--- library_name: transformers tags: - torchao - phi - phi4 - nlp - code - math - chat - conversational license: mit language: - multilingual base_model: - microsoft/Phi-4-mini-instruct pipeline_tag: text-generation --- [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. Use it directly, or serve using [vLLM](https://docs.vllm.ai/en/latest/) with 36% VRAM reduction, 15-20% speedup and little to no accuracy impact on H100. # Inference with vLLM Need to install vllm nightly to get some recent changes: ``` pip install vllm --pre --extra-index-url https://wheels.vllm.ai/nightly ``` ## Code Example ``` from vllm import LLM, SamplingParams llm = LLM(model="pytorch/Phi-4-mini-instruct-float8dq", trust_remote_code=True) 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?"}, ] sampling_params = SamplingParams( max_tokens=500, temperature=0.0, ) output = llm.chat(messages=messages, sampling_params=sampling_params) print(output[0].outputs[0].text) ``` ## Serving Then we can serve with the following command: ``` vllm serve pytorch/Phi-4-mini-instruct-float8dq --tokenizer microsoft/Phi-4-mini-instruct -O3 ``` # Inference with Transformers Install the required packages: ``` 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: ``` import torch from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline torch.random.manual_seed(0) model_path = "pytorch/Phi-4-mini-instruct-float8dq" 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: ``` 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: ``` 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", 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}-float8dq" 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):]) ``` # 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 ``` lm_eval --model hf --model_args pretrained=microsoft/Phi-4-mini-instruct --tasks hellaswag --device cuda:0 --batch_size 8 ``` ## float8 dynamic activation and float8 weight quantization (float8dq) ``` lm_eval --model hf --model_args pretrained=pytorch/Phi-4-mini-instruct-float8dq --tasks hellaswag --device cuda:0 --batch_size 8 ``` | Benchmark | | | |----------------------------------|----------------|---------------------| | | Phi-4 mini-Ins | phi4-mini-float8dq | | **Popular aggregated benchmark** | | | | mmlu (0-shot) | 66.73 | Pending | | mmlu_pro (5-shot) | 46.43 | Pending | | **Reasoning** | | | | arc_challenge (0-shot) | 56.91 | 56.66 | | gpqa_main_zeroshot | 30.13 | 29.46 | | HellaSwag | 54.57 | 54.55 | | openbookqa | 33.00 | 33.60 | | piqa (0-shot) | 77.64 | 77.48 | | social_iqa | 49.59 | 49.28 | | truthfulqa_mc2 (0-shot) | 48.39 | 48.09 | | winogrande (0-shot) | 71.11 | 72.77 | | **Multilingual** | | | | mgsm_en_cot_en | 60.8 | 60.0 | | **Math** | | | | gsm8k (5-shot) | 81.88 | 80.89 | | mathqa (0-shot) | 42.31 | 42.51 | | **Overall** | **TODO** | **TODO** | # Peak Memory Usage ## Results | Benchmark | | | |------------------|----------------|--------------------------------| | | Phi-4 mini-Ins | Phi-4-mini-instruct-float8dq | | Peak Memory (GB) | 8.91 | 5.70 (36% reduction) | ## Benchmark Peak Memory We can use the following code to get a sense of peak memory usage during inference: ``` import torch from transformers import AutoModelForCausalLM, AutoTokenizer, TorchAoConfig # use "microsoft/Phi-4-mini-instruct" or "pytorch/Phi-4-mini-instruct-float8dq" model_id = "microsoft/Phi-4-mini-instruct" 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 ## Results (H100 machine) | Benchmark | | | |----------------------------------|----------------|--------------------------| | | Phi-4 mini-Ins | phi4-mini-float8dq | | latency (batch_size=1) | 1.64s | 1.41s (16% speedup) | | latency (batch_size=128) | 3.1s | 2.72s (14% speedup) | | serving (num_prompts=1) | 1.35 req/s | 1.57 req/s (16% speedup) | | serving (num_prompts=1000) | 66.68 req/s | 80.53 req/s (21% speedup)| Note the result of latency (benchmark_latency) is in seconds, and serving (benchmark_serving) is in number of requests per second. ## benchmark_latency Need to install vllm nightly to get some recent changes ``` pip install vllm --pre --extra-index-url https://wheels.vllm.ai/nightly ``` Get vllm source code: ``` git clone git@github.com:vllm-project/vllm.git ``` Run the following under `vllm` root folder: ### 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 pytorch/Phi-4-mini-instruct-float8dq --batch-size 1 ``` ## benchmark_serving We also benchmarked the throughput in a serving environment. 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 Get vllm source code: ``` git clone git@github.com:vllm-project/vllm.git ``` Run the following under `vllm` root folder: ### 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 pytorch/Phi-4-mini-instruct-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-float8dq --num-prompts 1 ``` # 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.