README.md

---
base_model:
- microsoft/Phi-4-mini-instruct
language:
- multilingual
library_name: transformers
license: bsd-3-clause
pipeline_tag: text-generation
tags:
- torchao
- phi
- phi4
- nlp
- code
- math
- chat
- conversational
---

This repository hosts the **Phi4-mini-instruct** model quantized with [torchao](https://huggingface.co/docs/transformers/main/en/quantization/torchao) 
using int4 weight-only quantization and the [awq](https://arxiv.org/abs/2306.00978) algorithm.
This work is brought to you by the PyTorch team. This model can be used directly or served using [vLLM](https://docs.vllm.ai/en/latest/) for 56% VRAM reduction (3.95 GB needed) 
and 1.17x speedup on H100 GPUs. The model is calibrated with 2 samples from `mmlu_pro` task to recover the accuracy for `mmlu_pro` specifically. It recovered accuracy from `mmlu_pro`
from INT4 checkpoint from 36.98 to 43.13, while bfloat16 baseline accuracy is 46.43.

# Inference with vLLM
Install vllm nightly and torchao nightly to get some recent changes:
```
pip install --pre torchao torch vllm --index-url https://download.pytorch.org/whl/nightly/cu128
```

## Serving
Then we can serve with the following command:
```Shell
# Server
export MODEL=pytorch/Phi-4-mini-instruct-AWQ-INT4
VLLM_DISABLE_COMPILE_CACHE=1 vllm serve $MODEL --tokenizer $MODEL -O3
```

```Shell
# Client
curl http://localhost:8000/v1/chat/completions -H "Content-Type: application/json" -d '{
  "model": "pytorch/Phi-4-mini-instruct-AWQ-INT4",
  "messages": [
    {"role": "user", "content": "Give me a short introduction to large language models."}
  ],
  "temperature": 0.6,
  "top_p": 0.95,
  "top_k": 20,
  "max_tokens": 32768
}'
```

Note: please use `VLLM_DISABLE_COMPILE_CACHE=1` to disable compile cache when running this code, e.g. `VLLM_DISABLE_COMPILE_CACHE=1 python example.py`, since there are some issues with the composability of compile in vLLM and torchao,
this is expected be resolved in pytorch 2.8.

# Inference with Transformers

Install the required packages:
```Shell
# for compatibility with modeling file in checkpoint
pip install transformers==4.53.0
pip install --pre torchao torch --index-url https://download.pytorch.org/whl/nightly/cu126
pip install accelerate
```

Example:
```Py
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "pytorch/Phi-4-mini-instruct-AWQ-INT4"

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# prepare the model input
prompt = "Give me a short introduction to large language model."
messages = [
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True # Switches between thinking and non-thinking modes. Default is True.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()

# parsing thinking content
try:
    # rindex finding 151668 (</think>)
    index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
    index = 0

thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("")

print("thinking content:", thinking_content)
print("content:", content)
```




# Quantization Recipe

Install the required packages:
```Shell
# for compatibility with modeling file in checkpoint
pip install transformers==4.53.0
pip install accelerate
pip install --pre torchao torch --index-url https://download.pytorch.org/whl/nightly/cu128
```



Use the following code to get the quantized model:
```Py
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TorchAoConfig

model_id = "microsoft/Phi-4-mini-instruct"
model_to_quantize = "microsoft/Phi-4-mini-instruct"


from torchao.quantization import Int4WeightOnlyConfig, quantize_
from torchao.prototype.awq import (
    AWQConfig,
)
from torchao._models._eval import TransformerEvalWrapper
model = AutoModelForCausalLM.from_pretrained(
    model_to_quantize,
    device_map="auto",
    torch_dtype=torch.bfloat16,
)
tokenizer = AutoTokenizer.from_pretrained(model_id)

# Note: this is only compatible with H100
base_config = Int4WeightOnlyConfig(group_size=128)
# for A100, please use the following for base_config:
# base_config = Int4WeightOnlyConfig(group_size=128, int4_packing_format="tile_packed_to_4d", int4_choose_qparams_algorithm="hqq")
quant_config = AWQConfig(base_config, step="prepare")
quantize_(
    model,
    quant_config,
)
tasks = ["mmlu_pro"]
calibration_limit = 2
TransformerEvalWrapper(
    model=model,
    tokenizer=tokenizer,
    max_seq_length=max_seq_length,
).run_eval(
    tasks=tasks,
    limit=calibration_limit,
)
quant_config = AWQConfig(base_config, step="convert")
quantize_(model, quant_config)

quantized_model = model
quant_config = AWQConfig(base_config, step="prepare_for_loading")
quantized_model.config.quantization_config = TorchAoConfig(quant_config)


# Push to hub
USER_ID = "YOUR_USER_ID"
MODEL_NAME = model_id.split("/")[-1]
save_to = f"{USER_ID}/{MODEL_NAME}-AWQ-INT4"
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):])
```

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

# Model Quality
We rely on [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) to evaluate the quality of the quantized model. Here we only run on mmlu for sanity check.

