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---
library_name: vllm
language:
- ar
- de
- en
- es
- fr
- hi
- id
- it
- pt
- th
- tl
- vi
base_model:
- meta-llama/Llama-4-Scout-17B-16E-Instruct
pipeline_tag: image-text-to-text
tags:
- facebook
- meta
- pytorch
- llama
- llama4
- neuralmagic
- redhat
- llmcompressor
- quantized
- W4A16
- INT4
license: other
license_name: llama4
---

<h1 style="display: flex; align-items: center; gap: 10px; margin: 0;">
  Llama-4-Scout-17B-16E-Instruct-quantized.w4a16
  <img src="https://huggingface.co/RedHatAI/Llama-4-Scout-17B-16E-Instruct-FP8-dynamic/resolve/main/assets/Catalog-Validated_model.png" alt="Model Icon" width="40" style="margin: 0; padding: 0;" />
</h1>
  
<a href="https://www.redhat.com/en/products/ai/validated-models" target="_blank" style="margin: 0; padding: 0;">
<img src="https://huggingface.co/RedHatAI/Llama-4-Scout-17B-16E-Instruct-FP8-dynamic/resolve/main/assets/Validated_badge-Dark.png" alt="Validated Badge" width="250" style="margin: 0; padding: 0;" />
</a>

## Model Overview
- **Model Architecture:** Llama4ForConditionalGeneration
  - **Input:** Text / Image
  - **Output:** Text
- **Model Optimizations:**
  - **Activation quantization:** None
  - **Weight quantization:** INT4
- **Release Date:** 04/25/2025
- **Version:** 1.0
- **Model Developers:** Red Hat (Neural Magic)


### Model Optimizations

This model was obtained by quantizing weights of [Llama-4-Scout-17B-16E-Instruct](https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E-Instruct) to INT4 data type.
This optimization reduces the number of bits used to represent weights from 16 to 4, reducing GPU memory requirements by approximately 75%.
Weight quantization also reduces disk size requirements by approximately 75%. The [llm-compressor](https://github.com/vllm-project/llm-compressor) library is used for quantization.


## Deployment

This model can be deployed efficiently on vLLM, Red Hat Enterprise Linux AI, and Openshift AI, as shown in the example below.

Deploy on <strong>vLLM</strong>

```python
from vllm import LLM, SamplingParams
from transformers import AutoTokenizer

model_id = "RedHatAI/Llama-4-Scout-17B-16E-Instruct-quantized.w4a16"
number_gpus = 4

sampling_params = SamplingParams(temperature=0.7, top_p=0.8, max_tokens=256)

tokenizer = AutoTokenizer.from_pretrained(model_id)

prompt = "Give me a short introduction to large language model."

llm = LLM(model=model_id, tensor_parallel_size=number_gpus)

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](https://docs.vllm.ai/en/latest/) for more details.

<details>
  <summary>Deploy on <strong>Red Hat AI Inference Server</strong></summary>
  
```bash
$ 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/Llama-4-Scout-17B-16E-Instruct-quantized.w4a16
```
</details>

<details>
  <summary>Deploy on <strong>Red Hat Enterprise Linux AI</strong></summary>
  
```bash
# Download model from Red Hat Registry via docker
# Note: This downloads the model to ~/.cache/instructlab/models unless --model-dir is specified.
ilab model download --repository docker://registry.redhat.io/rhelai1/llama-4-scout-17b-16e-instruct-quantized-w4a16:1.5
```

```bash
# Serve model via ilab
ilab model serve --model-path ~/.cache/instructlab/models/llama-4-scout-17b-16e-instruct-quantized-w4a16
  
# Chat with model
ilab model chat --model ~/.cache/instructlab/models/llama-4-scout-17b-16e-instruct-quantized-w4a16
```
See [Red Hat Enterprise Linux AI documentation](https://docs.redhat.com/en/documentation/red_hat_enterprise_linux_ai/1.4) for more details.
</details>

<details>
  <summary>Deploy on <strong>Red Hat Openshift AI</strong></summary>
  
```python
# 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-2.20-cuda # CHANGE if needed. If AMD: quay.io/modh/vllm:rhoai-2.20-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
```

```python
# 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: Llama-4-Scout-17B-16E-Instruct-quantized.w4a16 # OPTIONAL CHANGE
    serving.kserve.io/deploymentMode: RawDeployment
  name: Llama-4-Scout-17B-16E-Instruct-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-llama-4-scout-17b-16e-instruct-quantized-w4a16:1.5
    tolerations:
    - effect: NoSchedule
      key: nvidia.com/gpu
      operator: Exists
```

```bash
# 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
```

```python
# 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": $(model-name),
    "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](https://docs.redhat.com/en/documentation/red_hat_openshift_ai/2025) for more details.
</details>



## Evaluation

The model was evaluated on the OpenLLM leaderboard tasks (v1 and v2), long context RULER, multimodal MMMU, and multimodal ChartQA.
All evaluations are obtained through [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness).

