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Model: RedHatAI/Qwen2.5-7B-Instruct-quantized.w8a8 Source: Original Platform
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README.md
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README.md
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---
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language:
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- zh
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- en
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- fr
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- es
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- pt
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- de
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- it
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- ru
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- ja
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- ko
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- vi
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- th
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- ar
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- id
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- tr
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- fa
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- nl
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- pl
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- cs
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- he
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- sv
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- fi
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- da
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- no
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- el
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- bg
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- uk
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- ur
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- sr
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- ms
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- zsm
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- nld
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base_model:
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- Qwen/Qwen2.5-7B-Instruct
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pipeline_tag: text-generation
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tags:
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- qwen
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- qwen2_5
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- qwen2_5_instruct
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- w8a8
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- int8
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- vllm
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- conversational
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- text-generation-inference
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- compressed-tensors
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license: apache-2.0
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license_name: apache-2.0
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name: RedHatAI/Qwen2.5-7B-Instruct-quantized.w8a8
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description: This model was obtained by quantizing the weights and activations of Qwen2.5-7B-Instruct to INT8 data type.
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readme: https://huggingface.co/RedHatAI/Qwen2.5-7B-Instruct-quantized.w8a8/main/README.md
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tasks:
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- text-to-text
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provider: Alibaba Cloud
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license_link: https://www.apache.org/licenses/LICENSE-2.0
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validated_on:
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- RHOAI 2.20
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- RHAIIS 3.0
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- RHELAI 1.5
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- vLLM 0.8.4
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---
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<h1 style="display: flex; align-items: center; gap: 10px; margin: 0;">
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Qwen2.5-7B-Instruct-quantized.w8a8
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<img src="https://www.redhat.com/rhdc/managed-files/Catalog-Validated_model_0.png" alt="Model Icon" width="40" style="margin: 0; padding: 0;" />
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</h1>
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<a href="https://www.redhat.com/en/products/ai/validated-models" target="_blank" style="margin: 0; padding: 0;">
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<img src="https://www.redhat.com/rhdc/managed-files/Validated_badge-Dark.png" alt="Validated Badge" width="250" style="margin: 0; padding: 0;" />
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</a>
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## Model Overview
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- **Model Architecture:** Qwen2
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- **Input:** Text
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- **Output:** Text
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- **Model Optimizations:**
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- **Activation quantization:** INT8
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- **Weight quantization:** INT8
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- **Intended Use Cases:** Intended for commercial and research use multiple languages. Similarly to [Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B), this models is intended for assistant-like chat.
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- **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws).
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- **Release Date:** 10/09/2024
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- **Version:** 1.0
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- **Validated on:** RHOAI 2.20, RHAIIS 3.0, RHELAI 1.5
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- **License(s):** [apache-2.0](https://huggingface.co/Qwen/Qwen2.5-7B/blob/main/LICENSE)
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- **Model Developers:** Neural Magic
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### Model Optimizations
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This model was obtained by quantizing activations and weights of [Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) to INT8 data type.
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This optimization reduces the number of bits used to represent weights and activations from 16 to 8, reducing GPU memory requirements (by approximately 50%) and increasing matrix-multiply compute throughput (by approximately 2x).
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Weight quantization also reduces disk size requirements by approximately 50%.
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Only weights and activations of the linear operators within transformers blocks are quantized.
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Weights are quantized with a symmetric static per-channel scheme, whereas activations are quantized with a symmetric dynamic per-token scheme.
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A combination of the [SmoothQuant](https://arxiv.org/abs/2211.10438) and [GPTQ](https://arxiv.org/abs/2210.17323) algorithms is applied for quantization, as implemented in the [llm-compressor](https://github.com/vllm-project/llm-compressor) library.
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## Deployment
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This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below.
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```python
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from vllm import LLM, SamplingParams
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from transformers import AutoTokenizer
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model_id = "RedHatAI/Qwen2.5-7B-Instruct-quantized.w8a8"
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number_gpus = 1
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max_model_len = 8192
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sampling_params = SamplingParams(temperature=0.7, top_p=0.8, max_tokens=256)
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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messages = [
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{"role": "user", "content": "Give me a short introduction to large language model."},
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]
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prompts = tokenizer.apply_chat_template(messages, tokenize=False)
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llm = LLM(model=model_id, tensor_parallel_size=number_gpus, max_model_len=max_model_len)
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outputs = llm.generate(prompts, sampling_params)
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generated_text = outputs[0].outputs[0].text
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print(generated_text)
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```
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vLLM aslo supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details.
