Upload README.md

README.md

@@ -14,8 +14,8 @@ tags:
 - safetensors
 - onnx
 - transformers.js
-model_name: SmolLM2 1.7B Instruct
-base_model: HuggingFaceTB/SmolLM2-1.7B-Instruct
+model_name: SmolLM2 135M Instruct
+base_model: HuggingFaceTB/SmolLM2-135M-Instruct
 inference: false
 model_creator: HuggingFaceTB
 pipeline_tag: text-generation
@@ -26,7 +26,7 @@ quantized_by: fbaldassarri
 
 ## Model Information
 
-Quantized version of [HuggingFaceTB/SmolLM2-1.7B-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct) using torch.float32 for quantization tuning.
+Quantized version of [HuggingFaceTB/SmolLM2-135M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-135M-Instruct) using torch.float32 for quantization tuning.
 - 8 bits (INT8)
 - group size = 128
 - Asymmetrical Quantization
@@ -36,7 +36,7 @@ Fast and low memory, 2-3X speedup (slight accuracy drop at W8G128)
 
 Quantization framework: [Intel AutoRound](https://github.com/intel/auto-round) v0.4.5
 
-Note: this INT8 version of SmolLM2-1.7B-Instruct has been quantized to run inference through CPU.
+Note: this INT8 version of SmolLM2-135M-Instruct has been quantized to run inference through CPU.
 
 ## Replication Recipe
 
@@ -61,14 +61,14 @@ pip install -vvv --no-build-isolation -e .[cpu]
 
 ```
   from transformers import AutoModelForCausalLM, AutoTokenizer
-  model_name = "HuggingFaceTB/SmolLM2-1.7B-Instruct"
+  model_name = "HuggingFaceTB/SmolLM2-135M-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/HuggingFaceTB_SmolLM2-1.7B-Instruct-auto_round-int8-gs128-asym"
+  output_dir = "./AutoRound/HuggingFaceTB_SmolLM2-135M-Instruct-auto_round-int8-gs128-asym"
   autoround.save_quantized(output_dir, format='auto_round', inplace=True)
 ```