Mistral-Small-3.1-24B-Instruct-2503-FP8-dynamic

Model Overview

Model Architecture: Mistral3ForConditionalGeneration
- Input: Text / Image
- Output: Text
Model Optimizations:
- Activation quantization: FP8
- Weight quantization: FP8
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: 04/15/2025
Version: 1.0
Validated on: RHOAI 2.20, RHAIIS 3.0, RHELAI 1.5
Model Developers: RedHat (Neural Magic)

Model Optimizations

This model was obtained by quantizing activations and weights of Mistral-Small-3.1-24B-Instruct-2503 to FP8 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. The llm-compressor library is used for quantization.

Deployment

Initialize vLLM server:

vllm serve RedHatAI/Mistral-Small-3.1-24B-Instruct-2503-quantized.w4a16 --tensor_parallel_size 1 --tokenizer_mode mistral

Send requests to the server:

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-3.1-24B-Instruct-2503-quantized.w4a16"


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)

Deploy on Red Hat AI Inference Server

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 1 \
--max-model-len 32768  \
--enforce-eager --model RedHatAI/Mistral-Small-3.1-24B-Instruct-2503-FP8-dynamic

See Red Hat AI Inference Server documentation for more details.

Deploy on Red Hat Enterprise Linux AI

# 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-3-1-24b-instruct-2503-fp8-dynamic:1.5

# Serve model via ilab
ilab model serve --model-path ~/.cache/instructlab/models/mistral-small-3-1-24b-instruct-2503-fp8-dynamic
  
# Chat with model
ilab model chat --model ~/.cache/instructlab/models/mistral-small-3-1-24b-instruct-2503-fp8-dynamic

See Red Hat Enterprise Linux AI documentation for more details.

Deploy on Red Hat Openshift AI

# 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

# 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-3-1-24b-instruct-2503-fp8-dynamic # OPTIONAL CHANGE
    serving.kserve.io/deploymentMode: RawDeployment
  name: mistral-small-3-1-24b-instruct-2503-fp8-dynamic          # 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-3-1-24b-instruct-2503-fp8-dynamic:1.5
    tolerations:
    - effect: NoSchedule
      key: nvidia.com/gpu
      operator: Exists

# 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

# 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-3-1-24b-instruct-2503-fp8-dynamic",
    "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 for more details.

Creation

Creation details

This model was created with [llm-compressor](https://github.com/vllm-project/llm-compressor) by running the code snippet below.

from llmcompressor.modifiers.quantization import QuantizationModifier
from llmcompressor.transformers import oneshot
from transformers import AutoModelForImageTextToText, AutoProcessor

# Load model
model_stub = "mistralai/Mistral-Small-3.1-24B-Instruct-2503"
model_name = model_stub.split("/")[-1]

model = AutoModelForImageTextToText.from_pretrained(model_stub)

processor = AutoProcessor.from_pretrained(model_stub)

# Configure the quantization algorithm and scheme
recipe = QuantizationModifier(
    ignore=["language_model.lm_head", "re:vision_tower.*", "re:multi_modal_projector.*"],
    targets="Linear",
    scheme="FP8_dynamic",
)

# Apply quantization
oneshot(
    model=model,
    recipe=recipe,
)

# Save to disk in compressed-tensors format
save_path = model_name + "-FP8-dynamic"
model.save_pretrained(save_path)
processor.save_pretrained(save_path)
print(f"Model and tokenizer saved to: {save_path}")

Evaluation

The model was evaluated on the OpenLLM leaderboard tasks (version 1), MMLU-pro, GPQA, HumanEval and MBPP. Non-coding tasks were evaluated with lm-evaluation-harness, whereas coding tasks were evaluated with a fork of evalplus. vLLM is used as the engine in all cases.

Evaluation details

MMLU

lm_eval \
  --model vllm \
  --model_args pretrained="RedHatAI/Mistral-Small-3.1-24B-Instruct-2503-FP8-dynamic",dtype=auto,gpu_memory_utilization=0.5,max_model_len=8192,enable_chunk_prefill=True,tensor_parallel_size=2 \
  --tasks mmlu \
  --num_fewshot 5 \
  --apply_chat_template\
  --fewshot_as_multiturn \
  --batch_size auto

ARC Challenge

lm_eval \
  --model vllm \
  --model_args pretrained="RedHatAI/Mistral-Small-3.1-24B-Instruct-2503-FP8-dynamic",dtype=auto,gpu_memory_utilization=0.5,max_model_len=8192,enable_chunk_prefill=True,tensor_parallel_size=2 \
  --tasks arc_challenge \
  --num_fewshot 25 \
  --apply_chat_template\
  --fewshot_as_multiturn \
  --batch_size auto

