Quantization Undoes Alignment: Bias Emergence in Compressed LLMs Across Models and Precision Levels Plawan Kumar Rath, Rahul Maliakkal. IEEE Cloud Summit 2026. Code: https://github.com/plawanrath/compression-bias-amplification arXiv: https://arxiv.org/abs/2605.15208

Quantization

This is the BF16 baseline used as the uncompressed reference in the paper. Weights have been re-serialized via mlx_lm.convert (no quantization) so this directory is loadable directly by MLX without an extra conversion step.

How this artifact was produced

bash

python -m mlx_lm.convert \
    --hf-path mistralai/Mistral-7B-Instruct-v0.3 \
    --mlx-path ./mistral-7b-instruct-v0.3-bf16 \

This is the exact artifact used to produce the inference results in §4.3 of the paper (911,100 records over BBQ ambiguous, 5 seeds × 12,148 items × 15 configs).

Usage (MLX)

pip install mlx-lm

python

from mlx_lm import load, generate

model, tokenizer = load("plawanrath/mistral-7b-instruct-v0.3-bf16-mlx-cba")
prompt = tokenizer.apply_chat_template(
    [{"role": "user", "content": "Hello!"}],
    add_generation_prompt=True,
    tokenize=False,
)
print(generate(model, tokenizer, prompt=prompt, max_tokens=128))

Or via CLI:

mlx_lm.generate --model plawanrath/mistral-7b-instruct-v0.3-bf16-mlx-cba --prompt "Hello!"

Paper findings relevant to this variant

The paper documents a dose-response relationship between quantization aggressiveness and emergent stereotypical behavior on BBQ ambiguous questions:

Variant	% of BF16-unbiased items that became biased
Q8	0.1–0.9%
Q6	0.3–1.3%
Q4	2.2–5.6%
Q3	6.0–21.1%

These changes are largely invisible to perplexity (<0.5% shift at Q8, <3% at Q4 across all three families). Treat any deployment of compressed instruction-tuned models on fairness-sensitive tasks accordingly.

Model details

Base model: mistralai/Mistral-7B-Instruct-v0.3
Family: Mistral
Parameters: 7.2B
Precision: BF16 (uncompressed baseline)
Format: MLX (Apple Silicon)
Conversion framework: mlx-lm

License

Inherited from the base model (apache-2.0). See the upstream model page for the full license text.

Citation

bibtex

@inproceedings{rath2026quantization,
  title     = { Quantization Undoes Alignment: Bias Emergence in Compressed LLMs Across Models and Precision Levels },
  author    = {Rath, Plawan Kumar and Maliakkal, Rahul},
  booktitle = { IEEE Cloud Summit 2026 },
  year      = {2026},
  eprint    = {2605.15208},
  archivePrefix = {arXiv},
  url       = {https://arxiv.org/abs/2605.15208}
}