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        🗜️ COMPRESSED & OPTIMIZED 🚀

Qwen3-30B-A3B-Instruct-2507 - W4A16 Quantized

W4A16 (4-bit weights, 16-bit activations) quantized version of Qwen/Qwen3-30B-A3B-Instruct-2507 using LLM-Compressor.

🗜️ Memory: ~75% reduction vs FP16
🚀 Speed: Faster inference on supported hardware
🔗 Original model: Qwen/Qwen3-30B-A3B-Instruct-2507

Click to view compression code

python

from datasets import load_dataset
from llmcompressor.modifiers.quantization import GPTQModifier
from llmcompressor import oneshot
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model with memory management
model_stub = "Qwen/Qwen3-30B-A3B-Instruct-2507"
model_name = model_stub.split("/")[-1]

# Use conservative parameters
num_samples = 1024
max_seq_len = 8192

print(f"Loading model: {model_stub}")
model = AutoModelForCausalLM.from_pretrained(
    model_stub,
    torch_dtype="auto",
    device_map="auto",
    low_cpu_mem_usage=True,
    max_memory={0: "44GB", "cpu": "55GB"},
)

print("Loading tokenizer...")
tokenizer = AutoTokenizer.from_pretrained(model_stub)

print("Loading calibration dataset...")
def preprocess_fn(example):
    return {"text": tokenizer.apply_chat_template(
        example["messages"], 
        add_generation_prompt=False, 
        tokenize=False
    )}

# Load dataset and preprocess
ds = load_dataset("neuralmagic/LLM_compression_calibration", split=f"train[:{num_samples}]")
ds = ds.map(preprocess_fn)
ds = ds.shuffle(seed=42)

# Tokenize the dataset
def tokenize(sample):
    return tokenizer(
        sample["text"],
        padding=False,
        max_length=max_seq_len,
        truncation=True,
        add_special_tokens=False,
    )

print("Tokenizing dataset...")
ds = ds.map(tokenize, remove_columns=ds.column_names)

# Configure GPTQ with proper Qwen3 MoE ignore patterns
print("Configuring quantization recipe...")
recipe = GPTQModifier(
    targets="Linear",
    scheme="W4A16", 
    ignore=["lm_head", "re:.*mlp.gate$"],  # Qwen3 MoE pattern (no shared experts)
    dampening_frac=0.01,
    # Remove sequential_targets - let llmcompressor handle automatically
)

# Apply quantization
print("Starting quantization process...")
oneshot(
    model=model,
    dataset=ds,
    recipe=recipe,
    max_seq_length=max_seq_len,
    num_calibration_samples=num_samples,
)

# Save quantized model
save_path = model_name + "-gptq-w4a16"
print(f"Saving model to: {save_path}")
model.save_pretrained(save_path, save_compressed=True)
tokenizer.save_pretrained(save_path)

print("Quantization completed successfully!")

📄 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.

python

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.

python

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:

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

ramblingpolymath/Qwen3-30B-A3B-Instruct-2507-W4A16

Qwen3-30B-A3B-Instruct-2507 - W4A16 Quantized

📄 Original Model README

Qwen3-30B-A3B-Instruct-2507

Highlights

Model Overview

Performance

Quickstart

Agentic Use

Best Practices

Citation

ramblingpolymath/
Qwen3-30B-A3B-Instruct-2507-W4A16