Update README.md

README.md

@@ -18,6 +18,6 @@ For models with bits per weight (BPW) over 6.0, I default to quantizing the `lm_
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 **Who are you? What's with these weird BPWs on [insert model here]?**  
-I specialize in optimized EXL2 quantization for models in the 70B to 100B+ range, specifically tailored for 48GB VRAM setups. My rig features 2 x 3090s with a Ryzen APU (used solely for desktop output—no VRAM wasted on the GPUs). I use TabbyAPI for inference, targeting context sizes between 32K and 64K.
+I specialize in optimized EXL2 quantization for models in the 70B to 100B+ range, specifically tailored for 48GB VRAM setups. My rig is built using 2 x 3090s with a Ryzen APU (APU used solely for desktop output—no VRAM wasted on the 3090s). I use TabbyAPI for inference, targeting context sizes between 32K and 64K.
 
 Every model I upload includes a `config.yml` file with my ideal TabbyAPI settings. If you're using my config, don’t forget to set `PYTORCH_CUDA_ALLOC_CONF=backend:cudaMallocAsync` to save some VRAM.