Qwen2.5-72B-quantized.w8a16

Model Overview

• Model Architecture: Qwen2
  • Input: Text
  • Output: Text
• Model Optimizations:
  • Weight quantization: INT8
• Intended Use Cases: Similarly to Qwen2.5-72B, this is a base language model.
• Out-of-scope: Use in any manner that violates applicable laws or regulations (including trade compliance laws).
• Release Date: 10/09/2024
• Version: 1.0
• Model Developers: Neural Magic

Quantized version of Qwen2.5-72B. It achieves an OpenLLMv1 score of 78.9, compared to 78.9 for Qwen2.5-72B.

Model Optimizations

This model was obtained by quantizing the weights of Qwen2.5-72B 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.

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/Qwen2.5-72B-quantized.w8a16"
number_gpus = 2
max_model_len = 8192

sampling_params = SamplingParams(temperature=0.7, top_p=0.8, max_tokens=256)

tokenizer = AutoTokenizer.from_pretrained(model_id)

prompt = "Give me a short introduction to large language model."

llm = LLM(model=model_id, tensor_parallel_size=number_gpus, max_model_len=max_model_len)

outputs = llm.generate(prompt, sampling_params)

generated_text = outputs[0].outputs[0].text
print(generated_text)

vLLM aslo supports OpenAI-compatible serving. See the documentation for more details.

Evaluation

The model was evaluated on the OpenLLMv1 benchmark, composed of MMLU, ARC-Challenge, GSM-8K, Hellaswag, Winogrande and TruthfulQA. Evaluation was conducted using lm-evaluation-harness and the vLLM engine.

Accuracy

Category	Benchmark	Qwen2.5-72B	Qwen2.5-72B-quantized.w8a16 (this model)	Recovery
OpenLLM v1
	MMLU (5-shot)	86.05	85.98	99.9%
	ARC Challenge (25-shot)	68.26	68.17	99.9%
	GSM-8k (5-shot, strict-match)	88.63	89.16	100.6%
	Hellaswag (10-shot)	87.53	87.49	100.0%
	Winogrande (5-shot)	82.56	82.56	100.0%
	TruthfulQA (0-shot, mc2)	60.44	60.30	99.8%
	Average	78.91	78.94	100.0%

Reproduction

The results were obtained using the following command:

lm_eval \
  --model vllm \
  --model_args pretrained="neuralmagic/Qwen2.5-72B-quantized.w8a16",dtype=auto,max_model_len=4096,add_bos_token=True,tensor_parallel_size=2 \
  --tasks openllm \
  --batch_size auto