README.md

metadata

language:
  - en
base_model:
  - deepseek-ai/DeepSeek-R1-0528
pipeline_tag: text-generation
tags:
  - deepseek_v3
  - deepseek
  - neuralmagic
  - redhat
  - llmcompressor
  - quantized
  - INT4
  - GPTQ
  - conversational
  - compressed-tensors
license: mit
license_name: mit
name: RedHatAI/DeepSeek-R1-0528-quantized.w4a16
description: >-
  This model was obtained by quantizing weights of DeepSeek-R1-0528 to INT4 data
  type.
readme: >-
  https://huggingface.co/RedHatAI/DeepSeek-R1-0528-quantized.w4a16/main/README.md
tasks:
  - text-to-text
provider: DeepSeek
license_link: https://choosealicense.com/licenses/mit/
validated_on:
  - RHOAI 2.24
  - RHAIIS 3.2.1

DeepSeek-R1-0528-quantized.w4a16

Model Overview

• Model Architecture: DeepseekV3ForCausalLM
  • Input: Text
  • Output: Text
• Model Optimizations:
  • Activation quantization: None
  • Weight quantization: INT4
• Release Date: 05/30/2025
• Version: 1.0
• Validated on: RHOAI 2.24, RHAIIS 3.2.1
• Model Developers: Red Hat (Neural Magic)

Model Optimizations

This model was obtained by quantizing weights of DeepSeek-R1-0528 to INT4 data type. This optimization reduces the number of bits used to represent weights from 8 to 4, reducing GPU memory requirements (by approximately 50%). Weight quantization also reduces disk size requirements by approximately 50%.

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 = "RedHatAI/DeepSeek-R1-0528-quantized.w4a16"
number_gpus = 8
sampling_params = SamplingParams(temperature=0.6, top_p=0.95, 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)
outputs = llm.generate(prompt, sampling_params)
generated_text = outputs[0].outputs[0].text
print(generated_text)

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

Deploy on Red Hat AI Inference Server

podman run --rm -it --device nvidia.com/gpu=all -p 8000:8000 \
 --ipc=host \
--env "HUGGING_FACE_HUB_TOKEN=$HF_TOKEN" \
--env "HF_HUB_OFFLINE=0" -v ~/.cache/vllm:/home/vllm/.cache \
--name=vllm \
registry.access.redhat.com/rhaiis/rh-vllm-cuda \
vllm serve \
--tensor-parallel-size 8 \
--max-model-len 32768  \
--enforce-eager --model RedHatAI/DeepSeek-R1-0528-quantized.w4a16

Deploy on Red Hat Openshift AI

# Setting up vllm server with ServingRuntime
# Save as: vllm-servingruntime.yaml
apiVersion: serving.kserve.io/v1alpha1
kind: ServingRuntime
metadata:
 name: vllm-cuda-runtime # OPTIONAL CHANGE: set a unique name
 annotations:
   openshift.io/display-name: vLLM NVIDIA GPU ServingRuntime for KServe
   opendatahub.io/recommended-accelerators: '["nvidia.com/gpu"]'
 labels:
   opendatahub.io/dashboard: 'true'
spec:
 annotations:
   prometheus.io/port: '8080'
   prometheus.io/path: '/metrics'
 multiModel: false
 supportedModelFormats:
   - autoSelect: true
     name: vLLM
 containers:
   - name: kserve-container
     image: quay.io/modh/vllm:rhoai-2.24-cuda # CHANGE if needed. If AMD: quay.io/modh/vllm:rhoai-2.24-rocm
     command:
       - python
       - -m
       - vllm.entrypoints.openai.api_server
     args:
       - "--port=8080"
       - "--model=/mnt/models"
       - "--served-model-name={{.Name}}"
     env:
       - name: HF_HOME
         value: /tmp/hf_home
     ports:
       - containerPort: 8080
         protocol: TCP

# Attach model to vllm server. This is an NVIDIA template
# Save as: inferenceservice.yaml
apiVersion: serving.kserve.io/v1beta1
kind: InferenceService
metadata:
  annotations:
    openshift.io/display-name: DeepSeek-R1-0528-quantized.w4a16 # OPTIONAL CHANGE
    serving.kserve.io/deploymentMode: RawDeployment
  name: DeepSeek-R1-0528-quantized.w4a16          # specify model name. This value will be used to invoke the model in the payload
  labels:
    opendatahub.io/dashboard: 'true'
spec:
  predictor:
    maxReplicas: 1
    minReplicas: 1
    model:
      modelFormat:
        name: vLLM
      name: ''
      resources:
        limits:
          cpu: '2'			# this is model specific
          memory: 8Gi		# this is model specific
          nvidia.com/gpu: '1'	# this is accelerator specific
        requests:			# same comment for this block
          cpu: '1'
          memory: 4Gi
          nvidia.com/gpu: '1'
      runtime: vllm-cuda-runtime	# must match the ServingRuntime name above
      storageUri: oci://registry.redhat.io/rhelai1/modelcar-deepseek-r1-0528-quantized-w4a16:1.5
    tolerations:
    - effect: NoSchedule
      key: nvidia.com/gpu
      operator: Exists

# make sure first to be in the project where you want to deploy the model
# oc project <project-name>

# apply both resources to run model

# Apply the ServingRuntime
oc apply -f vllm-servingruntime.yaml

# Apply the InferenceService
oc apply -f qwen-inferenceservice.yaml

# Replace <inference-service-name> and <cluster-ingress-domain> below:
# - Run `oc get inferenceservice` to find your URL if unsure.

# Call the server using curl:
curl https://<inference-service-name>-predictor-default.<domain>/v1/chat/completions
        -H "Content-Type: application/json" \
        -d '{
    "model": "DeepSeek-R1-0528-quantized.w4a16",
    "stream": true,
    "stream_options": {
        "include_usage": true
    },
    "max_tokens": 1,
    "messages": [
        {
            "role": "user",
            "content": "How can a bee fly when its wings are so small?"
        }
    ]
}'

See Red Hat Openshift AI documentation for more details.

Creation

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.

For more details, please refer to the MoE-Quant repository.

Evaluation

The model was evaluated on popular reasoning tasks (AIME 2024, MATH-500, GPQA-Diamond) via LightEval. 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.

Accuracy

	Recovery (%)	deepseek/DeepSeek-R1-0528	RedHatAI/DeepSeek-R1-0528-quantized.w4a16 (this model)
AIME 2024 pass@1	98.50	88.66	87.33
MATH-500 pass@1	99.88	97.52	97.40
GPQA Diamond pass@1	101.21	79.65	80.61
Reasoning Average Score	99.82	88.61	88.45