Update README.md

README.md

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-# Model Card for Model ID
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-<!-- Provide a quick summary of what the model is/does. -->
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-## Model Details
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-### Model Description
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-<!-- Provide a longer summary of what this model is. -->
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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-- **Developed by:** [More Information Needed]
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-## Uses
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-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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-### Direct Use
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-### Downstream Use [optional]
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-### Out-of-Scope Use
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-## Bias, Risks, and Limitations
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-### Recommendations
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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-## How to Get Started with the Model
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-Use the code below to get started with the model.
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-## Training Details
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-### Training Data
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-### Training Procedure
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-#### Preprocessing [optional]
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-#### Training Hyperparameters
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-#### Speeds, Sizes, Times [optional]
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-## Evaluation
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-### Testing Data, Factors & Metrics
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-#### Testing Data
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-#### Factors
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-#### Metrics
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-### Results
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-#### Summary
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-## Model Examination [optional]
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-## Environmental Impact
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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-## Technical Specifications [optional]
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-### Model Architecture and Objective
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-## Citation [optional]
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-## Glossary [optional]
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-## More Information [optional]
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-## Model Card Authors [optional]
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+---
+library_name: transformers
+tags:
+- torchao
+- qwen
+- qwen3
+- nlp
+- code
+- math
+- chat
+- conversational
+license: mit
+language:
+- multilingual
+base_model:
+- Qwen/Qwen3-4B
+pipeline_tag: text-generation
+---
 
