by RedHatAI
Open source · 224k downloads · 10 likes
Meta Llama 3.1 8B FP8 is an optimized version of the Meta Llama 3.1 model, designed for text-to-text generation. By using FP8 quantization for weights and activations, it halves the required disk space and GPU memory while maintaining performance close to the original. The model excels in various tasks such as text comprehension, response generation, and data analysis, and is suitable for both commercial and research use. Its lightweight and efficient design makes it an ideal choice for deployment on limited infrastructure.
Quantized version of Meta-Llama-3.1-8B. It achieves an average score of 65.90 on the OpenLLM benchmark (version 1), whereas the unquantized model achieves 66.47.
This model was obtained by quantizing the weights and activations of Meta-Llama-3.1-8B to FP8 data type, ready for inference with vLLM built from source. This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%.
Only the weights and activations of the linear operators within transformers blocks are quantized. Symmetric per-tensor quantization is applied, in which a single linear scaling maps the FP8 representations of the quantized weights and activations. LLM Compressor is used for quantization with 512 sequences of UltraChat.
This model was created by applying LLM Compressor with calibration samples from UltraChat, as presented in the code snipet below.
import torch
from datasets import load_dataset
from transformers import AutoTokenizer
from llmcompressor.transformers import SparseAutoModelForCausalLM, oneshot
from llmcompressor.transformers.compression.helpers import (
calculate_offload_device_map,
custom_offload_device_map,
)
recipe = """
quant_stage:
quant_modifiers:
QuantizationModifier:
ignore: ["lm_head"]
config_groups:
group_0:
weights:
num_bits: 8
type: float
strategy: tensor
dynamic: false
symmetric: true
input_activations:
num_bits: 8
type: float
strategy: tensor
dynamic: false
symmetric: true
targets: ["Linear"]
"""
model_stub = "meta-llama/Meta-Llama-3.1-8B"
model_name = model_stub.split("/")[-1]
device_map = calculate_offload_device_map(
model_stub, reserve_for_hessians=False, num_gpus=1, torch_dtype=torch.float16
)
model = SparseAutoModelForCausalLM.from_pretrained(
model_stub, torch_dtype=torch.float16, device_map=device_map
)
tokenizer = AutoTokenizer.from_pretrained(model_stub)
output_dir = f"./{model_name}-FP8"
DATASET_ID = "HuggingFaceH4/ultrachat_200k"
DATASET_SPLIT = "train_sft"
NUM_CALIBRATION_SAMPLES = 512
MAX_SEQUENCE_LENGTH = 4096
ds = load_dataset(DATASET_ID, split=DATASET_SPLIT)
ds = ds.shuffle(seed=42).select(range(NUM_CALIBRATION_SAMPLES))
def preprocess(example):
return {
"text": tokenizer.apply_chat_template(
example["messages"],
tokenize=False,
)
}
ds = ds.map(preprocess)
def tokenize(sample):
return tokenizer(
sample["text"],
padding=False,
max_length=MAX_SEQUENCE_LENGTH,
truncation=True,
add_special_tokens=False,
)
ds = ds.map(tokenize, remove_columns=ds.column_names)
oneshot(
model=model,
output_dir=output_dir,
dataset=ds,
recipe=recipe,
max_seq_length=MAX_SEQUENCE_LENGTH,
num_calibration_samples=NUM_CALIBRATION_SAMPLES,
save_compressed=True,
)
The model was evaluated on MMLU, ARC-Challenge, GSM-8K, Hellaswag, Winogrande and TruthfulQA. Evaluation was conducted using the Neural Magic fork of lm-evaluation-harness (branch llama_3.1_instruct) and the vLLM engine. This version of the lm-evaluation-harness includes versions of ARC-Challenge that matches the prompting style of Meta-Llama-3.1-evals.
| Benchmark | Meta-Llama-3.1-8B | Meta-Llama-3.1-8B-FP8(this model) | Recovery |
| MMLU (5-shot) | 65.19 | 65.01 | 99.72% |
| ARC Challenge (25-shot) | 78.84 | 77.73 | 98.59% |
| GSM-8K (5-shot, strict-match) | 50.34 | 48.82 | 96.98% |
| Hellaswag (10-shot) | 82.33 | 81.96 | 99.55% |
| Winogrande (5-shot) | 77.98 | 78.06 | 100.10% |
| TruthfulQA (0-shot, mc2) | 44.14 | 43.83 | 99.30% |
| Average | 66.47 | 65.90 | 99.14% |
The results were obtained using the following commands:
lm_eval \
--model vllm \
--model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-FP8",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \
--tasks mmlu \
--num_fewshot 5 \
--batch_size auto
lm_eval \
--model vllm \
--model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-FP8",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \
--tasks arc_challenge_llama_3.1_instruct \
--num_fewshot 25 \
--batch_size auto
lm_eval \
--model vllm \
--model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-FP8",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \
--tasks gsm8k \
--num_fewshot 5 \
--batch_size auto
lm_eval \
--model vllm \
--model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-FP8",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \
--tasks hellaswag \
--num_fewshot 10 \
--batch_size auto
lm_eval \
--model vllm \
--model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-FP8",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \
--tasks winogrande \
--num_fewshot 5 \
--batch_size auto
lm_eval \
--model vllm \
--model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-FP8",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \
--tasks truthfulqa \
--num_fewshot 0 \
--batch_size auto