "/home/darren/ougway_env/venv/lib/python3.10/site-packages/requests/models.py", line 1026, in raise_for_status
raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 404 Client Error: Not Found for url: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct-GGUF/resolve/main/Qwen2.5-7B-Instruct-Q4_K_M.gguf

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
File "/home/darren/ougway_env/venv/bin/hf", line 7, in <module>
sys.exit(main())
File "/home/darren/ougway_env/venv/lib/python3.10/site-packages/huggingface_hub/cli/hf.py", line 59, in main
service.run()
File "/home/darren/ougway_env/venv/lib/python3.10/site-packages/huggingface_hub/cli/download.py", line 136, in run
print(self._download()) # Print path to downloaded files
File "/home/darren/ougway_env/venv/lib/python3.10/site-packages/huggingface_hub/cli/download.py", line 149, in _download
return hf_hub_download(
File "/home/darren/ougway_env/venv/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
return fn(args, kwargs)
File "/home/darren/ougway_env/venv/lib/python3.10/site-packages/huggingface_hub/file_download.py", line 990, in hf_hub_download
return _hf_hub_download_to_local_dir(
File "/home/darren/ougway_env/venv/lib/python3.10/site-packages/huggingface_hub/file_download.py", line 1232, in _hf_hub_download_to_local_dir
(url_to_download, etag, commit_hash, expected_size, xet_file_data, head_call_error) = _get_metadata_or_catch_error(
File "/home/darren/ougway_env/venv/lib/python3.10/site-packages/huggingface_hub/file_download.py", line 1546, in _get_metadata_or_catch_error
metadata = get_hf_file_metadata(
File "/home/darren/ougway_env/venv/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
return fn(args, **kwargs)
File "/home/darren/ougway_env/venv/lib/python3.10/site-packages/huggingface_hub/file_download.py", line 1463, in get_hf_file_metadata
r = _request_wrapper(
File "/home/darren/ougway_env/venv/lib/python3.10/site-packages/huggingface_hub/file_download.py", line 286, in _request_wrapper
response = _request_wrapper(
File "/home/darren/ougway_env/venv/lib/python3.10/site-packages/huggingface_hub/file_download.py", line 310, in _request_wrapper
hf_raise_for_status(response)
File "/home/darren/ougway_env/venv/lib/python3.10/site-packages/huggingface_hub/utils/_http.py", line 420, in hf_raise_for_status
raise _format(EntryNotFoundError, message, response) from e
huggingface_hub.errors.EntryNotFoundError: 404 Client Error. (Request ID: Root=1-68cc3486-293618ad471f0ad632dd0fc0;6999fc84-41b6-42d8-8411-d523c91fda97)

Entry Not Found for url: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct-GGUF/resolve/main/Qwen2.5-7B-Instruct-Q4_K_M.gguf.
(venv) darren@ougway:~/ougway_env/llama.cpp$

ChatGPT2025-09-18T09:34:35-07:00 · gpt-5-thinking

You’re super close—the 404 just means the exact repo/file name doesn’t match what we asked for. Two quick ways to fix it:

A) Discover the exact filename (best)

Use hf ls to list what’s actually in the repo, then download that exact file.

# list files in the official repo
hf ls Qwen/Qwen2.5-7B-Instruct-GGUF --repo-type model | head -n 50

# or list the community mirror (often easier to grab)
hf ls bartowski/Qwen2.5-7B-Instruct-GGUF --repo-type model | head -n 50

You should see lines that look like:

Qwen2.5-7B-Instruct-Q4_K_M.gguf
Qwen2.5-7B-Instruct-Q5_K_M.gguf
Qwen2.5-7B-Instruct-IQ4_XS.gguf
...

Then download one you see listed (note the exact capitalization/underscores):

# example using Bartowski mirror
hf download bartowski/Qwen2.5-7B-Instruct-GGUF \
  --repo-type model \
  --include &quot;Qwen2.5-7B-Instruct-Q4_K_M.gguf&quot; \
  --local-dir models/qwen2.5-7b-instruct

(If you prefer official Qwen repo, just swap the owner:
Qwen/Qwen2.5-7B-Instruct-GGUF.)

Now verify the filename and run:

ls -lh models/qwen2.5-7b-instruct

./build/bin/llama-cli \
  -m models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q4_K_M.gguf \
  -ngl 999 -c 4096 -b 256 -t $(nproc)

B) Pull by pattern (skip guessing)

If you just want any Q4 quant without caring about the exact casing:

hf download bartowski/Qwen2.5-7B-Instruct-GGUF \
  --repo-type model \
  --include &quot;*Q4*_K_M*.gguf&quot; \
  --local-dir models/qwen2.5-7b-instruct

Then ls to see what landed and use that exact name in -m.

If you still get a 404 on the official repo

Sometimes the official model requires accepting a license on Hugging Face. If so:

hf login  # paste your token

Then visit the model page in a browser and click “Agree.”
(Using the Bartowski mirror usually avoids this step.)

Ping me with the hf ls output if anything looks odd, and I’ll point to the exact string to paste into -m.

Darren2025-09-18T09:35:52-07:00

(venv) darren@ougway:~/ougway_env/llama.cpp$ # list files in the official repo
hf ls Qwen/Qwen2.5-7B-Instruct-GGUF –repo-type model | head -n 50

or list the community mirror (often easier to grab)

hf ls bartowski/Qwen2.5-7B-Instruct-GGUF –repo-type model | head -n 50
usage: hf <command> [<args>]
hf: error: argument {auth,cache,download,jobs,repo,repo-files,upload,upload-large-folder,env,version,lfs-enable-largefiles,lfs-multipart-upload}: invalid choice: 'ls' (choose from 'auth', 'cache', 'download', 'jobs', 'repo', 'repo-files', 'upload', 'upload-large-folder', 'env', 'version', 'lfs-enable-largefiles', 'lfs-multipart-upload')
usage: hf <command> [<args>]
hf: error: argument {auth,cache,download,jobs,repo,repo-files,upload,upload-large-folder,env,version,lfs-enable-largefiles,lfs-multipart-upload}: invalid choice: 'ls' (choose from 'auth', 'cache', 'download', 'jobs', 'repo', 'repo-files', 'upload', 'upload-large-folder', 'env', 'version', 'lfs-enable-largefiles', 'lfs-multipart-upload')
(venv) darren@ougway:~/ougway_env/llama.cpp$

ChatGPT2025-09-18T09:35:53-07:00 · gpt-5

Ah, got it — your version of the hf CLI doesn’t have the ls subcommand yet. No problem — we’ll do this another way.

Here’s a direct, working download command for the Bartowski mirror (usually the most reliable for GGUF quants):

hf download bartowski/Qwen2.5-7B-Instruct-GGUF \
  --repo-type model \
  --include &quot;Qwen2.5-7B-Instruct-Q4_K_M.gguf&quot; \
  --local-dir models/qwen2.5-7b-instruct

If that runs clean, check:

ls -lh models/qwen2.5-7b-instruct

You should see something like:

Qwen2.5-7B-Instruct-Q4_K_M.gguf   4.2G

Then launch:

./build/bin/llama-cli \
  -m models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q4_K_M.gguf \
  -ngl 999 -c 4096 -b 256 -t $(nproc)

⚠️ If hf download still errors out, we can bypass hf completely and just wget the file directly from Hugging Face’s CDN link. Want me to drop you a ready-to-go wget line for that GGUF?

Darren2025-09-18T09:37:13-07:00

looks like a winnerThe thing I should remind you with is that apparently this is, this client's going to be quite large and if it turns out to be that one terabyte deal, it needs to go on the second drive.