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--- |
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language: |
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- en |
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- fr |
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- de |
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- es |
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- it |
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- pt |
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- zh |
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- ja |
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- ru |
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- ko |
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base_model: |
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- mistralai/Mistral-Small-24B-Instruct-2501 |
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pipeline_tag: text-generation |
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tags: |
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- mistral |
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- mistral-small |
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- w4a16 |
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- int4 |
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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/Mistral-Small-24B-Instruct-2501-quantized.w4a16 |
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description: This model was obtained by quantizing the weights of Mistral-Small-24B-Instruct-2501 to INT4 data type. |
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readme: https://huggingface.co/RedHatAI/Mistral-Small-24B-Instruct-2501-quantized.w4a16/main/README.md |
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tasks: |
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- text-to-text |
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provider: Mistral AI |
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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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--- |
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<h1 style="display: flex; align-items: center; gap: 10px; margin: 0;"> |
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Mistral-Small-24B-Instruct-2501-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:** Mistral-Small-24B-Instruct-2501 |
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- **Input:** Text |
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- **Output:** Text |
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- **Model Optimizations:** |
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- **Weight quantization:** INT4 |
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- **Activation quantization:** None |
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- **Release Date:** 3/1/2025 |
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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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- **Model Developers:** Neural Magic |
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Quantized version of [Mistral-Small-24B-Instruct-2501](https://huggingface.co/mistralai/Mistral-Small-24B-Instruct-2501). |
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### Model Optimizations |
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This model was obtained by quantizing the weights to INT4 data type, ready for inference with vLLM. |
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This optimization reduces the number of bits per parameter from 16 to 4, reducing the disk size and GPU memory requirements by approximately 75%. Only the weights of the linear operators within transformers blocks are quantized. |
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## Deployment |
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### Use with vLLM |
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1. Initialize vLLM server: |
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``` |
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vllm serve RedHatAI/Mistral-Small-24B-Instruct-2501-quantized.w4a16 --tensor_parallel_size 1 --tokenizer_mode mistral |
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``` |
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2. Send requests to the server: |
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```python |
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from openai import OpenAI |
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# Modify OpenAI's API key and API base to use vLLM's API server. |
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openai_api_key = "EMPTY" |
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openai_api_base = "http://<your-server-host>:8000/v1" |
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client = OpenAI( |
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api_key=openai_api_key, |
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base_url=openai_api_base, |
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) |
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model = "RedHatAI/Mistral-Small-24B-Instruct-2501-quantized.w4a16" |
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messages = [ |
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{"role": "user", "content": "Explain quantum mechanics clearly and concisely."}, |
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] |
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outputs = client.chat.completions.create( |
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model=model, |
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messages=messages, |
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) |
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generated_text = outputs.choices[0].message.content |
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print(generated_text) |
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``` |
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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/Mistral-Small-24B-Instruct-2501-quantized.w4a16 |
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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/mistral-small-24b-instruct-2501-quantized-w4a16:1.5 |
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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/mistral-small-24b-instruct-2501-quantized-w4a16 --gpu 1 -- --trust-remote-code |
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# Chat with model |
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ilab model chat --model ~/.cache/instructlab/models/mistral-small-24b-instruct-2501-quantized-w4a16 |
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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: Mistral-Small-24B-Instruct-2501-quantized.w4a16 # OPTIONAL CHANGE |
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serving.kserve.io/deploymentMode: RawDeployment |
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name: Mistral-Small-24B-Instruct-2501-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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args: |
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- "--trust-remote-code" |
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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-mistral-small-24b-instruct-2501-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": "Mistral-Small-24B-Instruct-2501-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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This model was created with [llm-compressor](https://github.com/vllm-project/llm-compressor) by running the code snippet below. |
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```bash |
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python quantize.py --model_path mistralai/Mistral-Small-24B-Instruct-2501 --quant_path "output_dir" --calib_size 1024 --dampening_frac 0.05 --observer minmax --actorder false |
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``` |
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```python |
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from datasets import load_dataset |
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from transformers import AutoTokenizer |
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from llmcompressor.modifiers.quantization import GPTQModifier |
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from llmcompressor.transformers import SparseAutoModelForCausalLM, oneshot, apply |
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import argparse |
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from compressed_tensors.quantization import QuantizationScheme, QuantizationArgs, QuantizationType, QuantizationStrategy |
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def parse_actorder(value): |
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# Interpret the input value for --actorder |
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if value.lower() == "false": |
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return False |
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elif value.lower() == "group": |
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return "group" |
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elif value.lower() == "weight": |
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return "weight" |
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else: |
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raise argparse.ArgumentTypeError("Invalid value for --actorder. Use 'group' or 'False'.") |
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parser = argparse.ArgumentParser() |
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parser.add_argument('--model_path', type=str) |
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parser.add_argument('--quant_path', type=str) |
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parser.add_argument('--num_bits', type=int, default=4) |
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parser.add_argument('--sequential_update', type=bool, default=True) |
