Upload README.md

README.md

@@ -23,8 +23,8 @@ tags:
 - llama-3
 - intel-autoround
 - intel
-model_name: Llama 3.2 1B
-base_model: meta-llama/Llama-3.2-1B
+model_name: Llama 3.2 1B Instruct
+base_model: meta-llama/Llama-3.2-1B-Instruct
 inference: false
 model_creator: meta-llama
 pipeline_tag: text-generation
@@ -35,7 +35,7 @@ quantized_by: fbaldassarri
 
 ## Model Information
 
-Quantized version of [meta-llama/Llama-3.2-1B](meta-llama/Llama-3.2-1B) using torch.float32 for quantization tuning.
+Quantized version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) using torch.float32 for quantization tuning.
 - 8 bits (INT8)
 - group size = 128
 - Asymmetrical Quantization
@@ -43,7 +43,7 @@ Quantized version of [meta-llama/Llama-3.2-1B](meta-llama/Llama-3.2-1B) using to
 
 Quantization framework: [Intel AutoRound](https://github.com/intel/auto-round)
 
-Note: this INT8 version of Llama-3.2-1B has been quantized to run inference through CPU.
+Note: this INT8 version of Llama-3.2-1B-Instruct has been quantized to run inference through CPU.
 
 ## Replication Recipe
 
@@ -68,14 +68,14 @@ pip install -vvv --no-build-isolation -e .[cpu]
 
 ```
   from transformers import AutoModelForCausalLM, AutoTokenizer
-  model_name = "meta-llama/Llama-3.2-1B"
+  model_name = "meta-llama/Llama-3.2-1B-Instruct"
   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, 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-asym"
+  output_dir = "./AutoRound/meta-llama_Llama-3.2-1B-Instruct-auto_gptq-int8-gs128-asym"
   autoround.save_quantized(output_dir, format='auto_gptq', inplace=True)
 ```