-## Model Card Contact
 
-[More Information Needed]
+[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).
+The model is suitable for mobile deployment with [ExecuTorch](https://github.com/pytorch/executorch).
+
+We provide the [quantized pte](TODO: ADD LINK) 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](TODO: ADD LINK) 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 [TODO: ADD] tokens/sec and uses [TODO: ADD] Mb of memory.
+
+[TODO: ADD SCREENSHOT]
+
+# Quantization Recipe
+
+First need to install the required packages:
+```Shell
+pip install git+https://github.com/huggingface/transformers@main
+pip install torchao
+```
+
+## Untie Embedding Weights
+We want to quantize the embedding and lm_head differently.  Since those layers are tied, we first need to untie the model:
+
+```Py
+from transformers import (
+  AutoModelForCausalLM,
+  AutoProcessor,
+  AutoTokenizer,
+)
+import torch
+
+model_id = "Qwen/Qwen3-4B"
+untied_model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+print(untied_model)
+from transformers.modeling_utils import find_tied_parameters
+print("tied weights:", find_tied_parameters(untied_model))
+if getattr(untied_model.config.get_text_config(decoder=True), "tie_word_embeddings"):
+    setattr(untied_model.config.get_text_config(decoder=True), "tie_word_embeddings", False)
+
+untied_model._tied_weights_keys = []
+untied_model.lm_head.weight = torch.nn.Parameter(untied_model.lm_head.weight.clone())
+
+print("tied weights:", find_tied_parameters(untied_model))
+
+USER_ID = "YOUR_USER_ID"
+MODEL_NAME = model_id.split("/")[-1]
+save_to = f"{USER_ID}/{MODEL_NAME}-untied-weights"
+
+untied_model.push_to_hub(save_to)
+tokenizer.push_to_hub(save_to)
+
+# or save locally
+save_to_local_path = f"{MODEL_NAME}-untied-weights"
+untied_model.save_pretrained(save_to_local_path)
+tokenizer.save_pretrained(save_to)
+```
+
+Note: to `push_to_hub` you need to run
+```Shell
+pip install -U "huggingface_hub[cli]"
+huggingface-cli login
+```
+and use a token with write access, from https://huggingface.co/settings/tokens
+
+## Quantization
+
+We used following code to get the quantized model:
+
+```Py
+from transformers import (
+  AutoModelForCausalLM,
+  AutoProcessor,
+  AutoTokenizer,
+  TorchAoConfig,
+)
+from torchao.quantization.quant_api import (
+    IntxWeightOnlyConfig,
+    Int8DynamicActivationIntxWeightConfig,
+    AOPerModuleConfig,
+    quantize_,
+)
+from torchao.quantization.granularity import PerGroup, PerAxis
+import torch
+
+# we start from the model with untied weights
+model_id = "Qwen/Qwen3-4B"
+USER_ID = "YOUR_USER_ID"
+MODEL_NAME = model_id.split("/")[-1]
+untied_model_id = f"{USER_ID}/{MODEL_NAME}-untied-weights"
+untied_model_local_path = f"{MODEL_NAME}-untied-weights"
+
+embedding_config = IntxWeightOnlyConfig(
+    weight_dtype=torch.int8,
+    granularity=PerAxis(0),
+)
+linear_config = Int8DynamicActivationIntxWeightConfig(
+    weight_dtype=torch.int4,
+    weight_granularity=PerGroup(32),
+    weight_scale_dtype=torch.bfloat16,
+)
+quant_config = AOPerModuleConfig({"_default": linear_config, "model.embed_tokens": embedding_config})
+quantization_config = TorchAoConfig(quant_type=quant_config, include_embedding=True, untie_embedding_weights=True, modules_to_not_convert=[])
+
+# either use `untied_model_id` or `untied_model_local_path`
+quantized_model = AutoModelForCausalLM.from_pretrained(untied_model_id, torch_dtype=torch.float32, device_map="auto", quantization_config=quantization_config)
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+# Push to hub
+MODEL_NAME = model_id.split("/")[-1]
+save_to = f"{USER_ID}/{MODEL_NAME}-untied-8da4w"
+quantized_model.push_to_hub(save_to, safe_serialization=False)
+tokenizer.push_to_hub(save_to)
+
+# Manual testing
+prompt = "Hey, are you conscious? Can you talk to me?"
+messages = [
+    {
+        "role": "system",
+        "content": "",
+    },
+    {"role": "user", "content": prompt},
+]
+templated_prompt = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+)
+print("Prompt:", prompt)
+print("Templated prompt:", templated_prompt)
+inputs = tokenizer(
+    templated_prompt,
+    return_tensors="pt",
+).to("cuda")
+generated_ids = quantized_model.generate(**inputs, max_new_tokens=128)
+output_text = tokenizer.batch_decode(
+    generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
+)
+print("Response:", output_text[0][len(prompt):])
+```
+
+The response from the manual testing is:
+
+```
+TODO: ADD RESPONSE
+```
+
+# Model Quality
+
+We rely on [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) to evaluate the quality of the quantized model.
+
+Need to install lm-eval from source: https://github.com/EleutherAI/lm-evaluation-harness#install
+
+## baseline
+```Shell
+lm_eval --model hf --model_args pretrained=microsoft/Phi-4-mini-instruct --tasks hellaswag --device cuda:0 --batch_size auto
+```
+
+## int8 dynamic activation and int4 weight quantization (8da4w)
+```Shell
+lm_eval --model hf --model_args pretrained=pytorch/Phi-4-mini-instruct-8da4w --tasks hellaswag --device cuda:0 --batch_size auto
+```
+
+| Benchmark                        |                |                           |
+|----------------------------------|----------------|---------------------------|
+|                                  | Qwen3-4B       | Qwen3-4B-8da4w | 
+| **Popular aggregated benchmark** |                |                           |
+| mmlu                    |          |                  |
+| mmlu_pro                |         |                  |
+| bbh
+| **Reasoning**                    |                |                           |
+| gpqa_main_zeroshot               |   |                   |
+| mgsm_en_cot_en
+| **Multilingual**                 |                |                           |
+| m_mmlu                   |          |                      |
+| **Math**                         |                |                           |
+| gsm8k                   |           |                      |
+| leaderboard_math_hard                  |           |                      |
+| **Overall**                      |           |                     |
+
+
+# Exporting to ExecuTorch
+
+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](TODO: ADD LINK) 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](TODO: ADD LINK) for convenience.
+```Shell
+python -m executorch.examples.models.qwen3.convert_weights pytorch_model.bin pytorch_model_converted.bin
+```
+
+Once the checkpoint is converted, we can export to ExecuTorch's pte format with the XNNPACK delegate.
+The below command exports with a max_seq_length/max_context_length of 1024, but it can be changed as desired.
+
+```Shell
+PARAMS="executorch/examples/models/qwen3/4b_config.json"
+python -m executorch.examples.models.llama.export_llama \
+  --model "qwen3-4b" \
+  --checkpoint "pytorch_model_converted.bin" \
+  --params "$PARAMS" \
+  -kv \
+  --use_sdpa_with_kv_cache \
+  -d fp32
+  -X \
+  --metadata '{"get_bos_id":199999, "get_eos_ids":[200020,199999]}' \
+  --max_seq_length 1024 \
+  --max_context_length 1024 \
+  --output_name="qwen3-4B-8da4w-1024-cxt.pte"
+```
+
+After that you can run the model in a mobile app (see [Running in a mobile app](#running-in-a-mobile-app)).
+
+# Disclaimer
+PyTorch has not performed safety evaluations or red teamed the quantized models. Performance characteristics, outputs, and behaviors may differ from the original models. Users are solely responsible for selecting appropriate use cases, evaluating and mitigating for accuracy, safety, and fairness, ensuring security, and complying with all applicable laws and regulations.
+
+Nothing contained in this Model Card should be interpreted as or deemed a restriction or modification to the licenses the models are released under, including any limitations of liability or disclaimers of warranties provided therein.