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Model: plawanrath/phi-3.5-mini-instruct-bf16-mlx-cba
Source: Original Platform

README.md

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+---
+library_name: mlx
+license: mit
+license_link: https://huggingface.co/microsoft/Phi-3.5-mini-instruct/resolve/main/LICENSE
+language:
+- multilingual
+pipeline_tag: text-generation
+base_model: microsoft/Phi-3.5-mini-instruct
+tags:
+- nlp
+- code
+- mlx
+- quantization
+- bias-evaluation
+---
+
+# phi-3.5-mini-instruct-bf16 (MLX, CBA artifact)
+
+MLX-format BF16 (uncompressed baseline) variant of [`microsoft/Phi-3.5-mini-instruct`](https://huggingface.co/microsoft/Phi-3.5-mini-instruct).
+
+This is one of the **15 model artifacts** from the paper:
+
+> **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 microsoft/Phi-3.5-mini-instruct \
+    --mlx-path ./phi-3.5-mini-instruct-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)
+
+```bash
+pip install mlx-lm
+```
+
+```python
+from mlx_lm import load, generate
+
+model, tokenizer = load("plawanrath/phi-3.5-mini-instruct-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:
+
+```bash
+mlx_lm.generate --model plawanrath/phi-3.5-mini-instruct-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:** [`microsoft/Phi-3.5-mini-instruct`](https://huggingface.co/microsoft/Phi-3.5-mini-instruct)
+- **Family:** Phi-3
+- **Parameters:** 3.8B
+- **Precision:** BF16 (uncompressed baseline)
+- **Format:** MLX (Apple Silicon)
+- **Conversion framework:** [`mlx-lm`](https://github.com/ml-explore/mlx-lm)
+
+## License
+
+Inherited from the base model (`mit`). 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}
+}
+```