Update README.md

README.md

@@ -73,11 +73,10 @@ The 8 bit GPTQ quant has minimum quality degradation from the original `bfloat16
 - Quantization is calibrated and aligned with random samples from the specified dataset (wikitext for now) for minimum accuracy loss.
 
 | Branch | Bits | Group Size | Act Order | Damp % | GPTQ Dataset | Sequence Length | VRAM Size | ExLlama | Special Tokens Fixed | Description |
-| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
+| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ------- | ---- |
 | [main](https://huggingface.co/astronomer-io/Llama-3-8B-GPTQ-8-Bit/tree/main) | 8 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 8192 | 9.74 GB | No | No | 8-bit, with Act Order and group size 32g. Minimum accuracy loss with decent VRAM usage reduction. | 
-| [untrained-special-tokens-fixed](https://huggingface.co/astronomer-io/Llama-3-8B-GPTQ-8-Bit/tree/untrained-special-tokens-fixed) | 8 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 8192 | 9.74 GB | No | Yes | Same as the main branch. The special tokens that were untrained causing exploding graidents/NaN gradients have had their embedding values set to the average of trained tokens for each feature | 
-
-| 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. |
+| [untrained-special-tokens-fixed](https://huggingface.co/astronomer-io/Llama-3-8B-GPTQ-8-Bit/tree/untrained-special-tokens-fixed) | 8 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 8192 | 9.74 GB | No | Yes | Same as the main branch. The special tokens that were untrained causing exploding gradients/NaN gradients have had their embedding values set to the average of trained tokens for each feature | 
+| More variants to come | TBD | 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. |
 
 ## Serving this GPTQ model using vLLM
 Tested serving this model via vLLM using an Nvidia T4 (16GB VRAM).