language:
- en
- de
- fr
- it
- pt
- hi
- es
- th
pipeline_tag: text-generation
license: llama3.1
Meta-Llama-3.1-8B-Instruct-quantized.w8a16
Model Overview
- Model Architecture: Meta-Llama-3
- Input: Text
- Output: Text
- Model Optimizations:
- Weight quantization: INT8
- Intended Use Cases: Intended for commercial and research use multiple languages. Similarly to Meta-Llama-3.1-8B-Instruct, this models is intended for assistant-like chat.
- Out-of-scope: Use in any manner that violates applicable laws or regulations (including trade compliance laws).
- Release Date: 7/23/2024
- Version: 1.0
- License(s): Llama3.1
- Model Developers: Neural Magic
Quantized version of Meta-Llama-3.1-8B-Instruct. It achieves scores within 1% of the scores of the unquantized model for MMLU, ARC-Challenge, GSM-8k, Hellaswag, Winogrande and TruthfulQA.
Model Optimizations
This model was obtained by quantizing the weights of Meta-Llama-3.1-8B-Instruct to INT8 data type. 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 of the linear operators within transformers blocks are quantized. Symmetric per-channel quantization is applied, in which a linear scaling per output dimension maps the INT8 and floating point representations of the quantized weights. The GPTQ algorithm is applied for quantization, as implemented in the llm-compressor library. GPTQ used a 1% damping factor and 256 sequences of 8,192 random tokens.
Deployment
This model can be deployed efficiently using the vLLM backend, as shown in the example below.
from vllm import LLM, SamplingParams
from transformers import AutoTokenizer
model_id = "neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a16"
number_gpus = 1
max_model_len = 8192
sampling_params = SamplingParams(temperature=0.6, top_p=0.9, max_tokens=256)
tokenizer = AutoTokenizer.from_pretrained(model_id)
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
prompts = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
llm = LLM(model=model_id, tensor_parallel_size=number_gpus, max_model_len=max_model_len)
outputs = llm.generate(prompts, sampling_params)
generated_text = outputs[0].outputs[0].text
print(generated_text)
vLLM aslo supports OpenAI-compatible serving. See the documentation for more details.
Creation
This model was created by using the llm-compressor library as presented in the code snipet below.
from transformers import AutoTokenizer
from datasets import Dataset
from llmcompressor.transformers import SparseAutoModelForCausalLM, oneshot
from llmcompressor.modifiers.quantization import GPTQModifier
import random
model_id = "meta-llama/Meta-Llama-3.1-8B-Instruct"
num_samples = 256
max_seq_len = 8192
tokenizer = AutoTokenizer.from_pretrained(model_id)
max_token_id = len(tokenizer.get_vocab()) - 1
input_ids = [[random.randint(0, max_token_id) for _ in range(max_seq_len)] for _ in range(num_samples)]
attention_mask = num_samples * [max_seq_len * [1]]
ds = Dataset.from_dict({"input_ids": input_ids, "attention_mask": attention_mask})
recipe = GPTQModifier(
targets="Linear",
scheme="W8A16",
ignore=["lm_head"],
dampening_frac=0.01,
)
model = SparseAutoModelForCausalLM.from_pretrained(
model_id,
device_map="auto",
trust_remote_code=True,
)
oneshot(
model=model,
dataset=ds,
recipe=recipe,
max_seq_length=max_seq_len,
num_calibration_samples=num_samples,
)
model.save_pretrained("Meta-Llama-3.1-8B-Instruct-quantized.w8a16")
Evaluation
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 and GSM-8K that match the prompting style of Meta-Llama-3.1-Instruct-evals.
Accuracy
| Benchmark | Meta-Llama-3.1-8B-Instruct | Meta-Llama-3.1-8B-Instruct-quantized.w8a16 (this model) | Recovery |
| MMLU (5-shot) | 67.94 | 68.09 | 100.2% |
| ARC Challenge (0-shot) | 83.19 | 82.68 | 99.4% |
| GSM-8K (CoT, 8-shot, strict-match) | 82.79 | 82.64 | 99.8% |
| Hellaswag (10-shot) | 80.01 | 80.21 | 100.3% |
| Winogrande (5-shot) | 77.90 | 77.27 | 99.2% |
| TruthfulQA (0-shot) | 54.04 | 54.15 | 100.2% |
| Average | 74.31 | 74.17 | 99.8% |
Reproduction
The results were obtained using the following commands:
MMLU
lm_eval \
--model vllm \
--model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a16",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \
--tasks mmlu \
--num_fewshot 5 \
--batch_size auto
ARC-Challenge
lm_eval \
--model vllm \
--model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a16",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \
--tasks arc_challenge_llama_3.1_instruct \
--apply_chat_template \
--num_fewshot 0 \
--batch_size auto
GSM-8K
lm_eval \
--model vllm \
--model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a16",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \
--tasks gsm8k_cot_llama_3.1_instruct \
--apply_chat_template \
--num_fewshot 8 \
--batch_size auto
Hellaswag
lm_eval \
--model vllm \
--model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a16",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \
--tasks hellaswag \
--num_fewshot 10 \
--batch_size auto
Winogrande
lm_eval \
--model vllm \
--model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a16",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \
--tasks winogrande \
--num_fewshot 5 \
--batch_size auto
Hellaswag
lm_eval \
--model vllm \
--model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a16",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \
--tasks truthfulqa_mc \
--num_fewshot 0 \
--batch_size auto