Text Generation
MLX
Safetensors
multilingual
phi3
nlp
code
quantization
bias-evaluation
q8
conversational
custom_code
8-bit precision
Instructions to use plawanrath/phi-3.5-mini-instruct-q8-mlx-cba with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use plawanrath/phi-3.5-mini-instruct-q8-mlx-cba with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("plawanrath/phi-3.5-mini-instruct-q8-mlx-cba") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use plawanrath/phi-3.5-mini-instruct-q8-mlx-cba with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "plawanrath/phi-3.5-mini-instruct-q8-mlx-cba"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "plawanrath/phi-3.5-mini-instruct-q8-mlx-cba" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "plawanrath/phi-3.5-mini-instruct-q8-mlx-cba", "messages": [ {"role": "user", "content": "Hello"} ] }' - Atomic Chat
Add arXiv link (2605.15208) to model card
Browse files
README.md
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@@ -24,6 +24,7 @@ This is one of the **15 model artifacts** from the paper:
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> **Quantization Undoes Alignment: Bias Emergence in Compressed LLMs Across Models and Precision Levels**
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> Plawan Kumar Rath, Rahul Maliakkal. *IEEE Cloud Summit 2026*.
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> Code: <https://github.com/plawanrath/compression-bias-amplification>
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## Quantization
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title = { Quantization Undoes Alignment: Bias Emergence in Compressed LLMs Across Models and Precision Levels },
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author = {Rath, Plawan Kumar and Maliakkal, Rahul},
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booktitle = { IEEE Cloud Summit 2026 },
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year = {2026}
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}
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```
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> **Quantization Undoes Alignment: Bias Emergence in Compressed LLMs Across Models and Precision Levels**
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> Plawan Kumar Rath, Rahul Maliakkal. *IEEE Cloud Summit 2026*.
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> Code: <https://github.com/plawanrath/compression-bias-amplification>
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> arXiv: <https://arxiv.org/abs/2605.15208>
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## Quantization
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title = { Quantization Undoes Alignment: Bias Emergence in Compressed LLMs Across Models and Precision Levels },
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author = {Rath, Plawan Kumar and Maliakkal, Rahul},
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booktitle = { IEEE Cloud Summit 2026 },
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year = {2026},
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eprint = {2605.15208},
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archivePrefix = {arXiv},
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url = {https://arxiv.org/abs/2605.15208}
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}
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```
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