Auto-Quant-Tool

Automated quantization benchmarking suite for GGUF, GPTQ, and TFLite models. Pulls a model from HuggingFace, generates multiple quantized variants, benchmarks them on your hardware, and outputs a Pareto frontier showing the best accuracy-to-speed tradeoff.

Screenshots

Auto-Quant-Tool UI

Supported formats

• GGUF (Q2 through Q8) — for llama.cpp / Ollama local inference
• GPTQ (INT4, INT8) — for GPU inference via gptqmodel
• TFLite (FP32, FP16, INT8) — for mobile deployment

Quick start

1. Clone the repo

git clone --recurse-submodules https://github.com/YOUR_USERNAME/auto-quant-tool.git
cd auto-quant-tool

2. Base install (all platforms)

uv sync

3. Hardware backend (run once, auto-detects your system)

python setup/install_backends.py

4. Launch the web UI

uv run python -m auto_quant_tool.cli ui

Then open http://localhost:7860 in your browser.

5. Or run via CLI

uv run python -m auto_quant_tool.cli run --config sample_llm.yaml

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Installation by platform

Windows + NVIDIA GPU

uv sync
python setup/install_backends.py --backend cuda

Requires Visual C++ Build Tools for llama.cpp compilation. Download: https://visualstudio.microsoft.com/visual-cpp-build-tools/

GPTQ quantization requires a GPU with 16GB+ VRAM. For systems with less VRAM, use the Kaggle notebook: notebooks/kaggle_gptq.ipynb

TFLite conversion is not supported on Windows. Use the Colab notebook instead: notebooks/colab_tflite.ipynb

macOS (Apple Silicon)

uv sync
python setup/install_backends.py --backend metal

Linux + NVIDIA GPU

uv sync
CMAKE_ARGS="-DLLAMA_CUDA=on" pip install llama-cpp-python

CPU only (any OS)

uv sync
python setup/install_backends.py --backend cpu

---

Configuration

Copy and edit a sample config:

cp sample_llm.yaml my_model.yaml

model:
  source: huggingface       # or local
  id: Qwen/Qwen2-0.5B
  modality: llm             # llm | vision | audio

quantize:
  formats: [gguf, gptq]
  gguf_levels: [Q2_K, Q4_K_M, Q5_0, Q8_0]
  gptq_levels: [int4]

benchmark:
  metrics: [perplexity, tok_s]
  full_mmlu: false
  soc_target: snapdragon_8_gen_3    # for TFLite sim benchmark
  dataset:
    name: wikitext
    split: test
    source: hf_datasets

---

Output structure

outputs/
├── models/          # cached HF model weights
├── gguf/            # GGUF quantized files per model
├── gptq/            # GPTQ quantized files per model
├── tflite/          # TFLite converted files per model
├── results/         # benchmark CSVs, unified JSON, Pareto HTML/PNG
└── best_model/      # knee-point model files copied here

---

Notebooks

• notebooks/kaggle_gptq.ipynb — GPTQ quantization on Kaggle T4 (16GB VRAM)
• notebooks/colab_tflite.ipynb — TFLite conversion on Google Colab

---

Hardware requirements

Task	Minimum	Recommended
GGUF conversion	8GB RAM	16GB RAM
GGUF inference (7B Q4)	8GB RAM	16GB RAM + any GPU
GPTQ quantization (7B)	16GB VRAM	A100 40GB
TFLite conversion	CPU only	CPU only
Simulated benchmark	CPU only	CPU only

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Known limitations

• TFLite conversion not supported on Windows (use Colab notebook)
• GPTQ requires 16GB+ VRAM (use Kaggle notebook for smaller GPUs)
• Perplexity measured on a short fixed corpus — use --full-mmlu for task-based accuracy (slower)
• TurboQuant (KV cache quantization) deferred to v2

License

Apache 2.0