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---
tags:
- int8
- vllm
- llm-compressor
language:
- en
pipeline_tag: text-generation
license: apache-2.0
base_model:
- Qwen/Qwen2.5-3B
---
# Qwen2.5-3B-quantized.w8a16
## Model Overview
- **Model Architecture:** Qwen2
- **Input:** Text
- **Output:** Text
- **Model Optimizations:**
- **Weight quantization:** INT8
- **Intended Use Cases:** Similarly to [Qwen2.5-3B](https://huggingface.co/Qwen/Qwen2.5-3B), 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 [Qwen2.5-3B](https://huggingface.co/Qwen/Qwen2.5-3B).
It achieves an OpenLLMv1 score of 63.8, compared to 63.6 for [Qwen2.5-3B](https://huggingface.co/Qwen/Qwen2.5-3B).
### Model Optimizations
This model was obtained by quantizing the weights of [Qwen2.5-3B](https://huggingface.co/Qwen/Qwen2.5-3B) 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](https://arxiv.org/abs/2210.17323) algorithm is applied for quantization, as implemented in the [llm-compressor](https://github.com/vllm-project/llm-compressor) library.
## Deployment
This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below.
```python
from vllm import LLM, SamplingParams
from transformers import AutoTokenizer
model_id = "neuralmagic/Qwen2.5-3B-quantized.w8a16"
number_gpus = 1
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](https://docs.vllm.ai/en/latest/) 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](https://github.com/EleutherAI/lm-evaluation-harness) and the [vLLM](https://docs.vllm.ai/en/stable/) engine.
### Accuracy
<table>
<tr>
<td><strong>Category</strong>
</td>
<td><strong>Benchmark</strong>
</td>
<td><strong>Qwen2.5-3B</strong>
</td>
<td><strong>Qwen2.5-3B-quantized.w8a16<br>(this model)</strong>
</td>
<td><strong>Recovery</strong>
</td>
</tr>
<tr>
<td rowspan="8" ><strong>OpenLLM v1</strong>
</td>
</tr>
<tr>
<td>MMLU (5-shot)
</td>
<td>65.68
</td>
<td>65.65
</td>
<td>100.0%
</td>
</tr>
<tr>
<td>ARC Challenge (25-shot)
</td>
<td>53.58
</td>
<td>53.07
</td>
<td>99.0%
</td>
</tr>
<tr>
<td>GSM-8k (5-shot, strict-match)
</td>
<td>68.23
</td>
<td>70.05
</td>
<td>102.7%
</td>
</tr>
<tr>
<td>Hellaswag (10-shot)
</td>
<td>51.83
</td>
<td>51.78
</td>
<td>99.9%
</td>
</tr>
<tr>
<td>Winogrande (5-shot)
</td>
<td>70.64
</td>
<td>70.56
</td>
<td>99.9%
</td>
</tr>
<tr>
<td>TruthfulQA (0-shot, mc2)
</td>
<td>49.93
</td>
<td>48.88
</td>
<td>99.9%
</td>
</tr>
<tr>
<td><strong>Average</strong>
</td>
<td><strong>63.59</strong>
</td>
<td><strong>63.78</strong>
</td>
<td><strong>100.3%</strong>
</td>
</tr>
</table>
### Reproduction
The results were obtained using the following command:
```
lm_eval \
--model vllm \
--model_args pretrained="neuralmagic/Qwen2.5-3B-quantized.w8a16",dtype=auto,max_model_len=4096,add_bos_token=True,tensor_parallel_size=1 \
--tasks openllm \
--batch_size auto
```
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