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<details>
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<summary>Deploy on <strong>Red Hat AI Inference Server</strong></summary>
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```bash
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podman run --rm -it --device nvidia.com/gpu=all -p 8000:8000 \
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--ipc=host \
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--env "HUGGING_FACE_HUB_TOKEN=$HF_TOKEN" \
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--env "HF_HUB_OFFLINE=0" -v ~/.cache/vllm:/home/vllm/.cache \
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--name=vllm \
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registry.access.redhat.com/rhaiis/rh-vllm-cuda \
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vllm serve \
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--tensor-parallel-size 8 \
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--max-model-len 32768 \
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--enforce-eager --model RedHatAI/Qwen2.5-7B-Instruct-quantized.w8a8
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```
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See [Red Hat AI Inference Server documentation](https://docs.redhat.com/en/documentation/red_hat_ai_inference_server/) for more details.
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</details>
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<details>
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<summary>Deploy on <strong>Red Hat Enterprise Linux AI</strong></summary>
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```bash
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# Download model from Red Hat Registry via docker
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# Note: This downloads the model to ~/.cache/instructlab/models unless --model-dir is specified.
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ilab model download --repository docker://registry.redhat.io/rhelai1/qwen2-5-7b-instruct-quantized-w8a8:1.5
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```
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```bash
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# Serve model via ilab
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ilab model serve --model-path ~/.cache/instructlab/models/qwen2-5-7b-instruct-quantized-w8a8
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# Chat with model
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ilab model chat --model ~/.cache/instructlab/models/qwen2-5-7b-instruct-quantized-w8a8
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```
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See [Red Hat Enterprise Linux AI documentation](https://docs.redhat.com/en/documentation/red_hat_enterprise_linux_ai/1.4) for more details.
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</details>
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<details>
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<summary>Deploy on <strong>Red Hat Openshift AI</strong></summary>
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```python
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# Setting up vllm server with ServingRuntime
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# Save as: vllm-servingruntime.yaml
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apiVersion: serving.kserve.io/v1alpha1
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kind: ServingRuntime
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metadata:
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name: vllm-cuda-runtime # OPTIONAL CHANGE: set a unique name
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annotations:
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openshift.io/display-name: vLLM NVIDIA GPU ServingRuntime for KServe
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opendatahub.io/recommended-accelerators: '["nvidia.com/gpu"]'
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labels:
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opendatahub.io/dashboard: 'true'
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spec:
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annotations:
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prometheus.io/port: '8080'
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prometheus.io/path: '/metrics'
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multiModel: false
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supportedModelFormats:
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- autoSelect: true
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name: vLLM
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containers:
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- name: kserve-container
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image: quay.io/modh/vllm:rhoai-2.20-cuda # CHANGE if needed. If AMD: quay.io/modh/vllm:rhoai-2.20-rocm
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command:
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- python
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- -m
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- vllm.entrypoints.openai.api_server
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args:
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- "--port=8080"
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- "--model=/mnt/models"
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- "--served-model-name={{.Name}}"
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env:
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- name: HF_HOME
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value: /tmp/hf_home
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ports:
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- containerPort: 8080
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protocol: TCP
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```
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```python
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# Attach model to vllm server. This is an NVIDIA template
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# Save as: inferenceservice.yaml
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apiVersion: serving.kserve.io/v1beta1
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kind: InferenceService
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metadata:
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annotations:
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openshift.io/display-name: Qwen2.5-7B-Instruct-quantized.w8a8 # OPTIONAL CHANGE
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serving.kserve.io/deploymentMode: RawDeployment
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name: Qwen2.5-7B-Instruct-quantized.w8a8 # specify model name. This value will be used to invoke the model in the payload
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labels:
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opendatahub.io/dashboard: 'true'
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spec:
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predictor:
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maxReplicas: 1
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minReplicas: 1
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model:
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modelFormat:
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name: vLLM
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name: ''
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resources:
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limits:
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cpu: '2' # this is model specific
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memory: 8Gi # this is model specific
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nvidia.com/gpu: '1' # this is accelerator specific
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requests: # same comment for this block
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cpu: '1'
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memory: 4Gi
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nvidia.com/gpu: '1'
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runtime: vllm-cuda-runtime # must match the ServingRuntime name above
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storageUri: oci://registry.redhat.io/rhelai1/modelcar-qwen2-5-7b-instruct-quantized-w8a8:1.5
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tolerations:
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- effect: NoSchedule
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key: nvidia.com/gpu
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operator: Exists
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```
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```bash
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# make sure first to be in the project where you want to deploy the model
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# oc project <project-name>
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# apply both resources to run model
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# Apply the ServingRuntime
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oc apply -f vllm-servingruntime.yaml
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# Apply the InferenceService
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oc apply -f qwen-inferenceservice.yaml
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```
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```python
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# Replace <inference-service-name> and <cluster-ingress-domain> below:
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# - Run `oc get inferenceservice` to find your URL if unsure.
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# Call the server using curl:
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curl https://<inference-service-name>-predictor-default.<domain>/v1/chat/completions
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-H "Content-Type: application/json" \
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-d '{
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"model": "Qwen2.5-7B-Instruct-quantized.w8a8",
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"stream": true,
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"stream_options": {
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"include_usage": true
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},
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"max_tokens": 1,
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"messages": [
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{
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"role": "user",
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"content": "How can a bee fly when its wings are so small?"
