--- tags: - fp8 - vllm license: mit license_link: https://huggingface.co/microsoft/Phi-3-medium-128k-instruct/resolve/main/LICENSE --- # Phi-3-medium-128k-instruct-FP8 ## Model Overview - **Model Architecture:** Phi-3 - **Input:** Text - **Output:** Text - **Model Optimizations:** - **Weight quantization:** FP8 - **Activation quantization:** FP8 - **Intended Use Cases:** Intended for commercial and research use in English. Similarly to [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct), this models is intended for assistant-like chat. - **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. - **Release Date:** 6/29/2024 - **Version:** 1.0 - **License(s):** [mit](https://huggingface.co/microsoft/Phi-3-medium-128k-instruct/resolve/main/LICENSE) - **Model Developers:** Neural Magic Quantized version of [Phi-3-medium-128k-instruct](https://huggingface.co/microsoft/Phi-3-medium-128k-instruct). It achieves an average score of 73.20 on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) benchmark (version 1), whereas the unquantized model achieves 73.47. ### Model Optimizations This model was obtained by quantizing the weights and activations of [Phi-3-medium-128k-instruct](https://huggingface.co/microsoft/Phi-3-medium-128k-instruct) to FP8 data type, ready for inference with vLLM >= 0.5.1. This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%. Only the weights and activations of the linear operators within transformers blocks are quantized. Symmetric per-tensor quantization is applied, in which a single linear scaling maps the FP8 representations of the quantized weights and activations. [AutoFP8](https://github.com/neuralmagic/AutoFP8) is used for quantization with 10 repeats of every token in random order. ## Deployment ### Use with vLLM This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below. ```python from vllm import LLM, SamplingParams from transformers import AutoTokenizer model_id = "neuralmagic/Phi-3-medium-128k-instruct-FP8" sampling_params = SamplingParams(temperature=0.6, top_p=0.9, max_tokens=256) tokenizer = AutoTokenizer.from_pretrained(model_id) messages = [ {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, {"role": "user", "content": "Who are you? Remember to respond in pirate speak!"}, ] prompts = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) llm = LLM(model=model_id) outputs = llm.generate(prompts, sampling_params) generated_text = outputs[0].outputs[0].text print(generated_text) ``` vLLM also supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details. ## Creation This model was created by applying [AutoFP8 with calibration samples from ultrachat](https://github.com/neuralmagic/AutoFP8/blob/147fa4d9e1a90ef8a93f96fc7d9c33056ddc017a/example_dataset.py), as presented in the code snipet below. Although AutoFP8 was used for this particular model, Neural Magic is transitioning to using [llm-compressor](https://github.com/vllm-project/llm-compressor) which supports several quantization schemes and models not supported by AutoFP8. ```python from datasets import load_dataset from transformers import AutoTokenizer import numpy as np import torch from auto_fp8 import AutoFP8ForCausalLM, BaseQuantizeConfig MODEL_DIR = "microsoft/Phi-3-medium-128k-instruct" final_model_dir = MODEL_DIR.split("/")[-1] CONTEXT_LENGTH = 4096 NUM_SAMPLES = 512 NUM_REPEATS = 10 pretrained_model_dir = MODEL_DIR tokenizer = AutoTokenizer.from_pretrained(pretrained_model_dir, use_fast=True, model_max_length=CONTEXT_LENGTH) tokenizer.pad_token = tokenizer.eos_token tokenizer_num_tokens = len(list(tokenizer.get_vocab().values())) total_token_samples = NUM_REPEATS * tokenizer_num_tokens num_random_samp = -(-total_token_samples // CONTEXT_LENGTH) input_ids = np.tile(np.arange(tokenizer_num_tokens), NUM_REPEATS + 1)[:num_random_samp * CONTEXT_LENGTH] np.random.shuffle(input_ids) input_ids = input_ids.reshape(num_random_samp, CONTEXT_LENGTH) input_ids = torch.tensor(input_ids, dtype=torch.int64).to("cuda") quantize_config = BaseQuantizeConfig( quant_method="fp8", activation_scheme="static", ) examples = input_ids model = AutoFP8ForCausalLM.from_pretrained(pretrained_model_dir, quantize_config=quantize_config) model.quantize(examples) quantized_model_dir = f"{final_model_dir}-FP8" model.save_quantized(quantized_model_dir) ``` ## Evaluation The model was evaluated on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) leaderboard tasks (version 1) with the [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness/tree/383bbd54bc621086e05aa1b030d8d4d5635b25e6) (commit 383bbd54bc621086e05aa1b030d8d4d5635b25e6) and the [vLLM](https://docs.vllm.ai/en/stable/) engine, using the following command: ``` lm_eval \ --model vllm \ --model_args pretrained="neuralmagic/Phi-3-medium-128k-instruct-FP8",dtype=auto,gpu_memory_utilization=0.4,add_bos_token=True,max_model_len=4096 \ --tasks openllm \ --batch_size auto ``` ### Accuracy #### Open LLM Leaderboard evaluation scores
Benchmark Phi-3-medium-128k-instruct Phi-3-medium-128k-instruct-FP8(this model) Recovery
MMLU (5-shot) 75.73 75.58 99.80%
ARC Challenge (25-shot) 67.66 66.89 98.86%
GSM-8K (5-shot, strict-match) 84.00 82.71 98.46%
Hellaswag (10-shot) 84.37 84.14 99.72%
Winogrande (5-shot) 75.53 74.35 98.43%
TruthfulQA (0-shot) 53.53 55.52 103.7%
Average 73.47 73.20 99.63%