--- 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. # Quantization Recipe First need to install the required packages: ``` pip install git+https://github.com/huggingface/transformers pip install --pre torchao --index-url https://download.pytorch.org/whl/nightly/cu126 ``` 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", 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):]) ``` # Serving with vllm We can use the same command we used in serving benchmarks to serve the model with vllm ``` vllm serve pytorch/Phi-4-mini-instruct-float8dq --tokenizer microsoft/Phi-4-mini-instruct -O3 ``` # 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 ``` ## 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 We can use the following code to get a sense of peak memory usage during inference: ## Results | Benchmark | | | |------------------|----------------|--------------------------------| | | Phi-4 mini-Ins | Phi-4-mini-instruct-float8dq | | Peak Memory (GB) | 8.91 | 5.70 (36% reduction) | ## Benchmark Peak Memory ``` 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 Need to install vllm nightly to get some recent changes ``` pip install vllm --pre --extra-index-url https://wheels.vllm.ai/nightly ``` ## 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. ## 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 Run the following under `vllm` source code 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. Run the following under `vllm` source code 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.