metadata
tags:
- fp8
- vllm
Meta-Llama-3-70B-Instruct-FP8-KV
Model Overview
Meta-Llama-3-70B-Instruct quantized to FP8 weights and activations using per-tensor quantization, ready for inference with vLLM >= 0.5.0.
This model checkpoint also includes per-tensor scales for FP8 quantized KV Cache, accessed through the --kv-cache-dtype fp8 argument in vLLM.
from vllm import LLM
model = LLM(model="neuralmagic/Meta-Llama-3-70B-Instruct-FP8-KV", kv_cache_dtype="fp8")
result = model.generate("Hello, my name is")
Usage and Creation
Produced using AutoFP8 with calibration samples from ultrachat.
from datasets import load_dataset
from transformers import AutoTokenizer
from auto_fp8 import AutoFP8ForCausalLM, BaseQuantizeConfig
pretrained_model_dir = "meta-llama/Meta-Llama-3-70B-Instruct"
quantized_model_dir = "Meta-Llama-3-70B-Instruct-FP8-KV"
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")
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",
ignore_patterns=["re:.*lm_head"],
kv_cache_quant_targets=("k_proj", "v_proj"),
)
model = AutoFP8ForCausalLM.from_pretrained(pretrained_model_dir, quantize_config)
model.quantize(examples)
model.save_quantized(quantized_model_dir)
Evaluation
Open LLM Leaderboard evaluation scores
Model evaluation results obtained via lm-evaluation-harness.
| Benchmark | Meta-Llama-3-70B-Instruct | Meta-Llama-3-70B-Instruct-FP8 | Meta-Llama-3-70B-Instruct-FP8-KV (this model) |
|---|---|---|---|
| ARC-c 25-shot |
72.69 | 72.61 | 72.57 |
| HellaSwag 10-shot |
85.50 | 85.41 | 85.32 |
| MMLU 5-shot |
80.18 | 80.06 | 79.69 |
| TruthfulQA 0-shot |
62.90 | 62.73 | 61.92 |
| WinoGrande 5-shot |
83.34 | 83.03 | 83.66 |
| GSM8K 5-shot |
92.49 | 91.12 | 90.83 |
| Average Accuracy |
79.51 | 79.16 | 79.00 |
| Recovery | 100% | 99.55% | 99.36% |