<details>
  <summary>Evaluation details</summary>

  **OpenLLM v1**
  ```
  lm_eval \
    --model vllm \
    --model_args pretrained="RedHatAI/Llama-4-Scout-17B-16E-Instruct-quantized.w4a16",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 \
    --tasks openllm \
    --batch_size auto 
  ```

  **OpenLLM v2**
  ```
  lm_eval \
    --model vllm \
    --model_args pretrained="RedHatAI/Llama-4-Scout-17B-16E-Instruct-quantized.w4a16",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 \
    --tasks leaderboard \
    --apply_chat_template \
    --fewshot_as_multiturn \
    --batch_size auto 
  ```

  **Long Context RULER**
  ```
  lm_eval \
    --model vllm \
    --model_args pretrained="RedHatAI/Llama-4-Scout-17B-16E-Instruct-quantized.w4a16",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 \
    --tasks ruler \
    --metadata='{"max_seq_lengths":[131072]}' \
    --batch_size auto 
  ```

  **Multimodal MMMU**
  ```
  lm_eval \
    --model vllm-vlm \
    --model_args pretrained="RedHatAI/Llama-4-Scout-17B-16E-Instruct-quantized.w4a16",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 \
    --tasks mmmu_val \
    --apply_chat_template \
    --batch_size auto 
  ```

  **Multimodal ChartQA**
  ```
  export VLLM_MM_INPUT_CACHE_GIB=8
  lm_eval \
    --model vllm-vlm \
    --model_args pretrained="RedHatAI/Llama-4-Scout-17B-16E-Instruct-quantized.w4a16",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 \
    --tasks chartqa \
    --apply_chat_template \
    --batch_size auto 
  ```

</details>

### Accuracy

|                                                | Recovery (%) | meta-llama/Llama-4-Scout-17B-16E-Instruct | RedHatAI/Llama-4-Scout-17B-16E-Instruct-quantized.w4a16<br>(this model) |
| ---------------------------------------------- | :-----------: | :---------------------------------------: | :-----------------------------------------------------------------: |
| ARC-Challenge<br>25-shot                       | 98.51       | 69.37                                     | 68.34                                                               |
| GSM8k<br>5-shot                                | 100.4        | 90.45                                     | 90.90
| HellaSwag<br>10-shot                           | 99.67        | 85.23                                     | 84.95                                                               |
| MMLU<br>5-shot                                 | 99.75        | 80.54                                     | 80.34                                                               |
| TruthfulQA<br>0-shot                           | 99.82        | 61.41                                     | 61.30                                                               |
| WinoGrande<br>5-shot                           | 98.98        | 77.90                                     | 77.11                                                               |
| **OpenLLM v1<br>Average Score**                    | **99.59**        | **77.48**                                     | **77.16**                                                               |
| IFEval<br>0-shot<br>avg of inst and prompt acc | 99.51       | 86.90                                     | 86.47                                                               |
| Big Bench Hard<br>3-shot                       | 99.46        | 65.13                                     | 64.78                                                               |
| Math Lvl 5<br>4-shot                           | 99.22        | 57.78                                     | 57.33                                                               |
| GPQA<br>0-shot                                 | 100.0       | 31.88                                     | 31.88                                                               |
| MuSR<br>0-shot                                 | 100.9       | 42.20                                     | 42.59                                                               |
| MMLU-Pro<br>5-shot                             | 98.67        | 55.70                                     | 54.96                                                               |
| **OpenLLM v2<br>Average Score**                    | **99.54**       | **56.60**                                     | **56.34**                                                               |                                                            |
| MMMU<br>0-shot                                 | 100.6        | 53.44                                     | 53.78                                                               |
| ChartQA<br>0-shot<br>exact_match               | 100.1       | 65.88                                     | 66.00                                                               |
| ChartQA<br>0-shot<br>relaxed_accuracy          | 99.55        | 88.92                                     | 88.52                                                               |
| **Multimodal Average Score**                       | **100.0**        | **69.41**                                     | **69.43**                                                               |
| RULER<br>seqlen = 131072<br>niah_multikey_1    | 98.41       | 88.20                                     | 86.80                                                               |
| RULER<br>seqlen = 131072<br>niah_multikey_2    | 94.73       | 83.60                                     | 79.20                                                               |
| RULER<br>seqlen = 131072<br>niah_multikey_3    | 96.44        | 78.80                                     | 76.00                                                               |
| RULER<br>seqlen = 131072<br>niah_multiquery    | 98.79       | 95.40                                     | 94.25                                                               |
| RULER<br>seqlen = 131072<br>niah_multivalue    | 101.6        | 73.75                                     | 74.95                                                               |
| RULER<br>seqlen = 131072<br>niah_single_1      | 100.0       | 100.00                                    | 100.0                                                              |
| RULER<br>seqlen = 131072<br>niah_single_2      | 100.0       | 99.80                                     | 99.80                                                               |
| RULER<br>seqlen = 131072<br>niah_single_3      | 100.2       | 99.80                                     | 100.0                                                               |
| RULER<br>seqlen = 131072<br>ruler_cwe          | 87.39        | 39.42                                     | 33.14                                                               |
| RULER<br>seqlen = 131072<br>ruler_fwe          | 98.13        | 92.93                                     | 91.20                                                               |
| RULER<br>seqlen = 131072<br>ruler_qa_hotpot    | 100.4       | 48.20                                     | 48.40                                                               |
| RULER<br>seqlen = 131072<br>ruler_qa_squad     | 96.22        | 53.57                                     | 51.55                                                               |
| RULER<br>seqlen = 131072<br>ruler_qa_vt        | 98.82       | 92.28                                     | 91.20                                                               |
| **RULER<br>seqlen = 131072<br>Average Score**      | **98.16**        | **80.44**                                     | **78.96**                                                               |