Article updated to reflect change in mlx test

aider/website/_posts/2024-11-21-quantization.md

@@ -16,7 +16,7 @@ aider's code editing benchmark, rivaling closed source frontier models.
 But pay attention to how your model is being quantized, as it
 can strongly impact code editing skill.
 Heavily quantized models are often used by cloud API providers
-and local model servers like Ollama.
+and local model servers like Ollama or MLX.
 
 <canvas id="quantChart" width="800" height="600" style="margin: 20px 0"></canvas>
 <script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
@@ -29,9 +29,7 @@ served both locally and from cloud providers.
 
 - The [HuggingFace BF16 weights](https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct) served via [glhf.chat](https://glhf.chat).
 - Hyperbolic labs API for [qwen2-5-coder-32b-instruct](https://app.hyperbolic.xyz/models/qwen2-5-coder-32b-instruct), which is using BF16. This result is probably within the expected variance of the HF result.
-- A [4bit quant for mlx](https://t.co/cwX3DYX35D). 
-This is the only model which was benchmarked using the "whole" [edit format](https://aider.chat/docs/more/edit-formats.html).
-The rest were benchmarked with the much more practical and challenging "diff"edit format.
+- A [4bit quant for mlx](https://t.co/cwX3DYX35D).
 - The results from [OpenRouter's mix of providers](https://openrouter.ai/qwen/qwen-2.5-coder-32b-instruct/providers) which serve the model with different levels of quantization.
 - Ollama locally serving [qwen2.5-coder:32b-instruct-q4_K_M)](https://ollama.com/library/qwen2.5-coder:32b-instruct-q4_K_M), which has `Q4_K_M` quantization, with Ollama's default 2k context window.