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--- |
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library_name: vllm |
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language: |
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- ar |
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- de |
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- en |
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- es |
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- fr |
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- hi |
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- id |
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- it |
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- pt |
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- th |
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- tl |
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- vi |
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base_model: |
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- meta-llama/Llama-4-Scout-17B-16E-Instruct |
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pipeline_tag: image-text-to-text |
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tags: |
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- facebook |
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- meta |
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- pytorch |
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- llama |
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- llama4 |
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- neuralmagic |
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- redhat |
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- llmcompressor |
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- quantized |
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- FP8 |
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license: other |
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license_name: llama4 |
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--- |
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<h1 style="display: flex; align-items: center; gap: 10px; margin: 0;"> |
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Llama-4-Scout-17B-16E-Instruct-FP8-dynamic |
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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:** Llama4ForConditionalGeneration |
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- **Input:** Text / Image |
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- **Output:** Text |
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- **Model Optimizations:** |
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- **Activation quantization:** FP8 |
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- **Weight quantization:** FP8 |
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- **Release Date:** 04/15/2025 |
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- **Version:** 1.0 |
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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 activations and weights of [Llama-4-Scout-17B-16E-Instruct](https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E-Instruct) to FP8 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%. The [llm-compressor](https://github.com/vllm-project/llm-compressor) library is used for quantization. |
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## Deployment |
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This model can be deployed efficiently on vLLM, Red Hat Enterprise Linux AI, and Openshift AI, as shown in the example below. |
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Deploy on <strong>vLLM</strong> |
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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/Llama-4-Scout-17B-16E-Instruct-FP8-dynamic" |
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number_gpus = 4 |
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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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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/Llama-4-Scout-17B-16E-Instruct-FP8-dynamic |
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``` |
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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/llama-4-scout-17b-16e-instruct-fp8-dynamic: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/llama-4-scout-17b-16e-instruct-fp8-dynamic |
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# Chat with model |
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ilab model chat --model ~/.cache/instructlab/models/llama-4-scout-17b-16e-instruct-fp8-dynamic |
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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: Llama-4-Scout-17B-16E-Instruct-FP8-dynamic # OPTIONAL CHANGE |
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serving.kserve.io/deploymentMode: RawDeployment |
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name: Llama-4-Scout-17B-16E-Instruct-FP8-dynamic # 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-llama-4-scout-17b-16e-instruct-fp8-dynamic: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": "Llama-4-Scout-17B-16E-Instruct-FP8-dynamic", |
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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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#!/usr/bin/env python3 |
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""" |
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This script loads an LLM model and applies FP8 quantization to |
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weights and activations. Activations are dynamically quantized, i.e. during |
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actual runtime. |
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""" |
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import argparse |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM, Llama4ForConditionalGeneration |
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from llmcompressor.modifiers.quantization import QuantizationModifier |
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from llmcompressor import oneshot |
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from compressed_tensors.quantization import ( |
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QuantizationScheme, |
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QuantizationArgs, |
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QuantizationType, |
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QuantizationStrategy, |
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) |
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def parse_arguments(): |
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"""Parse command line arguments.""" |
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parser = argparse.ArgumentParser(description="Quantize a causal language model") |
