---
tags:
- fp4
- vllm
language:
- en
- de
- fr
- it
- pt
- hi
- es
- th
pipeline_tag: text-generation
license: apache-2.0
base_model: Qwen/Qwen3-14B
---
# Qwen3-14B-NVFP4
## Model Overview
- **Model Architecture:** Qwen/Qwen3-14B
- **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 [Qwen/Qwen3-14B](https://huggingface.co/Qwen/Qwen3-14B).
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 [Qwen/Qwen3-14B](https://huggingface.co/Qwen/Qwen3-14B) 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](https://github.com/vllm-project/llm-compressor).
## Deployment
### Use with vLLM
This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below.
```python
from vllm import LLM, SamplingParams
from transformers import AutoTokenizer
model_id = "RedHatAI/Qwen3-14B-NVFP4"
number_gpus = 1
sampling_params = SamplingParams(temperature=0.6, top_p=0.9, max_tokens=256)
tokenizer = AutoTokenizer.from_pretrained(model_id)
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
prompts = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
llm = LLM(model=model_id, tensor_parallel_size=number_gpus)
outputs = llm.generate(prompts, sampling_params)
generated_text = outputs[0].outputs[0].text
print(generated_text)
```
vLLM aslo supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details.
## Creation
This model was created by applying [LLM Compressor with calibration samples from UltraChat](https://github.com/vllm-project/llm-compressor/blob/main/examples/quantization_w4a4_fp4/llama3_example.py), as presented in the code snipet below.
```python
from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer
from llmcompressor import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier
from llmcompressor.utils import dispatch_for_generation
MODEL_ID = "Qwen/Qwen3-14B"
# 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
recipe = [
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](https://github.com/neuralmagic/lm-evaluation-harness). The Reasoning evals were done using [ligheval](https://github.com/neuralmagic/lighteval).
### Accuracy
| Category |
Metric |
Qwen3-14B |
Qwen3-14B-NVFP4 (this model) |
Recovery |
| OpenLLM V1 |
arc_challenge |
67.32 |
67.06 |
99.61 |
| gsm8k |
88.70 |
88.25 |
99.49 |
| hellaswag |
79.62 |
78.24 |
98.27 |
| mmlu |
78.86 |
77.23 |
97.93 |
| truthfulqa_mc2 |
58.59 |
58.49 |
99.83 |
| winogrande |
73.72 |
73.80 |
100.11 |
| Average |
74.47 |
73.85 |
99.16 |
| OpenLLM V2 |
BBH (3-shot) |
59.45 |
56.78 |
95.51 |
| MMLU-Pro (5-shot) |
44.39 |
41.15 |
92.70 |
| MuSR (0-shot) |
38.62 |
37.83 |
97.95 |
| IFEval (0-shot) |
89.45 |
90.41 |
101.07 |
| GPQA (0-shot) |
27.43 |
26.59 |
96.94 |
| Math-|v|-5 (4-shot) |
57.33 |
53.40 |
93.14 |
| Average |
52.78 |
51.03 |
96.68 |
| Coding |
HumanEval_64 pass@2 |
90.74 |
89.87 |
99.04 |
| Reasoning |
AIME24 (0-shot) |
75.86 |
65.52 |
86.34 |
| AIME25 (0-shot) |
68.97 |
65.52 |
95.00 |
| GPQA (Diamond, 0-shot) |
64.97 |
60.40 |
93.00 |
| Average |
69.93 |
63.81 |
91.45 |
### Reproduction
The results were obtained using the following commands:
#### OpenLLM v1
```
lm_eval \
--model vllm \
--model_args pretrained="RedHatAI/Qwen3-14B-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/Qwen3-14B-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/Qwen3-14B-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
```
#### LightEval
```
# --- model_args.yaml ---
cat > model_args.yaml <<'YAML'
model_parameters:
model_name: "RedHatAI/Qwen3-14B-NVFP4"
dtype: auto
gpu_memory_utilization: 0.9
tensor_parallel_size: 2
max_model_length: 40960
generation_parameters:
seed: 42
temperature: 0.6
top_k: 20
top_p: 0.95
min_p: 0.0
max_new_tokens: 32768
YAML
lighteval vllm model_args.yaml \
"lighteval|aime24|0,lighteval|aime25|0,lighteval|gpqa:diamond|0" \
--max-samples -1 \
--output-dir out_dir
```