---
tags:
- fp8
- vllm
---
See original model card for information about how it was made. This is to enable fast inference use with Hopper level hardware in FP8. I quantized it to FP8 using neuralmagic code below on 4x L40s.
https://huggingface.co/alpindale/magnum-72b-v1
# Magnum-72b-v1-FP8
## Model Overview
* Model Architecture:
Based on and identical to the Qwen2-72B-Instruct architecture
* Model Optimizations:
Weights and activations quantized to FP8
* Release Date:
June 25, 2024
Magnum-72B-v1 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 better performance recovery.
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 = "alpindale/magnum-72b-v1"
quantized_model_dir = "Magnum-72B-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)
```