ramblingpolymath/Qwen3-30B-A3B-Instruct-2507-W8A8

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        🗜️ COMPRESSED & OPTIMIZED 🚀

Qwen3-30B-A3B-Instruct-2507 W8A8 Quantized

W8A8 (8-bit weights and activations) quantized version of Qwen/Qwen3-30B-A3B-Instruct-2507 using LLM-Compressor.

• 🗜️ Memory: ~50% reduction vs FP16
• 🚀 Speed: Faster inference on supported hardware
• 🔗 Original model: Qwen/Qwen3-30B-A3B-Instruct-2507
• 🏗️ Recommended architectures: Ampere and older

Click to view compression code

from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer

from llmcompressor.modifiers.quantization import GPTQModifier
from llmcompressor.modifiers.smoothquant import SmoothQuantModifier
from llmcompressor.transformers import oneshot
from llmcompressor.utils import dispatch_for_generation
from llmcompressor.modifiers.quantization import QuantizationModifier
# Select model and load it.
model_id = "Qwen/Qwen3-30B-A3B-Instruct-2507"
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
    low_cpu_mem_usage=True,
    offload_folder="./offload_tmp",  # Add offload directory
    max_memory={0: "22GB", 1: "22GB", "cpu": "64GB"},
)
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)

# Select calibration dataset.
DATASET_ID = "mit-han-lab/pile-val-backup"
DATASET_SPLIT = "validation"

# Select number of samples. 512 samples is a good place to start.
# Increasing the number of samples can improve accuracy.
NUM_CALIBRATION_SAMPLES = 256
MAX_SEQUENCE_LENGTH = 512

# 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(
            [{"role": "user", "content": example["text"]}],
            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 to run.
#   * quantize the activations to int8 (dynamic per token)
recipe = QuantizationModifier(targets="Linear", scheme="W8A8", ignore=["lm_head", "re:.*mlp.gate$"])

# Apply algorithms.
oneshot(
    model=model,
    dataset=ds,
    recipe=recipe,
    max_seq_length=MAX_SEQUENCE_LENGTH,
    num_calibration_samples=NUM_CALIBRATION_SAMPLES,
    output_dir="./Qwen3-30B-A3B-Instruct-2507-W8A8",  # Add this line
)


# Save to disk compressed.
SAVE_DIR = model_id.rstrip("/").split("/")[-1] + "-W8A8"
model.save_pretrained(SAVE_DIR, save_compressed=True)
tokenizer.save_pretrained(SAVE_DIR)

---

📄 Original Model README

Qwen3-30B-A3B-Instruct-2507

Highlights

We introduce the updated version of the Qwen3-30B-A3B non-thinking mode, named Qwen3-30B-A3B-Instruct-2507, featuring the following key enhancements:

• Significant improvements in general capabilities, including instruction following, logical reasoning, text comprehension, mathematics, science, coding and tool usage.
• Substantial gains in long-tail knowledge coverage across multiple languages.
• Markedly better alignment with user preferences in subjective and open-ended tasks, enabling more helpful responses and higher-quality text generation.
• Enhanced capabilities in 256K long-context understanding.

Model Overview

Qwen3-30B-A3B-Instruct-2507 has the following features:

• Type: Causal Language Models
• Training Stage: Pretraining & Post-training
• Number of Parameters: 30.5B in total and 3.3B activated
• Number of Paramaters (Non-Embedding): 29.9B
• Number of Layers: 48
• Number of Attention Heads (GQA): 32 for Q and 4 for KV
• Number of Experts: 128
• Number of Activated Experts: 8
• Context Length: 262,144 natively.

NOTE: This model supports only non-thinking mode and does not generate <think></think> blocks in its output. Meanwhile, specifying enable_thinking=False is no longer required.

For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our blog, GitHub, and Documentation.

