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---
tags:
- fp8
- vllm
---
Created using AutoFP8:
```python
from datasets import load_dataset
from transformers import AutoTokenizer
from auto_fp8 import AutoFP8ForCausalLM, BaseQuantizeConfig
pretrained_model_dir = "meta-llama/Llama-2-70b-chat-hf"
quantized_model_dir = "Llama-2-70b-chat-hf-FP8"
tokenizer = AutoTokenizer.from_pretrained(pretrained_model_dir, use_fast=False)
tokenizer.pad_token = tokenizer.eos_token
# Load and tokenize all dataset samples for calibration of activation scales
ds = load_dataset("mgoin/ultrachat_2k", split="train_sft")
examples = [tokenizer.apply_chat_template(batch["messages"], tokenize=False) for batch in ds]
examples = tokenizer(examples, padding=True, truncation=True, return_tensors="pt", max_length=4096).to("cuda")
print(examples)
# Define quantization config with static activation scales
quantize_config = BaseQuantizeConfig(
quant_method="fp8",
activation_scheme="static",
ignore_patterns=["re:.*lm_head"],
)
# Load the model, quantize, and save checkpoint
model = AutoFP8ForCausalLM.from_pretrained(pretrained_model_dir, quantize_config)
model.quantize(examples)
model.save_quantized(quantized_model_dir)
```
Evaluation:
```
vllm (pretrained=meta-llama/Llama-2-70b-chat-hf,tensor_parallel_size=2,distributed_executor_backend=ray), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: auto
|Tasks|Version| Filter |n-shot| Metric | |Value | |Stderr|
|-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|
|gsm8k| 3|flexible-extract| 5|exact_match|↑ |0.5307|± |0.0137|
| | |strict-match | 5|exact_match|↑ |0.5064|± |0.0138|
vllm (pretrained=nm-testing/Llama-2-70b-chat-hf-FP8,tensor_parallel_size=2,distributed_executor_backend=ray), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: auto
|Tasks|Version| Filter |n-shot| Metric | |Value | |Stderr|
|-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|
|gsm8k| 3|flexible-extract| 5|exact_match|↑ |0.5625|± |0.0137|
| | |strict-match | 5|exact_match|↑ |0.5428|± |0.0137|
``` |