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-# 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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-### 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
-### Training Data
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-## Evaluation
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-#### Summary
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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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 tags: []
 ---
+[Phi4-mini](https://huggingface.co/microsoft/Phi-4-mini-instruct) model quantized with [torchao](https://huggingface.co/docs/transformers/main/en/quantization/torchao) float8 dynamic activation and float8 weight quantization (per row granularity), by PyTorch team.
+# Quantization Recipe
+We used following code to get the quantized model:
+```
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, TorchAoConfig
+model_id = "microsoft/Phi-4-mini-instruct"
+from torchao.quantization import Float8DynamicActivationFloat8WeightConfig, PerRow
+quant_config = Float8DynamicActivationFloat8WeightConfig(granularity=PerRow())
+quantization_config = TorchAoConfig(quant_type=quant_config)
+quantized_model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", quantization_config=quantization_config)
+# Push to hub
+USER_ID = "YOUR_USER_ID"
+save_to = "{USER_ID}/{model_id}-int4wo"
+quantized_model.push_to_hub(save_to, safe_serialization=False)
+# Manual Testing
+messages = [
+    {"role": "system", "content": "You are a medieval knight and must provide explanations to modern people."},
+    {"role": "user", "content": "How should I explain the Internet?"},
+]
+prompt = "Hey, are you conscious? Can you talk to me?"
+inputs = tokenizer(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(output_text)
+# Local Benchmark
+import torch.utils.benchmark as benchmark
+from torchao.utils import benchmark_model
+import torchao
+def benchmark_fn(f, *args, **kwargs):
+    # Manual warmup
+    for _ in range(2):
+        f(*args, **kwargs)
+    t0 = benchmark.Timer(
+        stmt="f(*args, **kwargs)",
+        globals={"args": args, "kwargs": kwargs, "f": f},
+        num_threads=torch.get_num_threads(),
+    )
+    return f"{(t0.blocked_autorange().mean):.3f}"
+torchao.quantization.utils.recommended_inductor_config_setter()
+quantized_model = torch.compile(quantized_model, mode="max-autotune")
+print(f"{save_to} model:", benchmark_fn(quantized_model.generate, **inputs, max_new_tokens=128))
+```
+# Model Quality
+We rely on [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) to evaluate the quality of the quantized model.
+# Installing the nightly version to get most recent updates
+```
+pip install git+https://github.com/EleutherAI/lm-evaluation-harness
+```
+# baseline
+```
+lm_eval --model hf --model_args pretrained=microsoft/Phi-4-mini-instruct --tasks hellaswag --device cuda:0 --batch_size 8
+```
+# float8dq
+```
+lm_eval --model hf --model_args pretrained=jerryzh168/phi4-mini-float8dq --tasks hellaswag --device cuda:0 --batch_size 8
+```
+`TODO: more complete eval results`
+| Benchmark                        |             |                   |
+|----------------------------------|-------------|-------------------|
+|                                  | Phi-4 mini-Ins | phi4-mini-float8dq |
+| **Popular aggregated benchmark** |             |                   |
+| **Reasoning**                    |             |                   |
+| HellaSwag                        | 54.57       | 54.55             |
+| **Multilingual**                 |             |                   |
+| **Math**                         |             |                   |
+| **Overall**                      | **TODO**    | **TODO**          |
+# Model Performance
+# Install latest vllm to get the most recent changes
+```
+pip install git+https://github.com/vllm-project/vllm.git
+```
+# Download dataset
+Download sharegpt dataset: `wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json`
+Other datasets can be found in: https://github.com/vllm-project/vllm/tree/main/benchmarks
+# benchmark_latency
+## baseline
+```
+python benchmarks/benchmark_latency.py --input-len 256 --output-len 256 --model microsoft/Phi-4-mini-instruct --batch-size 1
+```
+## float8dq
+```
+python benchmarks/benchmark_latency.py --input-len 256 --output-len 256 --model jerryzh168/phi4-mini-float8dq --batch-size 1
+```
+# benchmark_serving
+We also benchmarked the throughput in a serving environment.
+## baseline
+Server:
+```
+vllm serve microsoft/Phi-4-mini-instruct --tokenizer microsoft/Phi-4-mini-instruct -O3
+```
+Client:
+```
+python benchmarks/benchmark_serving.py --backend vllm --dataset-name sharegpt --tokenizer microsoft/Phi-4-mini-instruct --dataset-path ./ShareGPT_V3_unfiltered_cleaned_split.json --model microsoft/Phi-4-mini-instruct --num-prompts 1
+```
+## float8dq
+Server:
+```
+vllm serve jerryzh168/phi4-mini-float8dq --tokenizer microsoft/Phi-4-mini-instruct -O3
+```
+Client:
+```
+python benchmarks/benchmark_serving.py --backend vllm --dataset-name sharegpt --tokenizer microsoft/Phi-4-mini-instruct --dataset-path ./ShareGPT_V3_unfiltered_cleaned_split.json --model jerryzh168/phi4-mini-int4wo-hqq --num-prompts 1
+```
+# Serving with vllm
+We can use the same command we used in serving benchmarks to serve the model with vllm
+```
+vllm serve jerryzh168/phi4-mini-float8dq --tokenizer microsoft/Phi-4-mini-instruct -O3
+```