NAME0x0/AVA-v2-GGUF

<p align="center"> <img src="https://raw.githubusercontent.com/NAME0x0/AVA/main/AVA_logo.png" alt="AVA logo" width="160" /> </p>

AVA v2 — GGUF

Ready-to-run GGUF builds of AVA v2, a 2B reasoning model fine-tuned entirely on a single 4 GB laptop GPU. 82.0% ARC-Challenge, 92.0% ARC-Easy, 59.2% MMLU on a 17-benchmark, 52,027-instance full evaluation (report).

Works with llama.cpp, Ollama, LM Studio, Jan, KoboldCpp — no Python, no GPU required.

Files

All sub-8-bit quants are built with an importance matrix calibrated on the model's own training distribution (reasoning, math, science, instruction following) — the same idea behind Google's Gemma QAT releases: keep the small quants as close to reference quality as possible.

Measured quality cost vs the Q8_0 reference (perplexity on a held-out slice of the training distribution, 512-token context — lower is better):

Quick start

Ollama

ollama run hf.co/NAME0x0/AVA-v2-GGUF:Q4_K_M

llama.cpp

llama-cli -m AVA-v2-Q4_K_M.gguf -ngl 99 --temp 0.7 \
  -p "Explain why ice floats on water."

LM Studio / Jan

Search for NAME0x0/AVA-v2-GGUF in the model browser and download a file.

Chat format

Qwen3.5 ChatML-style template (embedded in the GGUF — runtimes apply it automatically):

<|im_start|>user
{your prompt}<|im_end|>
<|im_start|>assistant

Benchmarks (Q8_0, full sets, 95% Wilson CI)

Full 17-benchmark table and protocol: RESULTS_REPORT_V2_FULL.md.

At 2B parameters, AVA v2's ARC-Challenge (82.0%) is in the range of 3B-class models like Llama 3.2 3B-Instruct (78.6%) and Phi-4-mini 3.8B (83.7%), each measured under its own eval protocol. AVA v2 was trained in 100 minutes on one 4 GB laptop GPU.

Provenance

• —Adapter + training details: NAME0x0/AVA-v2
• —Base model: Qwen/Qwen3.5-2B (Apache 2.0)
• —Everything reproducible: github.com/NAME0x0/AVA — corpus builders, training configs, eval harness, and this quantization pipeline are all in the repo.

Citation

@misc{ava-v2-2026,
  title={AVA v2: QLoRA Fine-tuning Under Extreme VRAM Constraints},
  author={Muhammad Afsah Mumtaz},
  year={2026},
  url={https://github.com/NAME0x0/AVA}
}