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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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- fp8 |
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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-FP8-dynamic |
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description: FP8-Quantized variant of Mistral-Small-24B-Instruct-2501. |
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readme: https://huggingface.co/RedHatAI/Mistral-Small-24B-Instruct-2501-FP8-dynamic/main/README.md |
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tasks: |
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- text-to-text |
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provider: Red Hat |
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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-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:** 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:** FP8 |
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- **Activation quantization:** FP8 |
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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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It achieves an average score of 78.88 on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) benchmark (version 1), whereas the unquantized model achieves 79.45. |
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### Model Optimizations |
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This model was obtained by quantizing the weights and activations to FP8 data type, ready for inference with vLLM. |
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This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%. Only the weights and activations 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-FP8-dynamic --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-FP8-dynamic" |
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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-FP8-dynamic |
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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-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/mistral-small-24b-instruct-2501-fp8-dynamic --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-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: mistral-small-24b-instruct-2501-fp8-dynamic # OPTIONAL CHANGE |
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serving.kserve.io/deploymentMode: RawDeployment |
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name: mistral-small-24b-instruct-2501-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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args: |
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- "--tokenizer-mode=mistral" |
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- "--config-format=mistral" |
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- "--load-format=mistral" |
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- "--tool-call-parser=mistral" |
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- "--enable-auto-tool-choice" |
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- "--limit-mm-per-prompt=image=10" |
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- "--max-model-len=16384" |
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- "--uvicorn-log-level=debug" |
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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-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": "mistral-small-24b-instruct-2501-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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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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import argparse |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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from llmcompressor.modifiers.quantization import QuantizationModifier |
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from llmcompressor.transformers import oneshot |
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import os |
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def main(): |
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parser = argparse.ArgumentParser(description='Quantize a transformer model to FP8') |
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parser.add_argument('--model_id', type=str, required=True, |
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help='The model ID from HuggingFace (e.g., "meta-llama/Meta-Llama-3-8B-Instruct")') |
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parser.add_argument('--save_path', type=str, default='.', |
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help='Custom path to save the quantized model. If not provided, will use model_name-FP8-dynamic') |
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args = parser.parse_args() |
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# Load model |
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model = AutoModelForCausalLM.from_pretrained( |
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args.model_id, device_map="auto", torch_dtype="auto", trust_remote_code=True, |
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) |
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tokenizer = AutoTokenizer.from_pretrained(args.model_id) |
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# Configure the quantization algorithm and scheme |
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recipe = QuantizationModifier( |
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targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"] |
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) |
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# Apply quantization |
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oneshot(model=model, recipe=recipe) |
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save_path = os.path.join(args.save_path, args.model_id.split("/")[1] + "-FP8-dynamic") |
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os.makedirs(save_path, exist_ok=True) |
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# Save to disk in compressed-tensors format |
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model.save_pretrained(save_path) |
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tokenizer.save_pretrained(save_path) |
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print(f"Model and tokenizer saved to: {save_path}") |
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if __name__ == "__main__": |
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main() |
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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-FP8-Dynamic",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-FP8-Dynamic",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 | nm-testing/Mistral-Small-24B-Instruct-2501-FP8-dynamic | |
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|-----------------------------------------|:---------------------------------:|:-------------------------------------------:| |
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| ARC-Challenge (Acc-Norm, 25-shot) | 72.18 | 71.76 | |
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| GSM8K (Strict-Match, 5-shot) | 90.14 | 89.01 | |
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| HellaSwag (Acc-Norm, 10-shot) | 85.05 | 84.65 | |
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| MMLU (Acc, 5-shot) | 80.69 | 80.55 | |
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| TruthfulQA (MC2, 0-shot) | 65.55 | 64.85 | |
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| Winogrande (Acc, 5-shot) | 83.11 | 82.48 | |
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| **Average Score** | **79.45** | **78.88** | |
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| **Recovery (%)** | **100.00** | **99.28** | |
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#### OpenLLM Leaderboard V2 evaluation scores |
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| Metric | mistralai/Mistral-Small-24B-Instruct-2501 | nm-testing/Mistral-Small-24B-Instruct-2501-FP8-dynamic | |
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|---------------------------------------------------------|:---------------------------------:|:-------------------------------------------:| |
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| IFEval (Inst-and-Prompt Level Strict Acc, 0-shot) | 73.27 | 73.53 | |
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| BBH (Acc-Norm, 3-shot) | 45.18 | 44.39 | |
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| MMLU-Pro (Acc, 5-shot) | 38.83 | 37.28 | |
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| **Average Score** | **52.42** | **51.73** | |
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| **Recovery (%)** | **100.00** | **98.68** | |
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| Math-Hard (Exact-Match, 4-shot) | 6.35 | 2.99 | |
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| GPQA (Acc-Norm, 0-shot) | 8.29 | 6.97 | |
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| MUSR (Acc-Norm, 0-shot) | 7.84 | 8.04 | |
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Results on Math-Hard, GPQA, and MUSR are not considred for accuracy recovery calculation because the unquantized model has close to random prediction accuracy (6.35, 8.29, 7.84) which doesn't provide a reliable baseline for recovery calculation. |
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