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parser.add_argument( |
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"--model_path", |
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type=str, |
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required=True, |
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help="Path to the pre-trained model", |
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) |
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parser.add_argument( |
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"--quant_path", |
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type=str, |
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required=True, |
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help="Output path for the quantized model", |
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) |
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return parser.parse_args() |
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def main(): |
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"""Main function to load and quantize the model.""" |
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args = parse_arguments() |
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print(f"Loading model from {args.model_path}...") |
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model = Llama4ForConditionalGeneration.from_pretrained( |
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args.model_path, |
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device_map="auto", |
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torch_dtype="auto", |
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trust_remote_code=True, |
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) |
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quant_scheme = QuantizationScheme( |
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targets=["Linear"], |
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weights=QuantizationArgs( |
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num_bits=8, |
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type=QuantizationType.FLOAT, |
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strategy=QuantizationStrategy.CHANNEL, |
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symmetric=True, |
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observer="mse", |
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), |
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input_activations=QuantizationArgs( |
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num_bits=8, |
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type=QuantizationType.FLOAT, |
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strategy=QuantizationStrategy.TOKEN, |
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symmetric=True, |
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dynamic=True, |
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), |
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output_activations=None, |
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) |
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recipe = QuantizationModifier( |
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targets="Linear", |
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config_groups={"group_0": quant_scheme}, |
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ignore=[ |
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're:.*lm_head', |
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're:.*self_attn', |
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're:.*router', |
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're:.*vision_model', |
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're:.*multi_modal_projector', |
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] |
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) |
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print("Applying quantization...") |
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oneshot( |
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model=model, |
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recipe=recipe, |
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trust_remote_code_model=True, |
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) |
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model.save_pretrained(args.quant_path, save_compressed=True, skip_compression_stats=True, disable_sparse_compression=True) |
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print(f"Quantized model saved to {args.quant_path}") |
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if __name__ == "__main__": |
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main() |
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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 leaderboard tasks (v1 and v2), long context RULER, multimodal MMMU, and multimodal ChartQA. |
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All evaluations are obtained through [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness). |
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<details> |
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<summary>Evaluation details</summary> |
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**OpenLLM v1** |
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``` |
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lm_eval \ |
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--model vllm \ |
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--model_args pretrained="RedHatAI/Llama-4-Scout-17B-16E-Instruct-FP8-dynamic",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=8,gpu_memory_utilization=0.7,enable_chunked_prefill=True,trust_remote_code=True \ |
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--tasks openllm \ |
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--batch_size auto |
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``` |
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**OpenLLM v2** |
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``` |
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lm_eval \ |
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--model vllm \ |
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--model_args pretrained="RedHatAI/Llama-4-Scout-17B-16E-Instruct-FP8-dynamic",dtype=auto,add_bos_token=False,max_model_len=16384,tensor_parallel_size=8,gpu_memory_utilization=0.5,enable_chunked_prefill=True,trust_remote_code=True \ |
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--tasks leaderboard \ |
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--apply_chat_template \ |
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--fewshot_as_multiturn \ |
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--batch_size auto |
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``` |
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**Long Context RULER** |
