f3692f2dc2
Model: RedHatAI/Mistral-Small-24B-Instruct-2501-quantized.w4a16 Source: Original Platform
435 lines
15 KiB
Markdown
435 lines
15 KiB
Markdown
---
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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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- vLLM 0.8.4
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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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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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