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Model: RedHatAI/Mistral-Small-24B-Instruct-2501-quantized.w8a8 Source: Original Platform
2026-06-03 19:44:14 +08:00
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+---
+language:
+- en
+- fr
+- de
+- es
+- it
+- pt
+- zh
+- ja
+- ru
+- ko
+base_model:
+- mistralai/Mistral-Small-24B-Instruct-2501
+pipeline_tag: text-generation
+tags:
+- mistral
+- mistral-small
+- quantized
+- W8A8
+- vllm
+- conversational
+- text-generation-inference
+- compressed-tensors
+license: apache-2.0
+license_name: apache-2.0
+name: RedHatAI/Mistral-Small-24B-Instruct-2501-quantized.w8a8
+description: This model was obtained by quantizing the weights and activations of Mistral-Small-24B-Instruct-2501 to INT8 data type.
+readme: https://huggingface.co/RedHatAI/Mistral-Small-24B-Instruct-2501-quantized.w8a8/main/README.md
+tasks:
+- text-to-text
+provider: Red Hat
+license_link: https://www.apache.org/licenses/LICENSE-2.0
+validated_on:
+  - RHOAI 2.20
+  - RHAIIS 3.0
+  - RHELAI 1.5
+---
+ 
+<h1 style="display: flex; align-items: center; gap: 10px; margin: 0;">
+  Mistral-Small-24B-Instruct-2501-quantized.w8a8
+  <img src="https://www.redhat.com/rhdc/managed-files/Catalog-Validated_model_0.png" alt="Model Icon" width="40" style="margin: 0; padding: 0;" />
+</h1>
+  
+<a href="https://www.redhat.com/en/products/ai/validated-models" target="_blank" style="margin: 0; padding: 0;">
+<img src="https://www.redhat.com/rhdc/managed-files/Validated_badge-Dark.png" alt="Validated Badge" width="250" style="margin: 0; padding: 0;" />
+</a>
+
+## Model Overview
+- **Model Architecture:** Mistral3ForConditionalGeneration
+  - **Input:** Text / Image
+  - **Output:** Text
+- **Model Optimizations:**
+  - **Activation quantization:** INT8
+  - **Weight quantization:** INT8
+- **Intended Use Cases:** It is ideal for:
+  - Fast-response conversational agents.
+  - Low-latency function calling.
+  - Subject matter experts via fine-tuning.
+  - Local inference for hobbyists and organizations handling sensitive data.
+  - Programming and math reasoning.
+  - Long document understanding.
+  - Visual understanding.
+- **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages not officially supported by the model.
+- **Release Date:** 03/03/2025
+- **Version:** 1.0
+- **Validated on:** RHOAI 2.20, RHAIIS 3.0, RHELAI 1.5
+- **Model Developers:** Red Hat (Neural Magic)
+
+
+### Model Optimizations
+
+This model was obtained by quantizing activations and weights of [Mistral-Small-24B-Instruct-2501](https://huggingface.co/mistralai/Mistral-Small-24B-Instruct-2501) to INT8 data type.
+This optimization reduces the number of bits used to represent weights and activations from 16 to 8, reducing GPU memory requirements (by approximately 50%) and increasing matrix-multiply compute throughput (by approximately 2x).
+Weight quantization also reduces disk size requirements by approximately 50%.
+
+Only weights and activations of the linear operators within transformers blocks are quantized.
+Weights are quantized with a symmetric static per-channel scheme, whereas activations are quantized with a symmetric dynamic per-token scheme.
+A combination of the [SmoothQuant](https://arxiv.org/abs/2211.10438) and [GPTQ](https://arxiv.org/abs/2210.17323) algorithms is applied for quantization, as implemented in the [llm-compressor](https://github.com/vllm-project/llm-compressor) library.
+
+
+## Deployment
+
+1. Initialize vLLM server:
+```
+vllm serve RedHatAI/Mistral-Small-24B-Instruct-2501-quantized.w8a8 --tensor_parallel_size 1 --tokenizer_mode mistral
+```
+
+2. Send requests to the server:
+
+```python
+from openai import OpenAI
+
+# Modify OpenAI's API key and API base to use vLLM's API server.
+openai_api_key = "EMPTY"
+openai_api_base = "http://<your-server-host>:8000/v1"
+
+client = OpenAI(
+    api_key=openai_api_key,
+    base_url=openai_api_base,
+)
+
+model = "RedHatAI/Mistral-Small-24B-Instruct-2501-quantized.w8a8"
+
+
+messages = [
+    {"role": "user", "content": "Explain quantum mechanics clearly and concisely."},
+]
+
+outputs = client.chat.completions.create(
+    model=model,
+    messages=messages,
+)
+
+generated_text = outputs.choices[0].message.content
+print(generated_text)
+```
+
+<details>
+  <summary>Deploy on <strong>Red Hat AI Inference Server</strong></summary>
+  
+```bash
+podman run --rm -it --device nvidia.com/gpu=all -p 8000:8000 \
+ --ipc=host \
+--env "HUGGING_FACE_HUB_TOKEN=$HF_TOKEN" \
+--env "HF_HUB_OFFLINE=0" -v ~/.cache/vllm:/home/vllm/.cache \
+--name=vllm \
+registry.access.redhat.com/rhaiis/rh-vllm-cuda \
+vllm serve \
+--tensor-parallel-size 8 \
+--max-model-len 32768  \
+--enforce-eager --model RedHatAI/Mistral-Small-24B-Instruct-2501-quantized.w8a8
+```
+See [Red Hat AI Inference Server documentation](https://docs.redhat.com/en/documentation/red_hat_ai_inference_server/) for more details.