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``` |
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lm_eval \ |
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--model vllm \ |
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--model_args pretrained="RedHatAI/Llama-4-Scout-17B-16E-Instruct-FP8-dynamic",dtype=auto,add_bos_token=False,max_model_len=524288,tensor_parallel_size=8,gpu_memory_utilization=0.9,enable_chunked_prefill=True,trust_remote_code=True \ |
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--tasks ruler \ |
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--metadata='{"max_seq_lengths":[131072]}' \ |
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--batch_size auto |
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``` |
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**Multimodal MMMU** |
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``` |
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lm_eval \ |
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--model vllm-vlm \ |
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--model_args pretrained="RedHatAI/Llama-4-Scout-17B-16E-Instruct-FP8-dynamic",dtype=auto,add_bos_token=False,max_model_len=1000000,tensor_parallel_size=8,gpu_memory_utilization=0.9,enable_chunked_prefill=True,trust_remote_code=True,max_images=10 \ |
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--tasks mmmu_val \ |
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--apply_chat_template \ |
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--batch_size auto |
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``` |
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**Multimodal ChartQA** |
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``` |
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export VLLM_MM_INPUT_CACHE_GIB=8 |
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lm_eval \ |
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--model vllm-vlm \ |
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--model_args pretrained="RedHatAI/Llama-4-Scout-17B-16E-Instruct-FP8-dynamic",dtype=auto,add_bos_token=False,max_model_len=1000000,tensor_parallel_size=8,gpu_memory_utilization=0.9,enable_chunked_prefill=True,trust_remote_code=True,max_images=10 \ |
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--tasks chartqa \ |
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--apply_chat_template \ |
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--batch_size auto |
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``` |
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</details> |
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|
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### Accuracy |
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|
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| | Recovery (%) | meta-llama/Llama-4-Scout-17B-16E-Instruct | RedHatAI/Llama-4-Scout-17B-16E-Instruct-FP8-dynamic<br>(this model) | |
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| ---------------------------------------------- | :-----------: | :---------------------------------------: | :-----------------------------------------------------------------: | |
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| ARC-Challenge<br>25-shot | 100.36 | 69.37 | 69.62 | |
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| GSM8k<br>5-shot | 99.24 | 90.45 | 89.76 | |
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| HellaSwag<br>10-shot | 99.94 | 85.23 | 85.18 | |
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| MMLU<br>5-shot | 99.94 | 80.54 | 80.49 | |
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| TruthfulQA<br>0-shot | 99.17 | 61.41 | 60.90 | |
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| WinoGrande<br>5-shot | 98.88 | 77.90 | 77.03 | |
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| **OpenLLM v1<br>Average Score** | **99.59** | **77.48** | **77.16** | |
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| IFEval<br>0-shot<br>avg of inst and prompt acc | 100.91 | 86.90 | 87.69 | |
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| Big Bench Hard<br>3-shot | 99.82 | 65.13 | 65.01 | |
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| Math Lvl 5<br>4-shot | 98.82 | 57.78 | 57.10 | |
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| GPQA<br>0-shot | 100.53 | 31.88 | 32.05 | |
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| MuSR<br>0-shot | 102.18 | 42.20 | 43.12 | |
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| MMLU-Pro<br>5-shot | 99.82 | 55.70 | 55.60 | |
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| **OpenLLM v2<br>Average Score** | **100.28** | **56.60** | **56.76** | |
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| RULER<br>seqlen = 131072<br>niah_multikey_1 | 101.36 | 88.20 | 89.40 | |
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| RULER<br>seqlen = 131072<br>niah_multikey_2 | 100.72 | 83.60 | 84.20 | |
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| RULER<br>seqlen = 131072<br>niah_multikey_3 | 96.19 | 78.80 | 75.80 | |
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| RULER<br>seqlen = 131072<br>niah_multiquery | 100.79 | 95.40 | 96.15 | |
|
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| RULER<br>seqlen = 131072<br>niah_multivalue | 97.22 | 73.75 | 71.70 | |
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| RULER<br>seqlen = 131072<br>niah_single_1 | 100.00 | 100.00 | 100.00 | |
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| RULER<br>seqlen = 131072<br>niah_single_2 | 100.00 | 99.80 | 99.80 | |
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| RULER<br>seqlen = 131072<br>niah_single_3 | 100.00 | 99.80 | 99.80 | |
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| RULER<br>seqlen = 131072<br>ruler_cwe | 96.19 | 39.42 | 37.92 | |
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| RULER<br>seqlen = 131072<br>ruler_fwe | 98.86 | 92.93 | 91.87 | |
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| RULER<br>seqlen = 131072<br>ruler_qa_hotpot | 100.00 | 48.20 | 48.20 | |
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| RULER<br>seqlen = 131072<br>ruler_qa_squad | 98.81 | 53.57 | 52.93 | |
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| RULER<br>seqlen = 131072<br>ruler_qa_vt | 100.35 | 92.28 | 92.60 | |
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| **RULER<br>seqlen = 131072<br>Average Score** | **99.49** | **80.44** | **80.03** | |
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| MMMU<br>0-shot | 97.92 | 53.44 | 52.33 | |
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| ChartQA<br>0-shot<br>exact_match | 100.12 | 65.88 | 65.96 | |
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| ChartQA<br>0-shot<br>relaxed_accuracy | 99.69 | 88.92 | 88.64 | |
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| **Multimodal Average Score** | **99.38** | **69.41** | **68.98** | |
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