metadata
tags:
- fp8
- vllm
Qwen2-72B-Instruct-FP8
Model Overview
Qwen2-72B-Instruct quantized to FP8 weights and activations using per-tensor quantization, ready for inference with vLLM >= 0.5.0.
Usage and Creation
Produced using AutoFP8 with calibration samples from ultrachat.
from datasets import load_dataset
from transformers import AutoTokenizer
from auto_fp8 import AutoFP8ForCausalLM, BaseQuantizeConfig
pretrained_model_dir = "Qwen/Qwen2-72B-Instruct"
quantized_model_dir = "Qwen2-72B-Instruct-FP8"
tokenizer = AutoTokenizer.from_pretrained(pretrained_model_dir, use_fast=True)
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)
Evaluation
Open LLM Leaderboard evaluation scores
| Qwen2-72B-Instruct | Qwen2-72B-Instruct-FP8 (this model) |
|
|---|---|---|
| arc-c 25-shot |
71.58 | 72.09 |
| hellaswag 10-shot |
86.94 | 86.83 |
| mmlu 5-shot |
83.97 | 84.06 |
| truthfulqa 0-shot |
66.98 | 66.95 |
| winogrande 5-shot |
82.79 | 83.18 |
| gsm8k 5-shot |
87.56 | 88.93 |
| Average Accuracy |
79.97 | 80.34 |
| Recovery | 100% | 100.46% |