Update README.md

README.md

@@ -1,199 +1,360 @@
 ---
 library_name: transformers
-tags: []
+tags:
+- torchao
+- code
+- math
+- chat
+- conversational
+language:
+- multilingual
+license: apache-2.0
+license_link: https://huggingface.co/Qwen/Qwen3-8B/blob/main/LICENSE
+pipeline_tag: text-generation
+base_model:
+- Qwen/Qwen3-8B
 ---
 
-# Model Card for Model ID
+[Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) quantized with [torchao](https://huggingface.co/docs/transformers/main/en/quantization/torchao) int4 weight only quantization, using [hqq](https://mobiusml.github.io/hqq_blog/) algorithm for improved accuracy, by PyTorch team.
+(UPDATE) Use it directly or serve using [vLLM](https://docs.vllm.ai/en/latest/) for 67% VRAM reduction and 12-20% speedup on A100 GPUs.
+
+# Inference with vLLM
+Install vllm nightly and torchao nightly to get some recent changes:
+```
+pip install vllm --pre --extra-index-url https://wheels.vllm.ai/nightly
+pip install torchao
+```
+
+## Code Example
+```Py
+from vllm import LLM, SamplingParams
+
+# Sample prompts.
+prompts = [
+    "Hello, my name is",
+    "The president of the United States is",
+    "The capital of France is",
+    "The future of AI is",
+]
+# Create a sampling params object.
+sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
+
+
+if __name__ == '__main__':
+    # Create an LLM.
+    llm = LLM(model="pytorch/Phi-4-mini-instruct-int4wo-hqq")
+    # Generate texts from the prompts.
+    # The output is a list of RequestOutput objects
+    # that contain the prompt, generated text, and other information.
+    outputs = llm.generate(prompts, sampling_params)
+    # Print the outputs.
+    print("\nGenerated Outputs:\n" + "-" * 60)
+    for output in outputs:
+        prompt = output.prompt
+        generated_text = output.outputs[0].text
+        print(f"Prompt:    {prompt!r}")
+        print(f"Output:    {generated_text!r}")
+        print("-" * 60)
+```
+
+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.
+
+## Serving
+Then we can serve with the following command:
+```Shell
+vllm serve pytorch/Phi-4-mini-instruct-int4wo-hqq --tokenizer microsoft/Phi-4-mini-instruct -O3
+```
+
+
+# Inference with Transformers
+
+Install the required packages:
+```Shell
+pip install git+https://github.com/huggingface/transformers@main
+pip install --pre torchao --index-url https://download.pytorch.org/whl/nightly/cu126
+pip install torch
+pip install accelerate
+```
+
+Example:
+```Py
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+ 
+torch.random.manual_seed(0)
+
+model_path = "pytorch/Phi-4-mini-instruct-int4wo-hqq"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_path,
+    device_map="auto",
+    torch_dtype="auto",
+    trust_remote_code=True,
+)
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+ 
+messages = [
+    {"role": "system", "content": "You are a helpful AI assistant."},
+    {"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"},
+    {"role": "assistant", "content": "Sure! Here are some ways to eat bananas and dragonfruits together: 1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey. 2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey."},
+    {"role": "user", "content": "What about solving an 2x + 3 = 7 equation?"},
+]
+ 
+pipe = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+)
+ 
+generation_args = {
+    "max_new_tokens": 500,
+    "return_full_text": False,
+    "temperature": 0.0,
+    "do_sample": False,
+}
+ 
+output = pipe(messages, **generation_args)
+print(output[0]['generated_text'])
+```
+
+# Quantization Recipe
+
+Install the required packages:
+```Shell
+pip install git+https://github.com/huggingface/transformers@main
+pip install --pre torchao --index-url https://download.pytorch.org/whl/nightly/cu126
+pip install torch
+pip install accelerate
+```
+
+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"
+
+from torchao.quantization import Int4WeightOnlyConfig
+quant_config = Int4WeightOnlyConfig(group_size=128, use_hqq=True)
+quantization_config = TorchAoConfig(quant_type=quant_config)
+quantized_model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.bfloat16, quantization_config=quantization_config)
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+# Push to hub
+USER_ID = "YOUR_USER_ID"
+MODEL_NAME = model_id.split("/")[-1]
+save_to = f"{USER_ID}/{MODEL_NAME}-int4wo-hqq"
+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.
+
+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 8
+```
+
+## int4 weight only quantization with hqq (int4wo-hqq)
+```Shell
+lm_eval --model hf --model_args pretrained=pytorch/Phi-4-mini-instruct-int4wo-hqq --tasks hellaswag --device cuda:0 --batch_size 8
+```
+
+| Benchmark                        |                |                           |
+|----------------------------------|----------------|---------------------------|
+|                                  | Phi-4-mini-ins | Phi-4-mini-ins-int4wo-hqq | 
+| **Popular aggregated benchmark** |                |                           |
+| mmlu (0-shot)                    | 66.73          |  63.56                    |
+| mmlu_pro (5-shot)                | 46.43          |  36.74                    |
+| **Reasoning**                    |                |                           |
+| arc_challenge (0-shot)           | 56.91          |  54.86                    |
+| gpqa_main_zeroshot               | 30.13          |  30.58                    |
+| HellaSwag                        | 54.57          |  53.54                    |
+| openbookqa                       | 33.00          |  34.40                    |
+| piqa (0-shot)	                   | 77.64          |  76.33                    |
+| social_iqa                       | 49.59          |  47.90                    |
+| truthfulqa_mc2 (0-shot)          | 48.39          |  46.44                    |
+| winogrande  (0-shot)             | 71.11          |  71.51                    |
+| **Multilingual**                 |                |                           |
+| mgsm_en_cot_en                   | 60.8           |  59.6                     |
+| **Math**                         |                |                           |
+| gsm8k (5-shot)                   | 81.88          |  74.37                    |
+| mathqa (0-shot)                  | 42.31          |  42.75                    |
+| **Overall**                      | **55.35**      | **53.28**                 |
+ 
+
+# Peak Memory Usage
+
+## Results
+
+| Benchmark        |                |                                |
+|------------------|----------------|--------------------------------|
+|                  | Phi-4 mini-Ins | Phi-4-mini-instruct-int4wo-hqq | 
+| Peak Memory (GB) | 8.91           | 2.98 (67% reduction)           |
+
+
+## Code Example
+
+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-int4wo-hqq"
+model_id = "pytorch/Phi-4-mini-instruct-int4wo-hqq"
+quantized_model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", 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")
+```
+
+# Model Performance
+
+Our int4wo is only optimized for batch size 1, so expect some slowdown with larger batch sizes, we expect this to be used in local server deployment for single or a few users where the decode tokens per second will matters more than the time to first token.
+
+## Results (A100 machine)
+| Benchmark (Latency)              |                |                          |
+|----------------------------------|----------------|--------------------------|
+|                                  | Phi-4 mini-Ins | phi4-mini-int4wo-hqq     | 
+| latency (batch_size=1)           | 2.46s          | 2.2s (12% speedup)       |
+| serving (num_prompts=1)          | 0.87 req/s     | 1.05 req/s (20% speedup) |
+
+Note the result of latency (benchmark_latency) is in seconds, and serving (benchmark_serving) is in number of requests per second.
+Int4 weight only is optimized for batch size 1 and short input and output token length, please stay tuned for models optimized for larger batch sizes or longer token length.
+
+## 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
+python benchmarks/benchmark_latency.py --input-len 256 --output-len 256 --model microsoft/Phi-4-mini-instruct --batch-size 1
+```
 
