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 75%.
Only the weights and activations of the linear operators within transformers blocks are quantized using LLM Compressor.
Deployment
Use with vLLM
- Initialize vLLM server:
vllm serve RedHatAI/Mistral-Small-3.2-24B-Instruct-2506-NVFP4 --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-3.2-24B-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
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