nm-testing/Llama-2-70b-chat-hf-FP8

Created using AutoFP8:

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|