Update README.md

README.md

@@ -18,14 +18,14 @@ pipeline_tag: text-generation
 ---
 
 
-[Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B) is quantized by the PyTorch team using [torchao](https://huggingface.co/docs/transformers/main/en/quantization/torchao) with 8-bit embeddings and 8-bit dynamic activations with 4-bit weight linears (8da4w).
+[Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B) is quantized by the PyTorch team using [torchao](https://huggingface.co/docs/transformers/main/en/quantization/torchao) with 8-bit embeddings and 8-bit dynamic activations with 4-bit weight linears (INT8-INT4).
 The model is suitable for mobile deployment with [ExecuTorch](https://github.com/pytorch/executorch).
 
-We provide the [quantized pte](https://huggingface.co/pytorch/Qwen3-4B-8da4w/blob/main/qwen3-4B-8da4w-1024-cxt.pte) for direct use in ExecuTorch.
+We provide the [quantized pte](https://huggingface.co/pytorch/Qwen3-4B-INT8-INT4/blob/main/qwen3-4B-8da4w-1024-cxt.pte) for direct use in ExecuTorch.
 (The provided pte file is exported with a max_seq_length/max_context_length of 1024; if you wish to change this, re-export the quantized model following the instructions in [Exporting to ExecuTorch](#exporting-to-executorch).)  
 
 # Running in a mobile app
-The [pte file](https://huggingface.co/pytorch/Qwen3-4B-8da4w/blob/main/qwen3-4B-8da4w-1024-cxt.pte) can be run with ExecuTorch on a mobile phone.  See the [instructions](https://pytorch.org/executorch/main/llm/llama-demo-ios.html) for doing this in iOS.
+The [pte file](https://huggingface.co/pytorch/Qwen3-4B-INT8-INT4/blob/main/qwen3-4B-8da4w-1024-cxt.pte) can be run with ExecuTorch on a mobile phone.  See the [instructions](https://pytorch.org/executorch/main/llm/llama-demo-ios.html) for doing this in iOS.
 On iPhone 15 Pro, the model runs at 14.8 tokens/sec and uses 3379 Mb of memory.
 
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/66049fc71116cebd1d3bdcf4/eVHB7fVllmwVauKJvGu0d.png)
@@ -130,7 +130,7 @@ tokenizer = AutoTokenizer.from_pretrained(model_id)
 
 # Push to hub
 MODEL_NAME = model_id.split("/")[-1]
-save_to = f"{USER_ID}/{MODEL_NAME}-8da4w"
+save_to = f"{USER_ID}/{MODEL_NAME}-INT8-INT4"
 quantized_model.push_to_hub(save_to, safe_serialization=False)
 tokenizer.push_to_hub(save_to)
 
@@ -171,7 +171,7 @@ Hello! I'm Qwen, a large language model developed by Alibaba Cloud. While I don'
 
 | Benchmark                        |                |                           |
 |----------------------------------|----------------|---------------------------|
-|                                  | Qwen3-4B       | Qwen3-4B-8da4w            | 
+|                                  | Qwen3-4B       | Qwen3-4B-INT8-INT4            | 
 | **Popular aggregated benchmark** |                |                           |
 | mmlu                             | 68.38          | 66.74                     |
 | mmlu_pro                         | 49.71          | 46.73                     |
@@ -198,9 +198,9 @@ Need to install lm-eval from source: https://github.com/EleutherAI/lm-evaluation
 lm_eval --model hf --model_args pretrained=Qwen3/Qwen3-4B --tasks mmlu --device cuda:0 --batch_size auto
 ```
 
-## int8 dynamic activation and int4 weight quantization (8da4w)
+## int8 dynamic activation and int4 weight quantization (INT8-INT4)
 ```Shell
-lm_eval --model hf --model_args pretrained=pytorch/Qwen3-4B-8da4w --tasks mmlu --device cuda:0 --batch_size auto
+lm_eval --model hf --model_args pretrained=pytorch/Qwen3-4B-INT8-INT4 --tasks mmlu --device cuda:0 --batch_size auto
 ```
 </details>
 
@@ -209,10 +209,10 @@ lm_eval --model hf --model_args pretrained=pytorch/Qwen3-4B-8da4w --tasks mmlu -
 We can run the quantized model on a mobile phone using [ExecuTorch](https://github.com/pytorch/executorch).
 Once ExecuTorch is [set-up](https://pytorch.org/executorch/main/getting-started.html), exporting and running the model on device is a breeze.
 
-We first convert the [quantized checkpoint](https://huggingface.co/pytorch/Qwen3-4B-8da4w/blob/main/pytorch_model.bin) to one ExecuTorch's LLM export script expects by renaming some of the checkpoint keys.
-The following script does this for you.  We have uploaded the converted checkpoint [pytorch_model_converted.bin](https://huggingface.co/pytorch/Qwen3-4B-8da4w/blob/main/pytorch_model_converted.bin) for convenience.
+We first convert the [quantized checkpoint](https://huggingface.co/pytorch/Qwen3-4B-INT8-INT4/blob/main/pytorch_model.bin) to one ExecuTorch's LLM export script expects by renaming some of the checkpoint keys.
+The following script does this for you.  We have uploaded the converted checkpoint [pytorch_model_converted.bin](https://huggingface.co/pytorch/Qwen3-4B-INT8-INT4/blob/main/pytorch_model_converted.bin) for convenience.
 ```Shell
-python -m executorch.examples.models.qwen3.convert_weights $(huggingface-cli download pytorch/Qwen3-4B-8da4w) pytorch_model_converted.bin
+python -m executorch.examples.models.qwen3.convert_weights $(huggingface-cli download pytorch/Qwen3-4B-INT8-INT4) pytorch_model_converted.bin
 ```
 
 Once the checkpoint is converted, we can export to ExecuTorch's pte format with the XNNPACK delegate.