Performance

	Deepseek-V3-0324	GPT-4o-0327	Gemini-2.5-Flash Non-Thinking	Qwen3-235B-A22B Non-Thinking	Qwen3-30B-A3B Non-Thinking	Qwen3-30B-A3B-Instruct-2507
Knowledge
MMLU-Pro	81.2	79.8	81.1	75.2	69.1	78.4
MMLU-Redux	90.4	91.3	90.6	89.2	84.1	89.3
GPQA	68.4	66.9	78.3	62.9	54.8	70.4
SuperGPQA	57.3	51.0	54.6	48.2	42.2	53.4
Reasoning
AIME25	46.6	26.7	61.6	24.7	21.6	61.3
HMMT25	27.5	7.9	45.8	10.0	12.0	43.0
ZebraLogic	83.4	52.6	57.9	37.7	33.2	90.0
LiveBench 20241125	66.9	63.7	69.1	62.5	59.4	69.0
Coding
LiveCodeBench v6 (25.02-25.05)	45.2	35.8	40.1	32.9	29.0	43.2
MultiPL-E	82.2	82.7	77.7	79.3	74.6	83.8
Aider-Polyglot	55.1	45.3	44.0	59.6	24.4	35.6
Alignment
IFEval	82.3	83.9	84.3	83.2	83.7	84.7
Arena-Hard v2*	45.6	61.9	58.3	52.0	24.8	69.0
Creative Writing v3	81.6	84.9	84.6	80.4	68.1	86.0
WritingBench	74.5	75.5	80.5	77.0	72.2	85.5
Agent
BFCL-v3	64.7	66.5	66.1	68.0	58.6	65.1
TAU1-Retail	49.6	60.3#	65.2	65.2	38.3	59.1
TAU1-Airline	32.0	42.8#	48.0	32.0	18.0	40.0
TAU2-Retail	71.1	66.7#	64.3	64.9	31.6	57.0
TAU2-Airline	36.0	42.0#	42.5	36.0	18.0	38.0
TAU2-Telecom	34.0	29.8#	16.9	24.6	18.4	12.3
Multilingualism
MultiIF	66.5	70.4	69.4	70.2	70.8	67.9
MMLU-ProX	75.8	76.2	78.3	73.2	65.1	72.0
INCLUDE	80.1	82.1	83.8	75.6	67.8	71.9
PolyMATH	32.2	25.5	41.9	27.0	23.3	43.1

*: For reproducibility, we report the win rates evaluated by GPT-4.1.

#: Results were generated using GPT-4o-20241120, as access to the native function calling API of GPT-4o-0327 was unavailable.

Quickstart

The code of Qwen3-MoE has been in the latest Hugging Face transformers and we advise you to use the latest version of transformers.

With transformers<4.51.0, you will encounter the following error:

KeyError: 'qwen3_moe'

The following contains a code snippet illustrating how to use the model generate content based on given inputs.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Qwen/Qwen3-30B-A3B-Instruct-2507"

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# prepare the model input
prompt = "Give me a short introduction to large language model."
messages = [
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=16384
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 

content = tokenizer.decode(output_ids, skip_special_tokens=True)

print("content:", content)

For deployment, you can use sglang>=0.4.6.post1 or vllm>=0.8.5 or to create an OpenAI-compatible API endpoint:

• SGLang:

  python -m sglang.launch_server --model-path Qwen/Qwen3-30B-A3B-Instruct-2507 --context-length 262144
  

• vLLM:

  vllm serve Qwen/Qwen3-30B-A3B-Instruct-2507 --max-model-len 262144
  

Note: If you encounter out-of-memory (OOM) issues, consider reducing the context length to a shorter value, such as 32,768.

For local use, applications such as Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers have also supported Qwen3.

Agentic Use

Qwen3 excels in tool calling capabilities. We recommend using Qwen-Agent to make the best use of agentic ability of Qwen3. Qwen-Agent encapsulates tool-calling templates and tool-calling parsers internally, greatly reducing coding complexity.

To define the available tools, you can use the MCP configuration file, use the integrated tool of Qwen-Agent, or integrate other tools by yourself.

from qwen_agent.agents import Assistant

# Define LLM
llm_cfg = {
    'model': 'Qwen3-30B-A3B-Instruct-2507',

    # Use a custom endpoint compatible with OpenAI API:
    'model_server': 'http://localhost:8000/v1',  # api_base
    'api_key': 'EMPTY',
}

# Define Tools
tools = [
    {'mcpServers': {  # You can specify the MCP configuration file
            'time': {
                'command': 'uvx',
                'args': ['mcp-server-time', '--local-timezone=Asia/Shanghai']
            },
            "fetch": {
                "command": "uvx",
                "args": ["mcp-server-fetch"]
            }
        }
    },
  'code_interpreter',  # Built-in tools
]

# Define Agent
bot = Assistant(llm=llm_cfg, function_list=tools)

# Streaming generation
messages = [{'role': 'user', 'content': 'https://qwenlm.github.io/blog/ Introduce the latest developments of Qwen'}]
for responses in bot.run(messages=messages):
    pass
print(responses)

Best Practices

To achieve optimal performance, we recommend the following settings:

1. Sampling Parameters:

   • We suggest using Temperature=0.7, TopP=0.8, TopK=20, and MinP=0.
   • For supported frameworks, you can adjust the presence_penalty parameter between 0 and 2 to reduce endless repetitions. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance.

2. Adequate Output Length: We recommend using an output length of 16,384 tokens for most queries, which is adequate for instruct models.

3. Standardize Output Format: We recommend using prompts to standardize model outputs when benchmarking.

   • Math Problems: Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt.
   • Multiple-Choice Questions: Add the following JSON structure to the prompt to standardize responses: "Please show your choice in the answer field with only the choice letter, e.g., "answer": "C"."

Citation

If you find our work helpful, feel free to give us a cite.

@misc{qwen3technicalreport,
      title={Qwen3 Technical Report}, 
      author={Qwen Team},
      year={2025},
      eprint={2505.09388},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2505.09388}, 
}