GSM8k

lm_eval \
  --model vllm \
  --model_args pretrained="RedHatAI/Mistral-Small-3.1-24B-Instruct-2503-FP8-dynamic",dtype=auto,gpu_memory_utilization=0.9,max_model_len=8192,enable_chunk_prefill=True,tensor_parallel_size=2 \
  --tasks gsm8k \
  --num_fewshot 8 \
  --apply_chat_template\
  --fewshot_as_multiturn \
  --batch_size auto

Hellaswag

lm_eval \
  --model vllm \
  --model_args pretrained="RedHatAI/Mistral-Small-3.1-24B-Instruct-2503-FP8-dynamic",dtype=auto,gpu_memory_utilization=0.5,max_model_len=8192,enable_chunk_prefill=True,tensor_parallel_size=2 \
  --tasks hellaswag \
  --num_fewshot 10 \
  --apply_chat_template\
  --fewshot_as_multiturn \
  --batch_size auto

Winogrande

lm_eval \
  --model vllm \
  --model_args pretrained="RedHatAI/Mistral-Small-3.1-24B-Instruct-2503-FP8-dynamic",dtype=auto,gpu_memory_utilization=0.5,max_model_len=8192,enable_chunk_prefill=True,tensor_parallel_size=2 \
  --tasks winogrande \
  --num_fewshot 5 \
  --apply_chat_template\
  --fewshot_as_multiturn \
  --batch_size auto

TruthfulQA

lm_eval \
  --model vllm \
  --model_args pretrained="RedHatAI/Mistral-Small-3.1-24B-Instruct-2503-FP8-dynamic",dtype=auto,gpu_memory_utilization=0.5,max_model_len=8192,enable_chunk_prefill=True,tensor_parallel_size=2 \
  --tasks truthfulqa \
  --num_fewshot 0 \
  --apply_chat_template\
  --batch_size auto

MMLU-pro

lm_eval \
  --model vllm \
  --model_args pretrained="RedHatAI/Mistral-Small-3.1-24B-Instruct-2503-FP8-dynamic",dtype=auto,gpu_memory_utilization=0.5,max_model_len=8192,enable_chunk_prefill=True,tensor_parallel_size=2 \
  --tasks mmlu_pro \
  --num_fewshot 5 \
  --apply_chat_template\
  --fewshot_as_multiturn \
  --batch_size auto

Coding

The commands below can be used for mbpp by simply replacing the dataset name.

Generation

python3 codegen/generate.py \
  --model RedHatAI/Mistral-Small-3.1-24B-Instruct-2503-FP8-dynamic \
  --bs 16 \
  --temperature 0.2 \
  --n_samples 50 \
  --root "." \
  --dataset humaneval

Sanitization

python3 evalplus/sanitize.py \
  humaneval/RedHatAI--Mistral-Small-3.1-24B-Instruct-2503-FP8-dynamic_vllm_temp_0.2

Evaluation

evalplus.evaluate \
  --dataset humaneval \
  --samples humaneval/RedHatAI--Mistral-Small-3.1-24B-Instruct-2503-FP8-dynamic_vllm_temp_0.2-sanitized

Accuracy

Category	Benchmark	Mistral-Small-3.1-24B-Instruct-2503	Mistral-Small-3.1-24B-Instruct-2503-FP8-dynamic (this model)	Recovery
OpenLLM v1	MMLU (5-shot)	80.67	80.71	100.1%
	ARC Challenge (25-shot)	72.78	72.87	100.1%
	GSM-8K (5-shot, strict-match)	58.68	49.96	85.1%
	Hellaswag (10-shot)	83.70	83.67	100.0%
	Winogrande (5-shot)	83.74	82.56	98.6%
	TruthfulQA (0-shot, mc2)	70.62	70.88	100.4%
	Average	75.03	73.49	97.9%
	MMLU-Pro (5-shot)	67.25	66.86	99.4%
	GPQA CoT main (5-shot)	42.63	41.07	99.4%
	GPQA CoT diamond (5-shot)	45.96	45.45	98.9%
Coding	HumanEval pass@1	84.70	84.70	100.0%
	HumanEval+ pass@1	79.50	79.30	99.8%
	MBPP pass@1	71.10	70.00	98.5%
	MBPP+ pass@1	60.60	59.50	98.2%