|
--- |
|
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%** | |
|
|
|
|