Since the checkpoint is tuned on `mmlu_pro`, we check against the accuracy for `mmlu_pro`:

| Benchmark                        |                |                           |                           |
|----------------------------------|----------------|---------------------------|---------------------------|
|                                  | microsoft/Phi-4-mini-instruct   | pytorch/Phi-4-mini-instruct-INT4 | pytorch/Phi-4-mini-instruct-AWQ-INT4 
| mmlu_pro                         | 46.43   | 36.98                      |    43.13        |


<details>
<summary> Reproduce Model Quality Results </summary>

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 mmlu --device cuda:0 --batch_size 8
```

## AWQ-INT4
```Shell
export MODEL=pytorch/Phi-4-mini-instruct-AWQ-INT4
lm_eval --model hf --model_args pretrained=$MODEL --tasks mmlu --device cuda:0 --batch_size 8
```
</details>




# Peak Memory Usage

## Results

| Benchmark        |                |                                |                                |
|------------------|----------------|--------------------------------|--------------------------------|
|                  | microsoft/Phi-4-mini-instruct   | jerryzh168/Phi-4-mini-instruct-INT4 | pytorch/Phi-4-mini-instruct-AWQ-INT4              |
| Peak Memory (GB) | 8.91   | 3.02 (66% reduction)    | 3.95 (55.67% reduction) |



<details>
<summary> Reproduce Peak Memory Usage Results </summary>

We can use the following code to get a sense of peak memory usage during inference:

```Py
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TorchAoConfig

# use "microsoft/Phi-4-mini-instruct" or "pytorch/Phi-4-mini-instruct-AWQ-INT4"
model_id = "pytorch/Phi-4-mini-instruct-AWQ-INT4"
quantized_model = AutoModelForCausalLM.from_pretrained(model_id, device_map="cuda:0", torch_dtype=torch.bfloat16)
tokenizer = AutoTokenizer.from_pretrained(model_id)

torch.cuda.reset_peak_memory_stats()

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):])

mem = torch.cuda.max_memory_reserved() / 1e9
print(f"Peak Memory Usage: {mem:.02f} GB")
```

</details>




# Model Performance

## Results (H100 machine)
| Benchmark (Latency)              |                |                          |                          |
|----------------------------------|----------------|--------------------------|--------------------------|
|                                  | microsoft/Phi-4-mini-instruct | jerryzh168/Phi-4-mini-instruct-INT4 | pytorch/Phi-4-mini-instruct-AWQ-INT4
| latency (batch_size=1)           | 1.61s          | 1.33s (1.21x speedup)    | 1.37s (1.17x speedup) |
| latency (batch_size=256)         | 5.31s          | 5.38s (0.99x speedup)    | 5.44s (0.98x speedup) |

Note: it's expected that the awq-int4 checkpoint is slower when batch size is 256 since the problem is not memory bound but becomes compute bound when batch size is larger, while
int4 weight only checkpoint is only expected to have speedup for memory bound situations.
Note: we are comparing to jerryzh168/Phi-4-mini-instruct-INT4 which is a checkpoint for H100, since the AWQ-INT4 is using the new INT4 config that's optimized for H100 that doesn't regress the performance for batch size 256. It's possible to generate
AWQ-INT4 for A100 as well using `Int4WeightOnlyConfig(group_size=128, int4_packing_foramt="tile_packed_to_4d", int4_choose_qparams_algorithm="hqq")`

<details>
<summary> Reproduce Model Performance Results </summary>

## Setup

Get vllm source code:
```Shell
git clone [email protected]:vllm-project/vllm.git
```

Install vllm
```
VLLM_USE_PRECOMPILED=1 pip install --editable .
```

Run the benchmarks under `vllm` root folder:

## benchmark_latency

### baseline
```Shell
export MODEL=microsoft/Phi-4-mini-instruct
vllm bench latency --input-len 256 --output-len 256 --model $MODEL --batch-size 1
```

### AWQ-INT4
```Shell
export MODEL=pytorch/Phi-4-mini-instruct-AWQ-INT4
VLLM_DISABLE_COMPILE_CACHE=1 vllm bench latency --input-len 256 --output-len 256 --model $MODEL --batch-size 1
```
</details>

# Paper: TorchAO: PyTorch-Native Training-to-Serving Model Optimization
The model's quantization is powered by **TorchAO**, a framework presented in the paper [TorchAO: PyTorch-Native Training-to-Serving Model Optimization](https://huggingface.co/papers/2507.16099).

**Abstract:** We present TorchAO, a PyTorch-native model optimization framework leveraging quantization and sparsity to provide an end-to-end, training-to-serving workflow for AI models. TorchAO supports a variety of popular model optimization techniques, including FP8 quantized training, quantization-aware training (QAT), post-training quantization (PTQ), and 2:4 sparsity, and leverages a novel tensor subclass abstraction to represent a variety of widely-used, backend agnostic low precision data types, including INT4, INT8, FP8, MXFP4, MXFP6, and MXFP8. TorchAO integrates closely with the broader ecosystem at each step of the model optimization pipeline, from pre-training (TorchTitan) to fine-tuning (TorchTune, Axolotl) to serving (HuggingFace, vLLM, SGLang, ExecuTorch), connecting an otherwise fragmented space in a single, unified workflow. TorchAO has enabled recent launches of the quantized Llama 3.2 1B/3B and LlamaGuard3-8B models and is open-source at this https URL .

# Resources
*   **Official TorchAO GitHub Repository:** [https://github.com/pytorch/ao](https://github.com/pytorch/ao)
*   **TorchAO Documentation:** [https://docs.pytorch.org/ao/stable/index.html](https://docs.pytorch.org/ao/stable/index.html)


# 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.