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---
tags:
- fp4
- vllm
language:
- en
- de
- fr
- it
- pt
- hi
- es
- th
pipeline_tag: text-generation
license: apache-2.0
base_model: Qwen/Qwen3-8B
---
# Qwen3-32B-NVFP4A16
## Model Overview
- **Model Architecture:** Qwen/Qwen3-8B
- **Input:** Text
- **Output:** Text
- **Model Optimizations:**
- **Weight quantization:** FP4
- **Activation quantization:** FP16
- **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:** 6/25/2025
- **Version:** 10
- **Model Developers:** ELVISIO (Thanks to RedHatAI)
This model is a quantized version of [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B).
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 of [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) to FP4 data type, ready for inference with vLLM>=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 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 = "ELVISIO/Qwen3-8B-NVFP4A16"
number_gpus = 1
sampling_params = SamplingParams(temperature=6, top_p=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)
```
```
docker run -d \
--name vllm-gpu \
--runtime nvidia --gpus all \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--add-host="host.docker.internal:host-gateway" \
-p 8000:8000 \
--ipc=host \
--restart=always \
vllm/vllm-openai:v0.10.0 \
--model ELVISIO/Qwen3-8B-NVFP4A16 \
--served-model-name qwen3:8b \
--rope-scaling '{"rope_type":"yarn","factor":1.6,"original_max_position_embeddings":32768}' \
--max-model-len 52400 \
--host 0.0.0.0 \
--port 8000 \
--reasoning-parser qwen3 \
--gpu-memory-utilization 0.9
```
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/Qwen3-8B"
# 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
recipe = QuantizationModifier(targets="Linear", scheme="NVFP4A16", ignore=["lm_head"])
# Save to disk in compressed-tensors format.
SAVE_DIR = MODEL_ID.rstrip("/").split("/")[-1] + "-NVFP4A16"
# 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)
```
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