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--- |
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tags: |
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- fp8 |
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- vllm |
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--- |
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# Phi-3-mini-128k-instruct-FP8 |
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## Model Overview |
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* <h3 style="display: inline;">Model Architecture:</h3> Based on and identical to the Phi-3-mini-128k-instruct architecture |
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* <h3 style="display: inline;">Model Optimizations:</h3> Weights and activations quantized to FP8 |
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* <h3 style="display: inline;">Release Date:</h3> June 29, 2024 |
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* <h3 style="display: inline;">Model Developers:</h3> Neural Magic |
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Phi-3-mini-128k-instruct quantized to FP8 weights and activations using per-tensor quantization through the [AutoFP8 repository](https://github.com/neuralmagic/AutoFP8), ready for inference with vLLM >= 0.5.0. |
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Calibrated with 512 UltraChat samples to achieve 100% performance recovery on the Open LLM Benchmark evaluations. |
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Reduces space on disk by ~50%. |
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Part of the [FP8 LLMs for vLLM collection](https://huggingface.co/collections/neuralmagic/fp8-llms-for-vllm-666742ed2b78b7ac8df13127). |
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## Usage and Creation |
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Produced using [AutoFP8 with calibration samples from ultrachat](https://github.com/neuralmagic/AutoFP8/blob/147fa4d9e1a90ef8a93f96fc7d9c33056ddc017a/example_dataset.py). |
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```python |
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from datasets import load_dataset |
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from transformers import AutoTokenizer |
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from auto_fp8 import AutoFP8ForCausalLM, BaseQuantizeConfig |
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pretrained_model_dir = "microsoft/Phi-3-mini-128k-instruct" |
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quantized_model_dir = "Phi-3-mini-128k-instruct-FP8" |
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tokenizer = AutoTokenizer.from_pretrained(pretrained_model_dir, use_fast=True, model_max_length=4096) |
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tokenizer.pad_token = tokenizer.eos_token |
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ds = load_dataset("mgoin/ultrachat_2k", split="train_sft").select(range(512)) |
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examples = [tokenizer.apply_chat_template(batch["messages"], tokenize=False) for batch in ds] |
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examples = tokenizer(examples, padding=True, truncation=True, return_tensors="pt").to("cuda") |
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quantize_config = BaseQuantizeConfig(quant_method="fp8", activation_scheme="static") |
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model = AutoFP8ForCausalLM.from_pretrained( |
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pretrained_model_dir, quantize_config=quantize_config |
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) |
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model.quantize(examples) |
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model.save_quantized(quantized_model_dir) |
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``` |
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Evaluated through vLLM>=0.5.1 with the following script: |
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```bash |
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#!/bin/bash |
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# Example usage: |
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# CUDA_VISIBLE_DEVICES=0 ./eval_openllm.sh "neuralmagic/Phi-3-mini-128k-instruct-FP8" "tensor_parallel_size=1,max_model_len=4096,add_bos_token=True,gpu_memory_utilization=0.7" |
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export MODEL_DIR=${1} |
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export MODEL_ARGS=${2} |
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declare -A tasks_fewshot=( |
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["arc_challenge"]=25 |
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["winogrande"]=5 |
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["truthfulqa_mc2"]=0 |
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["hellaswag"]=10 |
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["mmlu"]=5 |
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["gsm8k"]=5 |
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) |
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declare -A batch_sizes=( |
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["arc_challenge"]="auto" |
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["winogrande"]="auto" |
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["truthfulqa_mc2"]="auto" |
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["hellaswag"]="auto" |
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["mmlu"]=1 |
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["gsm8k"]="auto" |
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) |
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for TASK in "${!tasks_fewshot[@]}"; do |
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NUM_FEWSHOT=${tasks_fewshot[$TASK]} |
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BATCH_SIZE=${batch_sizes[$TASK]} |
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lm_eval --model vllm \ |
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--model_args pretrained=$MODEL_DIR,$MODEL_ARGS \ |
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--tasks ${TASK} \ |
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--num_fewshot ${NUM_FEWSHOT} \ |
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--write_out \ |
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--show_config \ |
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--device cuda \ |
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--batch_size ${BATCH_SIZE} \ |
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--output_path="results/${TASK}" |
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done |
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``` |
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## Evaluation |
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Evaluated on the Open LLM Leaderboard evaluations through vLLM. |
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### Open LLM Leaderboard evaluation scores |
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| | Phi-3-mini-128k-instruct-FP8 | neuralmagic/Phi-3-mini-128k-instruct-FP8<br>(this model) | |
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| :------------------: | :----------------------: | :------------------------------------------------: | |
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| arc-c<br>25-shot | 63.65 | 64.24 | |
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| hellaswag<br>10-shot | 79.76 | 79.79 | |
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| mmlu<br>5-shot | 68.10 | 67.93 | |
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| truthfulqa<br>0-shot | 53.97 | 53.50 | |
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| winogrande<br>5-shot | 73.72 | 74.11 | |
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| gsm8k<br>5-shot | 75.59 | 74.37 | |
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| **Average<br>Accuracy** | **69.13** | **68.99** | |
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| **Recovery** | **100%** | **99.80%** | |