+</details>
+
+<details>
+  <summary>Deploy on <strong>Red Hat Enterprise Linux AI</strong></summary>
+  
+```bash
+# Download model from Red Hat Registry via docker
+# Note: This downloads the model to ~/.cache/instructlab/models unless --model-dir is specified.
+ilab model download --repository docker://registry.redhat.io/rhelai1/mistral-small-24b-instruct-2501-quantized-w8a8:1.5
+```
+
+```bash
+# Serve model via ilab
+ilab model serve --model-path ~/.cache/instructlab/models/mistral-small-24b-instruct-2501-quantized-w8a8
+
+# Chat with model
+ilab model chat --model ~/.cache/instructlab/models/mistral-small-24b-instruct-2501-quantized-w8a8
+```
+See [Red Hat Enterprise Linux AI documentation](https://docs.redhat.com/en/documentation/red_hat_enterprise_linux_ai/1.4) for more details.
+</details>
+
+<details>
+  <summary>Deploy on <strong>Red Hat Openshift AI</strong></summary>
+  
+```python
+# Setting up vllm server with ServingRuntime
+# Save as: vllm-servingruntime.yaml
+apiVersion: serving.kserve.io/v1alpha1
+kind: ServingRuntime
+metadata:
+ name: vllm-cuda-runtime # OPTIONAL CHANGE: set a unique name
+ annotations:
+   openshift.io/display-name: vLLM NVIDIA GPU ServingRuntime for KServe
+   opendatahub.io/recommended-accelerators: '["nvidia.com/gpu"]'
+ labels:
+   opendatahub.io/dashboard: 'true'
+spec:
+ annotations:
+   prometheus.io/port: '8080'
+   prometheus.io/path: '/metrics'
+ multiModel: false
+ supportedModelFormats:
+   - autoSelect: true
+     name: vLLM
+ containers:
+   - name: kserve-container
+     image: quay.io/modh/vllm:rhoai-2.20-cuda # CHANGE if needed. If AMD: quay.io/modh/vllm:rhoai-2.20-rocm
+     command:
+       - python
+       - -m
+       - vllm.entrypoints.openai.api_server
+     args:
+       - "--port=8080"
+       - "--model=/mnt/models"
+       - "--served-model-name={{.Name}}"
+     env:
+       - name: HF_HOME
+         value: /tmp/hf_home
+     ports:
+       - containerPort: 8080
+         protocol: TCP
+```
+
+```python
+# Attach model to vllm server. This is an NVIDIA template
+# Save as: inferenceservice.yaml
+apiVersion: serving.kserve.io/v1beta1
+kind: InferenceService
+metadata:
+  annotations:
+    openshift.io/display-name: mistral-small-24b-instruct-2501-quantized-w8a8 # OPTIONAL CHANGE
+    serving.kserve.io/deploymentMode: RawDeployment
+  name: mistral-small-24b-instruct-2501-quantized-w8a8         # specify model name. This value will be used to invoke the model in the payload
+  labels:
+    opendatahub.io/dashboard: 'true'
+spec:
+  predictor:
+    maxReplicas: 1
+    minReplicas: 1
+    model:
+      modelFormat:
+        name: vLLM
+      name: ''
+      resources:
+        limits:
+          cpu: '2'			# this is model specific
+          memory: 8Gi		# this is model specific
+          nvidia.com/gpu: '1'	# this is accelerator specific
+        requests:			# same comment for this block
+          cpu: '1'
+          memory: 4Gi
+          nvidia.com/gpu: '1'
+      runtime: vllm-cuda-runtime	# must match the ServingRuntime name above
+      storageUri: oci://registry.redhat.io/rhelai1/modelcar-mistral-small-24b-instruct-2501-quantized-w8a8:1.5
+    tolerations:
+    - effect: NoSchedule
+      key: nvidia.com/gpu
+      operator: Exists
+```
+
+```bash
+# make sure first to be in the project where you want to deploy the model
+# oc project <project-name>
+
+# apply both resources to run model
+
+# Apply the ServingRuntime
+oc apply -f vllm-servingruntime.yaml
+
+# Apply the InferenceService
+oc apply -f qwen-inferenceservice.yaml
+```
+
+```python
+# Replace <inference-service-name> and <cluster-ingress-domain> below:
+# - Run `oc get inferenceservice` to find your URL if unsure.