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parser.add_argument('--calib_size', type=int, default=256) |
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parser.add_argument('--dampening_frac', type=float, default=0.05) |
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parser.add_argument('--observer', type=str, default="minmax") |
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parser.add_argument( |
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'--actorder', |
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type=parse_actorder, |
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default=False, # Default value is False |
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help="Specify actorder as 'group' (string) or False (boolean)." |
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) |
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args = parser.parse_args() |
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model = SparseAutoModelForCausalLM.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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use_cache=False, |
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) |
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tokenizer = AutoTokenizer.from_pretrained(args.model_path) |
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NUM_CALIBRATION_SAMPLES = args.calib_size |
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DATASET_ID = "garage-bAInd/Open-Platypus" |
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DATASET_SPLIT = "train" |
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ds = load_dataset(DATASET_ID, split=DATASET_SPLIT) |
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ds = ds.shuffle(seed=42).select(range(NUM_CALIBRATION_SAMPLES)) |
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def preprocess(example): |
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concat_txt = example["instruction"] + "\n" + example["output"] |
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return {"text": concat_txt} |
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ds = ds.map(preprocess) |
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def tokenize(sample): |
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return tokenizer( |
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sample["text"], |
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padding=False, |
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truncation=False, |
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add_special_tokens=True, |
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) |
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ds = ds.map(tokenize, remove_columns=ds.column_names) |
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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=args.num_bits, |
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type=QuantizationType.INT, |
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symmetric=True, |
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group_size=128, |
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strategy=QuantizationStrategy.GROUP, |
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observer=args.observer, |
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actorder=args.actorder |
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), |
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input_activations=None, |
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output_activations=None, |
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) |
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recipe = [ |
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GPTQModifier( |
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targets=["Linear"], |
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ignore=["lm_head"], |
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sequential_update=args.sequential_update, |
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dampening_frac=args.dampening_frac, |
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config_groups={"group_0": quant_scheme}, |
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) |
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] |
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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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num_calibration_samples=args.calib_size, |
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) |
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# Save to disk compressed. |
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SAVE_DIR = args.quant_path |
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model.save_pretrained(SAVE_DIR, save_compressed=True) |
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tokenizer.save_pretrained(SAVE_DIR) |
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``` |
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## Evaluation |
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The model was evaluated on OpenLLM Leaderboard [V1](https://huggingface.co/spaces/open-llm-leaderboard-old/open_llm_leaderboard) and [V2](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/), using the following commands: |
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OpenLLM Leaderboard V1: |
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``` |
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lm_eval \ |
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--model vllm \ |
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--model_args pretrained="neuralmagic/Mistral-Small-24B-Instruct-2501-quantized.w4a16",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1,gpu_memory_utilization=0.8,enable_chunked_prefill=True,trust_remote_code=True \ |
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--tasks openllm \ |
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--write_out \ |
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--batch_size auto \ |
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--output_path output_dir \ |
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--show_config |
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``` |
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OpenLLM Leaderboard V2: |
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``` |
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lm_eval \ |
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--model vllm \ |
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--model_args pretrained="neuralmagic/Mistral-Small-24B-Instruct-2501-quantized.w4a16",dtype=auto,add_bos_token=False,max_model_len=4096,tensor_parallel_size=1,gpu_memory_utilization=0.8,enable_chunked_prefill=True,trust_remote_code=True \ |
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--apply_chat_template \ |
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--fewshot_as_multiturn \ |
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--tasks leaderboard \ |
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--write_out \ |
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--batch_size auto \ |
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--output_path output_dir \ |
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--show_config |
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``` |
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### Accuracy |
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#### OpenLLM Leaderboard V1 evaluation scores |
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| Metric | mistralai/Mistral-Small-24B-Instruct-2501 | neuralmagic/Mistral-Small-24B-Instruct-2501-quantized.w4a16 | |
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|-----------------------------------------|:---------------------------------:|:-------------------------------------------:| |
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| ARC-Challenge (Acc-Norm, 25-shot) | 72.18 | 71.16 | |
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| GSM8K (Strict-Match, 5-shot) | 90.14 | 89.69 | |
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| HellaSwag (Acc-Norm, 10-shot) | 85.05 | 84.43 | |
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| MMLU (Acc, 5-shot) | 80.69 | 80.00 | |
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| TruthfulQA (MC2, 0-shot) | 65.55 | 63.92 | |
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| Winogrande (Acc, 5-shot) | 83.11 | 82.24 | |
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| **Average Score** | **79.45** | **78.57** | |
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| **Recovery (%)** | **100.00** | **98.9** | |
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#### OpenLLM Leaderboard V2 evaluation scores |
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| Metric | mistralai/Mistral-Small-24B-Instruct-2501 | neuralmagic/Mistral-Small-24B-Instruct-2501-quantized.w4a16 | |
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|---------------------------------------------------------|:---------------------------------:|:-------------------------------------------:| |
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| IFEval (Inst-and-Prompt Level Strict Acc, 0-shot) | 73.27 | 74.37 | |
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| BBH (Acc-Norm, 3-shot) | 45.18 | 45.15 | |
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| MMLU-Pro (Acc, 5-shot) | 38.83 | 36.00 | |
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| **Average Score** | **52.42** | **51.84** | |
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| **Recovery (%)** | **100.00** | **98.89** | |
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| GPQA (Acc-Norm, 0-shot) | 8.29 | 6.81 | |
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| MUSR (Acc-Norm, 0-shot) | 7.84 | 9.46 | |
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|
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Results on GPQA and MUSR are not considred for accuracy recovery calculation because the unquantized model has close to random prediction accuracy (8.29, 7.84) which doesn't provide a reliable baseline for recovery calculation. |
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|
