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Model: RedHatAI/Qwen3-30B-A3B-Instruct-2507-quantized.w8a8
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---
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
base_model:
- Qwen/Qwen3-30B-A3B-Instruct-2507
tags:
- neuralmagic
- redhat
- llmcompressor
- quantized
- INT8
---
# Qwen3-30B-A3B-Instruct-2507.w8a8
## Model Overview
- **Model Architecture:** Qwen3ForCausalLM
- **Input:** Text
- **Output:** Text
- **Model Optimizations:**
- **Weight quantization:** INT8
- **Intended Use Cases:**
- Reasoning.
- Function calling.
- Subject matter experts via fine-tuning.
- Multilingual instruction following.
- Translation.
- **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws).
- **Release Date:** 05/05/2025
- **Version:** 1.0
- **Model Developers:** RedHat (Neural Magic)
### Model Optimizations
This model was obtained by quantizing the weights of [Qwen/Qwen3-30B-A3B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507) to INT8 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.
A combination of the [SmoothQuant](https://arxiv.org/abs/2211.10438) and [GPTQ](https://arxiv.org/abs/2210.17323) algorithms is applied for quantization, as implemented in the [llm-compressor](https://github.com/vllm-project/llm-compressor) library.
## Deployment
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/Qwen3-30B-A3B-Instruct-2507.w8a8"
number_gpus = 1
sampling_params = SamplingParams(temperature=0.6, top_p=0.95, top_k=20, min_p=0, max_tokens=256)
messages = [
{"role": "user", "content": prompt}
]
tokenizer = AutoTokenizer.from_pretrained(model_id)
messages = [{"role": "user", "content": "Give me a short introduction to large language model."}]
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
<details>
<summary>Creation details</summary>
This model was created with [llm-compressor](https://github.com/vllm-project/llm-compressor) by running the code snippet below.
```python
from llmcompressor.modifiers.quantization import GPTQModifier
from llmcompressor.modifiers.smoothquant import SmoothQuantModifier
from llmcompressor.transformers import oneshot
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load model
model_stub = "Qwen/Qwen3-30B-A3B-Instruct"
model_name = model_stub.split("/")[-1]
num_samples = 1024
max_seq_len = 8192
model = AutoModelForCausalLM.from_pretrained(model_stub)
tokenizer = AutoTokenizer.from_pretrained(model_stub)
def preprocess_fn(example):
return {"text": tokenizer.apply_chat_template(example["messages"], add_generation_prompt=False, tokenize=False)}
ds = load_dataset("neuralmagic/LLM_compression_calibration", split="train")
ds = ds.map(preprocess_fn)
# Configure the quantization algorithm and scheme
recipe = [
SmoothQuantModifier(
smoothing_strength=0.9,
mappings=[
[["re:.*q_proj", "re:.*k_proj", "re:.*v_proj"], "re:.*input_layernorm"],
],
),
GPTQModifier(
ignore: ["lm_head"]
config_groups={"group_0": {"targets": ["Linear"], "weights": { "num_bits": 4, "type": int, "strategy": "group", "group_size": 128, "symmetric": true, "dynamic": false, "observer": "mse" } } },
dampening_frac=0.1,
)
]
# Apply quantization
oneshot(
model=model,
dataset=ds,
recipe=recipe,
max_seq_length=max_seq_len,
num_calibration_samples=num_samples,
)
# Save to disk in compressed-tensors format
save_path = model_name + "-quantized.w8a8"
model.save_pretrained(save_path)
tokenizer.save_pretrained(save_path)
print(f"Model and tokenizer saved to: {save_path}")
```
</details>
## Evaluation
The model was evaluated on the ifeval, mmlu_pro and gsm8k_platinum using [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness), on reasoning tasks using [lighteval](https://github.com/neuralmagic/lighteval/tree/reasoning).
[vLLM](https://docs.vllm.ai/en/stable/) was used for all evaluations.
<details>
<summary>Evaluation details</summary>
Deploy using vllm to create an OpenAI-compatible API endpoint:
- vLLM:
```shell
vllm serve RedHatAI/Qwen3-30B-A3B-Instruct-2507.w8a8 --max-model-len 262144
```
**lm-evaluation-harness**
```
lm_eval --model local-chat-completions \
--tasks mmlu_pro_chat \
--model_args "model=RedHatAI/Qwen3-30B-A3B-Instruct-2507.w8a8,max_length=262144,base_url=http://0.0.0.0:8000/v1/chat/completions,num_concurrent=64,max_retries=3,tokenized_requests=False,tokenizer_backend=None,timeout=1200" \
--num_fewshot 0 \
--apply_chat_template \
--gen_kwargs "do_sample=True,temperature=0.6,top_p=0.95,top_k=20,min_p=0.0,max_gen_toks=64000
```
```
lm_eval --model local-chat-completions \
--tasks ifeval \
--model_args "model=RedHatAI/Qwen3-30B-A3B-Instruct-2507.w8a8,max_length=262144,base_url=http://0.0.0.0:8000/v1/chat/completions,num_concurrent=64,max_retries=3,tokenized_requests=False,tokenizer_backend=None,timeout=1200" \
--num_fewshot 0 \
--apply_chat_template \
--gen_kwargs "do_sample=True,temperature=0.6,top_p=0.95,top_k=20,min_p=0.0,max_gen_toks=64000
```
```
lm_eval --model local-chat-completions \
--tasks mmlu_cot_llama \
--model_args "model=RedHatAI/Qwen3-30B-A3B-Instruct-2507.w8a8,max_length=262144,base_url=http://0.0.0.0:8000/v1/chat/completions,num_concurrent=64,max_retries=3,tokenized_requests=False,tokenizer_backend=None,timeout=1200" \
--num_fewshot 0 \
--apply_chat_template \
--gen_kwargs "do_sample=True,temperature=0.6,top_p=0.95,top_k=20,min_p=0.0,max_gen_toks=64000
```
```
lm_eval --model local-chat-completions \
--tasks gsm8k_platinum_cot_llama \
--model_args "model=RedHatAI/Qwen3-30B-A3B-Instruct-2507.w8a8,max_length=262144,base_url=http://0.0.0.0:8000/v1/chat/completions,num_concurrent=64,max_retries=3,tokenized_requests=False,tokenizer_backend=None,timeout=1200" \
--num_fewshot 0 \
--apply_chat_template \
--gen_kwargs "do_sample=True,temperature=0.6,top_p=0.95,top_k=20,min_p=0.0,max_gen_toks=64000
```
**lighteval**
lighteval_model_arguments.yaml
```yaml
model_parameters:
model_name: RedHatAI/Qwen3-30B-A3B-Instruct-2507.w8a8
dtype: auto
gpu_memory_utilization: 0.9
max_model_length: 40960
generation_parameters:
temperature: 0.6
top_k: 20
min_p: 0.0
top_p: 0.95
max_new_tokens: 32000
```
```
lighteval endpoint litellm lighteval_model_arguments.yaml \
"aime25|0,math_500|0,gpqa:diamond|0"
```
</details>
### Accuracy
| Benchmark | Qwen3-30B-A3B Instruct | Qwen3-30B-A3B Instruct.w8a8 (this model) | Recovery (%) |
|--------|-------------|-------------------|--------------|
| GSM8k Platinum (5-shot) | 96.11 | 97.57 | 101.52 |
| MMLU-Cot (5-shot) | 84.29 | 84.30 | 100.02 |
| MMLU-Pro (5-shot) | 78.90 | 78.81 | 99.89 |
| IfEval | 89.13 | 88.89 | 99.73 |
| Math 500 | 89.91 | 90.48 | 100.62 |