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}
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]
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}'
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```
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See [Red Hat Openshift AI documentation](https://docs.redhat.com/en/documentation/red_hat_openshift_ai/2025) for more details.
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</details>
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## Creation
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<details>
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<summary>Creation details</summary>
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This model was created with [llm-compressor](https://github.com/vllm-project/llm-compressor) by running the code snippet below.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from llmcompressor.modifiers.quantization import GPTQModifier
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from llmcompressor.modifiers.smoothquant import SmoothQuantModifier
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from llmcompressor.transformers import oneshot
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from datasets import load_dataset
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# Load model
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model_stub = "Qwen/Qwen2.5-7B-Instruct"
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model_name = model_stub.split("/")[-1]
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num_samples = 512
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max_seq_len = 8192
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tokenizer = AutoTokenizer.from_pretrained(model_stub)
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model = AutoModelForCausalLM.from_pretrained(
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model_stub,
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device_map="auto",
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torch_dtype="auto",
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)
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def preprocess_fn(example):
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return {"text": tokenizer.apply_chat_template(example["messages"], add_generation_prompt=False, tokenize=False)}
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ds = load_dataset("neuralmagic/LLM_compression_calibration", split="train")
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ds = ds.map(preprocess_fn)
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# Configure the quantization algorithm and scheme
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recipe = [
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SmoothQuantModifier(
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smoothing_strength=0.8,
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mappings=[
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[["re:.*q_proj", "re:.*k_proj", "re:.*v_proj"], "re:.*input_layernorm"],
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[["re:.*gate_proj", "re:.*up_proj"], "re:.*post_attention_layernorm"],
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[["re:.*down_proj"], "re:.*up_proj"],
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],
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),
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GPTQModifier(
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ignore=["lm_head"],
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sequential_targets=["Qwen2DecoderLayer"],
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dampening_frac=0.01,
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targets="Linear",
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scheme="W8A8",
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),
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]
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# Apply quantization
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oneshot(
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model=model,
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dataset=ds,
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recipe=recipe,
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max_seq_length=max_seq_len,
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num_calibration_samples=num_samples,
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)
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# Save to disk in compressed-tensors format
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save_path = model_name + "-quantized.w8a8"
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model.save_pretrained(save_path)
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tokenizer.save_pretrained(save_path)
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print(f"Model and tokenizer saved to: {save_path}")
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```
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</details>
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## Evaluation
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The model was evaluated on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) leaderboard tasks (version 1) with the [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness/tree/387Bbd54bc621086e05aa1b030d8d4d5635b25e6) (commit 387Bbd54bc621086e05aa1b030d8d4d5635b25e6) and the [vLLM](https://docs.vllm.ai/en/stable/) engine, using the following command:
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```
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lm_eval \
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--model vllm \
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--model_args pretrained="neuralmagic/Qwen2.5-7B-Instruct-quantized.w8a8",dtype=auto,gpu_memory_utilization=0.5,max_model_len=4096,add_bos_token=True,enable_chunk_prefill=True,tensor_parallel_size=1 \
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--tasks openllm \
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--batch_size auto
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```
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### Accuracy
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#### Open LLM Leaderboard evaluation scores
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<table>
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<tr>
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<th>Benchmark
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</th>
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<th>Qwen2.5-7B-Instruct
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</th>
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<th>Qwen2.5-7B-Instruct-quantized.w8a8<br>(this model)
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</th>
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<th>Recovery
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</th>
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</tr>
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<tr>
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<td>MMLU (5-shot)
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</td>
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<td>74.24
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</td>
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<td>73.87
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</td>
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<td>99.5%
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</td>
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</tr>
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<tr>
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<td>ARC Challenge (25-shot)
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</td>
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<td>63.40
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</td>
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<td>63.23
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</td>
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<td>99.7%
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</td>
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</tr>
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<tr>
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<td>GSM-8K (5-shot, strict-match)
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</td>
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<td>80.36
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</td>
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<td>80.74
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</td>
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<td>100.5%
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</td>
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</tr>
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<tr>
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<td>Hellaswag (10-shot)
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</td>
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<td>81.52
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</td>
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<td>81.06
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</td>
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<td>99.4%
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</td>
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</tr>
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<tr>
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<td>Winogrande (5-shot)
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</td>
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<td>74.66
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</td>
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<td>74.82
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</td>
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<td>100.2%
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</td>
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</tr>
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<tr>
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<td>TruthfulQA (0-shot, mc2)
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</td>
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<td>64.76
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</td>
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<td>64.58
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</td>
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<td>99.7%
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</td>
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</tr>
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<tr>
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<td><strong>Average</strong>
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</td>
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<td><strong>73.16</strong>
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</td>
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<td><strong>73.05</strong>
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</td>
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<td><strong>99.4%</strong>
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</td>
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</tr>
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</table>
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Block a user