+
+# Call the server using curl:
+curl https://<inference-service-name>-predictor-default.<domain>/v1/chat/completions
+        -H "Content-Type: application/json" \
+        -d '{
+    "model": "mistral-small-24b-instruct-2501-quantized-w8a8",
+    "stream": true,
+    "stream_options": {
+        "include_usage": true
+    },
+    "max_tokens": 1,
+    "messages": [
+        {
+            "role": "user",
+            "content": "How can a bee fly when its wings are so small?"
+        }
+    ]
+}'
+
+```
+
+See [Red Hat Openshift AI documentation](https://docs.redhat.com/en/documentation/red_hat_openshift_ai/2025) for more details.
+</details>
+
+## Creation
+
+<details>
+  <summary>Creation details</summary>
+  This model was created with [llm-compressor](https://github.com/vllm-project/llm-compressor) by running the code snippet below. 
+
+
+  ```python
+  from transformers import AutoTokenizer, AutoModelForCausalLM
+  from llmcompressor.modifiers.quantization import GPTQModifier
+  from llmcompressor.modifiers.smoothquant import SmoothQuantModifier
+  from llmcompressor.transformers import oneshot
+  from datasets import load_dataset
+  
+  # Load model
+  model_stub = "mistralai/Mistral-Small-24B-Instruct-2501"
+  model_name = model_stub.split("/")[-1]
+  
+  num_samples = 1024
+  max_seq_len = 8192
+  
+  tokenizer = AutoTokenizer.from_pretrained(model_stub)
+  
+  model = AutoModelForCausalLM.from_pretrained(
+      model_stub,
+      device_map="auto",
+      torch_dtype="auto",
+  )
+
+  # Data processing
+  def preprocess_text(example):
+      text = tokenizer.apply_chat_template(example["messages"], tokenize=False, add_generation_prompt=False)
+      return tokenizer(text, padding=False, max_length=max_seq_len, truncation=True)
+  
+  ds = load_dataset("neuralmagic/calibration", name="LLM", split="train").select(range(num_samples))
+  ds = ds.map(preprocess_text, remove_columns=ds.column_names)
+
+  # Configure the quantization algorithm and scheme
+  recipe = [
+      SmoothQuantModifier(
+        smoothing_strength=0.9,
+        mappings=[
+            [["re:.*q_proj", "re:.*k_proj", "re:.*v_proj"], "re:.*input_layernorm"],
+            [["re:.*gate_proj", "re:.*up_proj"], "re:.*post_attention_layernorm"],
+            [["re:.*down_proj"], "re:.*up_proj"],
+        ],
+      ),
+      GPTQModifier(
+          ignore=["lm_head"],
+          sequential_targets=["MistralDecoderLayer"],
+          dampening_frac=0.1,
+          targets="Linear",
+          scheme="W8A8",
+      ),
+  ]
+
+  # Apply quantization
+  oneshot(
+      model=model,
+      dataset=ds,
+      recipe=recipe,
+      max_seq_length=max_seq_len,
+      num_calibration_samples=num_samples
+  )
+  
+  # Save to disk in compressed-tensors format
+  save_path = model_name + "-quantized.w8a8"
+  model.save_pretrained(save_path)
+  processor.save_pretrained(save_path)
+  print(f"Model and tokenizer saved to: {save_path}")
+  ```
+</details>
+ 
+
+
+## Evaluation
+
+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:
+
+OpenLLM Leaderboard V1:
+```
+lm_eval \
+  --model vllm \
+  --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 \
+  --tasks openllm \
+  --write_out \
+  --batch_size auto \
+  --output_path output_dir \
+  --show_config
+```
+
+OpenLLM Leaderboard V2:
+```
+lm_eval \
+  --model vllm \
+  --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 \
+  --apply_chat_template \
+  --fewshot_as_multiturn \
+  --tasks leaderboard \
+  --write_out \
+  --batch_size auto \
+  --output_path output_dir \
+  --show_config
+
+```
+
+### Accuracy
+
+#### OpenLLM Leaderboard V1 evaluation scores
+
+| Metric                                   | mistralai/Mistral-Small-24B-Instruct-2501             | nm-testing/Mistral-Small-24B-Instruct-2501-quantized.w8a8 |
+|-----------------------------------------|:---------------------------------:|:-------------------------------------------:|
+| ARC-Challenge (Acc-Norm, 25-shot)       |  72.18                            |   68.86                                  |
+| GSM8K (Strict-Match, 5-shot)            |  90.14                            |   90.00                                      |
+| HellaSwag (Acc-Norm, 10-shot)           |  85.05                            |   85.06                                     |
+| MMLU (Acc, 5-shot)                      |  80.69                            |   80.25                                     |
+| TruthfulQA (MC2, 0-shot)                |  65.55                            |   65.69                                     |
+| Winogrande (Acc, 5-shot)                |  83.11                            |   81.69                                     |
+| **Average Score**                       | **79.45**                        | **78.59**                                   |
+| **Recovery (%)**                            | **100.00**                       | **98.92**                                   |