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--- |
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language: |
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- en |
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base_model: |
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- deepseek-ai/DeepSeek-R1-0528 |
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pipeline_tag: text-generation |
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tags: |
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- deepseek_v3 |
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- deepseek |
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- neuralmagic |
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- redhat |
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- llmcompressor |
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- quantized |
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- INT4 |
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- GPTQ |
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- conversational |
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- compressed-tensors |
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license: mit |
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license_name: mit |
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name: RedHatAI/DeepSeek-R1-0528-quantized.w4a16 |
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description: This model was obtained by quantizing weights of DeepSeek-R1-0528 to INT4 data type. |
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readme: https://huggingface.co/RedHatAI/DeepSeek-R1-0528-quantized.w4a16/main/README.md |
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tasks: |
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- text-to-text |
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provider: DeepSeek |
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license_link: https://choosealicense.com/licenses/mit/ |
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validated_on: |
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- RHOAI 2.24 |
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- RHAIIS 3.2.1 |
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--- |
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<h1 style="display: flex; align-items: center; gap: 10px; margin: 0;"> |
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DeepSeek-R1-0528-quantized.w4a16 |
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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:** DeepseekV3ForCausalLM |
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- **Input:** Text |
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- **Output:** Text |
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- **Model Optimizations:** |
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- **Activation quantization:** None |
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- **Weight quantization:** INT4 |
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- **Release Date:** 05/30/2025 |
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- **Version:** 1.0 |
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- **Validated on:** RHOAI 2.24, RHAIIS 3.2.1 |
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- **Model Developers:** Red Hat (Neural Magic) |
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### Model Optimizations |
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This model was obtained by quantizing weights of [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) to INT4 data type. |
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This optimization reduces the number of bits used to represent weights from 8 to 4, reducing GPU memory requirements (by approximately 50%). |
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Weight quantization also reduces disk size requirements by approximately 50%. |
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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/DeepSeek-R1-0528-quantized.w4a16" |
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number_gpus = 8 |
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sampling_params = SamplingParams(temperature=0.6, top_p=0.95, max_tokens=256) |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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prompt = "Give me a short introduction to large language model." |
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llm = LLM(model=model_id, tensor_parallel_size=number_gpus) |
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outputs = llm.generate(prompt, 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 also 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/DeepSeek-R1-0528-quantized.w4a16 |
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``` |
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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-2.24-cuda # CHANGE if needed. If AMD: quay.io/modh/vllm:rhoai-2.24-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: DeepSeek-R1-0528-quantized.w4a16 # OPTIONAL CHANGE |
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serving.kserve.io/deploymentMode: RawDeployment |
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name: DeepSeek-R1-0528-quantized.w4a16 # 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-deepseek-r1-0528-quantized-w4a16:1.5 |
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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": "DeepSeek-R1-0528-quantized.w4a16", |
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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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We created this model using **MoE-Quant**, a library developed jointly with **ISTA** and tailored for the quantization of very large Mixture-of-Experts (MoE) models. |
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For more details, please refer to the [MoE-Quant repository](https://github.com/IST-DASLab/MoE-Quant). |
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## Evaluation |
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The model was evaluated on popular reasoning tasks (AIME 2024, MATH-500, GPQA-Diamond) via [LightEval](https://github.com/huggingface/open-r1). |
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For reasoning evaluations, we estimate pass@1 based on 10 runs with different seeds, `temperature=0.6`, `top_p=0.95` and `max_new_tokens=65536`. |
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### Accuracy |
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| | Recovery (%) | deepseek/DeepSeek-R1-0528 | RedHatAI/DeepSeek-R1-0528-quantized.w4a16<br>(this model) | |
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| --------------------------- | :----------: | :------------------: | :--------------------------------------------------: | |
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| AIME 2024<br>pass@1 | 98.50 | 88.66 | 87.33 | |
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| MATH-500<br>pass@1 | 99.88 | 97.52 | 97.40 | |
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| GPQA Diamond<br>pass@1 | 101.21 | 79.65 | 80.61 | |
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| **Reasoning<br>Average Score** | **99.82** | **88.61** | **88.45** | |
