Mistral-Small-3.2-24B-Instruct-2506-NVFP4

Model Overview

• Model Architecture: unsloth/Mistral-Small-3.2-24B-Instruct-2506
  • Input: Text
  • Output: Text
• Model Optimizations:
  • Weight quantization: FP4
  • Activation quantization: FP4
• Out-of-scope: Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English.
• Release Date: 10/29/2025
• Version: 1.0
• Model Developers: RedHatAI

This model is a quantized version of unsloth/Mistral-Small-3.2-24B-Instruct-2506. It was evaluated on a several tasks to assess the its quality in comparison to the unquatized model.

Model Optimizations

This model was obtained by quantizing the weights and activations of unsloth/Mistral-Small-3.2-24B-Instruct-2506 to FP4 data type, ready for inference with vLLM>=0.9.1 This optimization reduces the number of bits per parameter from 16 to 4, reducing the disk size and GPU memory requirements by approximately 25%.

Only the weights and activations of the linear operators within transformers blocks are quantized using LLM Compressor.

Deployment

Use with vLLM

1. Initialize vLLM server:

vllm serve RedHatAI/Mistral-Small-3.2-24B-Instruct-2506-NVFP4 --tensor_parallel_size 1 --tokenizer_mode mistral

2. 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-3.2-24B-Instruct-2506-NVFP4"


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)

Creation

This model was created by applying LLM Compressor with calibration samples from UltraChat, as presented in the code snipet below.

from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer

from llmcompressor import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier
from llmcompressor.modifiers.smoothquant import SmoothQuantModifier
from llmcompressor.utils import dispatch_for_generation

MODEL_ID = "unsloth/Mistral-Small-3.2-24B-Instruct-2506"

# Load model.
model = AutoModelForCausalLM.from_pretrained(MODEL_ID, torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)

DATASET_ID = "HuggingFaceH4/ultrachat_200k"
DATASET_SPLIT = "train_sft"

# Select number of samples. 512 samples is a good place to start.
# Increasing the number of samples can improve accuracy.
NUM_CALIBRATION_SAMPLES = 512
MAX_SEQUENCE_LENGTH = 2048

# Load dataset and preprocess.
ds = load_dataset(DATASET_ID, split=f"{DATASET_SPLIT}[:{NUM_CALIBRATION_SAMPLES}]")
ds = ds.shuffle(seed=42)

def preprocess(example):
    return {
        "text": tokenizer.apply_chat_template(
            example["messages"],
            tokenize=False,
        )
    }

ds = ds.map(preprocess)

# Tokenize inputs.
def tokenize(sample):
    return tokenizer(
        sample["text"],
        padding=False,
        max_length=MAX_SEQUENCE_LENGTH,
        truncation=True,
        add_special_tokens=False,
    )

ds = ds.map(tokenize, remove_columns=ds.column_names)

# Configure the quantization algorithm and scheme.
# In this case, we:
#   * quantize the weights to fp4 with per group 16 via ptq
#   * calibrate a global_scale for activations, which will be used to
#       quantize activations to fp4 on the fly
smoothing_strength = 0.9
recipe = [
    SmoothQuantModifier(smoothing_strength=smoothing_strength),
    QuantizationModifier(
        ignore=["re:.*lm_head.*"],
        config_groups={
            "group_0": {
                "targets": ["Linear"],
                "weights": {
                    "num_bits": 4,
                    "type": "float",
                    "strategy": "tensor_group",
                    "group_size": 16,
                    "symmetric": True,
                    "observer": "mse",
                },
                "input_activations": {
                    "num_bits": 4,
                    "type": "float",
                    "strategy": "tensor_group",
                    "group_size": 16,
                    "symmetric": True,
                    "dynamic": "local",
                    "observer": "minmax",
                },
            }
        },
    )
]

# Save to disk in compressed-tensors format.
SAVE_DIR = MODEL_ID.rstrip("/").split("/")[-1] + "-NVFP4"

# Apply quantization.
oneshot(
    model=model,
    dataset=ds,
    recipe=recipe,
    max_seq_length=MAX_SEQUENCE_LENGTH,
    num_calibration_samples=NUM_CALIBRATION_SAMPLES,
    output_dir=SAVE_DIR,
)

print("\n\n")
print("========== SAMPLE GENERATION ==============")
dispatch_for_generation(model)
input_ids = tokenizer("Hello my name is", return_tensors="pt").input_ids.to("cuda")
output = model.generate(input_ids, max_new_tokens=100)
print(tokenizer.decode(output[0]))
print("==========================================\n\n")

model.save_pretrained(SAVE_DIR, save_compressed=True)
tokenizer.save_pretrained(SAVE_DIR)

Evaluation

This model was evaluated on the well-known OpenLLM v1, OpenLLM v2 and HumanEval_64 benchmarks using lm-evaluation-harness.

Accuracy

Category	Metric	unsloth/Mistral-Small-3.2-24B-Instruct-2506	RedHatAI/Mistral-Small-3.2-24B-Instruct-2506-NVFP4	Recovery
OpenLLM V1	arc_challenge	68.52	66.98	97.75
	gsm8k	89.61	87.11	97.21
	hellaswag	85.70	85.11	99.31
	mmlu	81.06	79.43	97.99
	truthfulqa_mc2	61.35	60.34	98.35
	winogrande	83.27	81.61	98.01
	Average	78.25	76.76	98.10
OpenLLM V2	BBH (3-shot)	65.86	64.05	97.25
	MMLU-Pro (5-shot)	50.84	48.45	95.30
	MuSR (0-shot)	39.15	40.21	102.71
	IFEval (0-shot)	84.05	84.41	100.43
	GPQA (0-shot)	33.14	32.55	98.22
	Math-|v|-5 (4-shot)	41.69	37.76	90.57
	Average	52.46	51.24	97.68
Coding	HumanEval_64 pass@2	88.88	88.84	99.95

Reproduction

The results were obtained using the following commands:

lm_eval \
  --model vllm \
  --model_args pretrained="RedHatAI/Mistral-Small-3.2-24B-Instruct-2506-NVFP4",dtype=auto,max_model_len=4096,tensor_parallel_size=2,enable_chunked_prefill=True,enforce_eager=True\
  --apply_chat_template \
  --fewshot_as_multiturn \
  --tasks openllm \
  --batch_size auto

OpenLLM v2

lm_eval \
  --model vllm \
  --model_args pretrained="RedHatAI/Mistral-Small-3.2-24B-Instruct-2506-NVFP4",dtype=auto,max_model_len=4096,tensor_parallel_size=2,enable_chunked_prefill=True,enforce_eager=True\
  --apply_chat_template \
  --fewshot_as_multiturn \
  --tasks leaderboard \
  --batch_size auto

HumanEval_64

lm_eval \
  --model vllm \
  --model_args pretrained="RedHatAI/Mistral-Small-3.2-24B-Instruct-2506-NVFP4",dtype=auto,max_model_len=4096,tensor_parallel_size=2,enable_chunked_prefill=True,enforce_eager=True\
  --apply_chat_template \
  --fewshot_as_multiturn \
  --tasks humaneval_64_instruct \
  --batch_size auto