File size: 4,355 Bytes
b847d03 581c229 b847d03 581c229 f722512 b847d03 f722512 b847d03 f722512 b847d03 f722512 b847d03 f722512 b847d03 f722512 b847d03 f722512 b847d03 ca403ec b847d03 f722512 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 |
---
tags:
- fp8
- vllm
---
# Phi-3-mini-128k-instruct-FP8
## Model Overview
* <h3 style="display: inline;">Model Architecture:</h3> Based on and identical to the Phi-3-mini-128k-instruct architecture
* <h3 style="display: inline;">Model Optimizations:</h3> Weights and activations quantized to FP8
* <h3 style="display: inline;">Release Date:</h3> June 29, 2024
* <h3 style="display: inline;">Model Developers:</h3> Neural Magic
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.
Calibrated with 512 UltraChat samples to achieve 100% performance recovery on the Open LLM Benchmark evaluations.
Reduces space on disk by ~50%.
Part of the [FP8 LLMs for vLLM collection](https://huggingface.co/collections/neuralmagic/fp8-llms-for-vllm-666742ed2b78b7ac8df13127).
## Usage and Creation
Produced using [AutoFP8 with calibration samples from ultrachat](https://github.com/neuralmagic/AutoFP8/blob/147fa4d9e1a90ef8a93f96fc7d9c33056ddc017a/example_dataset.py).
```python
from datasets import load_dataset
from transformers import AutoTokenizer
from auto_fp8 import AutoFP8ForCausalLM, BaseQuantizeConfig
pretrained_model_dir = "microsoft/Phi-3-mini-128k-instruct"
quantized_model_dir = "Phi-3-mini-128k-instruct-FP8"
tokenizer = AutoTokenizer.from_pretrained(pretrained_model_dir, use_fast=True, model_max_length=4096)
tokenizer.pad_token = tokenizer.eos_token
ds = load_dataset("mgoin/ultrachat_2k", split="train_sft").select(range(512))
examples = [tokenizer.apply_chat_template(batch["messages"], tokenize=False) for batch in ds]
examples = tokenizer(examples, padding=True, truncation=True, return_tensors="pt").to("cuda")
quantize_config = BaseQuantizeConfig(quant_method="fp8", activation_scheme="static")
model = AutoFP8ForCausalLM.from_pretrained(
pretrained_model_dir, quantize_config=quantize_config
)
model.quantize(examples)
model.save_quantized(quantized_model_dir)
```
Evaluated through a modified version of vLLM with the following script:
```
#!/bin/bash
# Example usage:
# 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"
export MODEL_DIR=${1}
export MODEL_ARGS=${2}
declare -A tasks_fewshot=(
["arc_challenge"]=25
["winogrande"]=5
["truthfulqa_mc2"]=0
["hellaswag"]=10
["mmlu"]=5
["gsm8k"]=5
)
declare -A batch_sizes=(
["arc_challenge"]="auto"
["winogrande"]="auto"
["truthfulqa_mc2"]="auto"
["hellaswag"]="auto"
["mmlu"]=1
["gsm8k"]="auto"
)
for TASK in "${!tasks_fewshot[@]}"; do
NUM_FEWSHOT=${tasks_fewshot[$TASK]}
BATCH_SIZE=${batch_sizes[$TASK]}
lm_eval --model vllm \
--model_args pretrained=$MODEL_DIR,$MODEL_ARGS \
--tasks ${TASK} \
--num_fewshot ${NUM_FEWSHOT} \
--write_out \
--show_config \
--device cuda \
--batch_size ${BATCH_SIZE} \
--output_path="results/${TASK}"
done
```
## Evaluation
Evaluated on the Open LLM Leaderboard evaluations through vLLM.
### Open LLM Leaderboard evaluation scores
| | Phi-3-mini-128k-instruct-FP8 | neuralmagic/Phi-3-mini-128k-instruct-FP8<br>(this model) |
| :------------------: | :----------------------: | :------------------------------------------------: |
| arc-c<br>25-shot | 63.65 | 64.24 |
| hellaswag<br>10-shot | 79.76 | 79.79 |
| mmlu<br>5-shot | 68.10 | 67.93 |
| truthfulqa<br>0-shot | 53.97 | 53.50 |
| winogrande<br>5-shot | 73.72 | 74.11 |
| gsm8k<br>5-shot | 75.59 | 74.37 |
| **Average<br>Accuracy** | **69.13** | **68.99** |
| **Recovery** | **100%** | **99.80%** | |