Update README.md

README.md

@@ -72,7 +72,7 @@ The 8 bit GPTQ quant has minimum quality degradation from the original `bfloat16
 - This model is quantized by utilizing the AutoGPTQ library, following best practices noted by [GPTQ paper](https://arxiv.org/abs/2210.17323)
 - Quantization is calibrated and aligned with random samples from the specified dataset (wikitext for now) for minimum accuracy loss.
 
-| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | VRAM Size | ExLlama | Desc |
+| Branch | Bits | Group Size | Act Order | Damp % | GPTQ Dataset | Sequence Length | VRAM Size | ExLlama | Description |
 | ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
 | [main](https://huggingface.co/astronomer-io/Llama-3-8B-Instruct-GPTQ-8-Bit/tree/main) | 8 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 8192 | 9.09 GB | No | 8-bit, with Act Order and group size 32g. Minimum accuracy loss with decent VRAM usage reduction. | 
 | More variants to come | TBD | TBD | TBD | TBD | TBD | TBD | TBD | TBD | May upload additional variants of GPTQ 8 bit models in the future using different parameters such as 128g group size and etc. |