-<!-- Provide a quick summary of what the model is/does. -->
+### int4wo-hqq
+```Shell
+VLLM_DISABLE_COMPILE_CACHE=1 python benchmarks/benchmark_latency.py --input-len 256 --output-len 256 --model pytorch/Phi-4-mini-instruct-int4wo-hqq --batch-size 1
+```
 
+## benchmark_serving
 
+We benchmarked the throughput in a serving environment.
 
-## Model Details
+Download sharegpt dataset: 
 
-### Model Description
+```Shell
+wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
+```
 
-<!-- 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.
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+Other datasets can be found in: https://github.com/vllm-project/vllm/tree/main/benchmarks
 
-### Model Sources [optional]
+Note: you can change the number of prompts to be benchmarked with `--num-prompts` argument for `benchmark_serving` script.
 
-<!-- Provide the basic links for the model. -->
+### baseline
+Server:
+```Shell
+vllm serve microsoft/Phi-4-mini-instruct --tokenizer microsoft/Phi-4-mini-instruct -O3
+```
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+Client:
+```Shell
+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
+```
 
-## Uses
+### int4wo-hqq
+Server:
+```Shell
+VLLM_DISABLE_COMPILE_CACHE=1 vllm serve pytorch/Phi-4-mini-instruct-int4wo-hqq --tokenizer microsoft/Phi-4-mini-instruct -O3 --pt-load-map-location cuda:0
+```
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+Client:
+```Shell
+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 pytorch/Phi-4-mini-instruct-int4wo-hqq --num-prompts 1
+```
 
-### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/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.
 
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-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.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
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-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
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-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
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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).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
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-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
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-
-### Compute Infrastructure
-
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-#### Hardware
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-#### Software
-
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-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
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-
-**APA:**
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-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-## More Information [optional]
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-## Model Card Authors [optional]
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-## Model Card Contact
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+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.