easyllama-divi845 1.0.0

Zero-friction GPU bootstrap for llama-cpp-python on Windows.

easyllama

Small helper package so your llama_cpp scripts can stay short while still using GPU.

What it does

• Adds CUDA DLL folders to runtime search path automatically
• Detects pip-installed NVIDIA runtime DLL folders inside the active venv
• Forces dedicated GPU usage defaults for llama_cpp.Llama (unless you override)

Install (new laptop / fresh Windows)

1. Install CUDA-enabled llama-cpp-python in your venv:

python -m pip install --upgrade --force-reinstall --no-cache-dir llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cu124

2. Install this package:

cd "C:\Users\divid\Desktop\Data analytics\easyllama"
python -m pip install -e .

Use

from easyllama import Llama

llm = Llama(
    model_path=r"C:\AI\Model\Qwen3-4B.Q4_K_M.gguf",
    n_ctx=2048,
)

n_gpu_layers=-1, main_gpu=0, and offload_kqv=True are auto-set unless you pass your own values. Also, logs are quiet by default (verbose=False, no_perf=True) so CUDA graph spam does not flood your terminal.

If you want debug logs back:

$env:LLAMA_VERBOSE="1"
$env:LLAMA_NO_PERF="0"

Quick check

python -m easyllama

PyPI Trusted Publishing (GitHub Actions)

This repo includes:

• .github/workflows/publish.yml

How to enable:

1. Push this project to GitHub.
2. On PyPI, configure Trusted Publishing:
   • If easyllama does not exist yet: use account settings -> Publishing -> add a pending publisher for easyllama.
   • If easyllama already exists: open project settings -> Publishing -> add a publisher.
3. Fill with your GitHub details:
   • Owner: your GitHub username/org
   • Repository: your repo name
   • Workflow name: publish.yml
   • Environment name: pypi
4. Create and push a version tag like v1.0.0.
5. GitHub Actions publishes automatically to PyPI using OIDC (no API token needed).