Update README.md

README.md

@@ -38,7 +38,7 @@ quantized_by: fbaldassarri
 Quantized version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) using torch.float32 for quantization tuning.
 - 8 bits (INT8)
 - group size = 128
-- Symmetrical Quantization
+- Asymmetrical Quantization
 - Method AutoGPTQ
 
 Quantization framework: [Intel AutoRound](https://github.com/intel/auto-round)
@@ -72,10 +72,10 @@ pip install -vvv --no-build-isolation -e .[cpu]
   model = AutoModelForCausalLM.from_pretrained(model_name)
   tokenizer = AutoTokenizer.from_pretrained(model_name)
   from auto_round import AutoRound
-  bits, group_size, sym, device, amp = 8, 128, True, 'cpu', False
+  bits, group_size, sym, device, amp = 8, 128, False, 'cpu', False
   autoround = AutoRound(model, tokenizer, nsamples=128, iters=200, seqlen=512, batch_size=4, bits=bits, group_size=group_size, sym=sym, device=device, amp=amp)
   autoround.quantize()
-  output_dir = "./AutoRound/meta-llama_Llama-3.2-1B-auto_gptq-int8-gs128-sym"
+  output_dir = "./AutoRound/meta-llama_Llama-3.2-1B-auto_gptq-int8-gs128-asym"
   autoround.save_quantized(output_dir, format='auto_gptq', inplace=True)
 ```