Qwen-2.5-3B-AQLM-OA-EM-2Bit-2x8

Qwen-2.5-3B-AQLM-OA-EM-2Bit-2x8

This is Qwen 2.5 3B quantized to 2 bits per parameter using AQLM with OA-EM initialization and PV-tuning.

OA-EM (Output-Aware Expectation-Maximisation) is a Hessian-weighted EM algorithm that significantly improves codebook initialization for additive quantization, particularly at extreme compression rates. See our paper for details.

Quantization Details

Results

Perplexity

Downstream Tasks (post-PV-tuning, b=8)

Note: On Qwen, the baseline holds a small downstream advantage (0.606 vs 0.603 average accuracy), consistent with the mild initialisation bottleneck on this architecture. OA-EM wins on perplexity (both WikiText-2 and C4 after PV-tuning).

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "kennedyian94/Qwen-2.5-3B-AQLM-OA-EM-2Bit-2x8",
    trust_remote_code=True,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("kennedyian94/Qwen-2.5-3B-AQLM-OA-EM-2Bit-2x8")

inputs = tokenizer("The meaning of life is", return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Note: Requires the aqlm inference library:

pip install aqlm[gpu]

Paper

Initialisation Determines the Basin: Efficient Codebook Optimisation for Extreme LLM Quantization

Ian W. Kennedy and Nafise Sadat Moosavi, University of Sheffield

arXiv:2604.08118 | Code

Citation

@article{kennedy2026oaem,
  title={Initialisation Determines the Basin: Efficient Codebook Optimisation for Extreme LLM Quantization},
  author={Kennedy, Ian W. and Moosavi, Nafise Sadat},
  journal={arXiv preprint arXiv:2604.08118},
  year={2026}
}