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---
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](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 = "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<br>(this model) |
| :------------------: | :----------------------: | :------------------------------------------------: |
| arc-c<br>25-shot | 71.58 | 72.09 |
| hellaswag<br>10-shot | 86.94 | 86.83 |
| mmlu<br>5-shot | xx.xx | 84.06 |
| truthfulqa<br>0-shot | 66.94 | 66.95 |
| winogrande<br>5-shot | 82.79 | 83.18 |
| gsm8k<br>5-shot | xx.xx | 88.93 |
| **Average<br>Accuracy** | **xx.xx** | **80.34** |
| **Recovery** | **100%** | **xx.xx%** |
|