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)
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.
# 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.
# 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
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.
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