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@@ -3,197 +3,112 @@ library_name: transformers
 tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-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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-### Model Sources [optional]
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-## Uses
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-### Direct 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.
-## How to Get Started with the Model
-Use the code below to get started with the model.
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-## Training Details
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-### Training Procedure
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-#### Training Hyperparameters
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-## Evaluation
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-#### Factors
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-### Results
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-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
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-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-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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 tags: []
 ---
+# Quantization Recipe
+We used following code to get the quantized model:
+```
+model_id = "microsoft/Phi-4-mini-instruct"
+from transformers import (
+    AutoModelForCausalLM,
+    AutoProcessor,
+    AutoTokenizer,
+)
+from torchao.quantization.quant_api import (
+    Int8DynamicActivationIntxWeightConfig,
+    MappingType,
+    quantize_,
+)
+from torchao.quantization.granularity import PerGroup
+import torch
+model = AutoModelForCausalLM.from_pretrained(
+    model_id, torch_dtype="auto", device_map="auto"
+)
+linear_config = Int8DynamicActivationIntxWeightConfig(
+    weight_dtype=torch.int4,
+    weight_granularity=PerGroup(32),
+    weight_mapping_type=MappingType.SYMMETRIC,
+)
+quantize_(
+	model,
+    linear_config,
+)
+state_dict = model.state_dict()
+torch.save(state_dict, "phi4-mini-8dq4w.pt")
+```
+# Model Quality
+We rely on [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) to evaluate the quality of the quantized model.
+# baseline
+```
+lm_eval --model hf --model_args pretrained=microsoft/Phi-4-mini-instruct --tasks hellaswag --device cuda:0 --batch_size 8
+```
+# 8dq4w
+```
+import lm_eval
+from lm_eval import evaluator
+from lm_eval.utils import (
+    make_table,
+)
+# model is after calling quantize_ as we do in the recipe
+# quantize_(
+#	model,
+#    linear_config,
+#)
+lm_eval_model = lm_eval.models.huggingface.HFLM(pretrained=model, batch_size=8)
+results = evaluator.simple_evaluate(
+    lm_eval_model, tasks=["hellaswag"], device="cuda:0", batch_size="auto"
+)
+print(make_table(results))
+```
+| Benchmark                        |             |                   |
+|----------------------------------|-------------|-------------------|
+|                                  | Phi-4 mini-Ins | phi4-mini-8dq4w |
+| **Popular aggregated benchmark** |             |                   |
+| **Reasoning**                    |             |                   |
+| HellaSwag                        | 54.57        | 53.19            |
+| **Multilingual**                 |             |                   |
+| **Math**                         |             |                   |
+| **Overall**                      | **TODO**    | **TODO**          |
+# Exporting to ExecuTorch
+Exporting to ExecuTorch requires you clone and install [ExecuTorch](https://github.com/pytorch/executorch).
+## Convert quantized checkpoint to ExecuTorch's format
+python -m executorch.examples.models.phi_4_mini.convert_weights phi4-mini-8dq4w.pt phi4-mini-8dq4w-converted.pt
+## Export to an ExecuTorch *.pte with XNNPACK
+PARAMS="executorch/examples/models/phi_4_mini/config.json"
+python -m executorch.examples.models.llama.export_llama \
+  --model "phi_4_mini" \
+  --checkpoint "phi4-mini-8dq4w-converted.pt" \
+  --params "$PARAMS" \
+  -kv \
+  --use_sdpa_with_kv_cache \
+  -X \
+  --metadata '{"get_bos_id":128000, "get_eos_ids":[128009, 128001]}' \
+  --output_name="phi4-mini-8dq4w.pte"
+## Run model with pybindings
+export TOKENIZER="/path/to/tokenizer.json"
+export TOKENIZER_CONFIG="/path/to/tokenizer_config.json"
+export PROMPT="<|system|><|end|><|user|>What is in a california roll?<|end|><|assistant|>"
+python -m executorch.examples.models.llama.runner.native \
+  --model phi_4_mini \
+  --pte phi4-mini-8dq4w.pte \
+  -kv \
+  --tokenizer ${TOKENIZER} \
+  --tokenizer_config ${TOKENIZER_CONFIG} \
+  --prompt "${PROMPT}" \
+  --params "${PARAMS}" \
+  --max_len 128 \
+  --temperature 0