README.md

---
library_name: transformers
tags: []
---

[Phi4-mini](https://huggingface.co/microsoft/Phi-4-mini-instruct) model quantized with [torchao](https://huggingface.co/docs/transformers/main/en/quantization/torchao) int4 weight only quantization, 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 Int4WeightOnlyConfig
quant_config = Int4WeightOnlyConfig(group_size=128)
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)

# 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
```

# int4wo-hqq
```
lm_eval --model hf --model_args pretrained=jerryzh168/phi4-mini-int4wo-hqq --tasks hellaswag --device cuda:0 --batch_size 8
```

`TODO: more complete eval results`


| Benchmark                        |             |                   |
|----------------------------------|-------------|-------------------|
|                                  | Phi-4 mini-Ins | phi4-mini-int4wo | 
| **Popular aggregated benchmark** |             |                   |
| **Reasoning**                    |             |                   |
| HellaSwag                        | 54.57        | 53.54            |
| **Multilingual**                 |             |                   |
| **Math**                         |             |                   |
| **Overall**                      | **TODO**    | **TODO**          |
 
# Model Performance

Our int4wo is only optimized for batch size 1, so we'll only benchmark the batch size 1 performance with vllm.
For batch size N, please see our [gemlite checkpoint](https://huggingface.co/jerryzh168/phi4-mini-int4wo-gemlite).

# 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
```

## int4wo-hqq
```
python benchmarks/benchmark_latency.py --input-len 256 --output-len 256 --model jerryzh168/phi4-mini-int4wo-hqq --batch-size 1
```

# benchmark_serving

We also benchmarked the throughput with real 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
```

## int4wo-hqq
Server:
```
vllm serve jerryzh168/phi4-mini-int4wo-hqq --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-int4wo-hqq --tokenizer microsoft/Phi-4-mini-instruct -O3
```