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cpatonn/
Qwen3-4B-Instruct-2507-AWQ-4bit

Name: Qwen3-4B-Instruct-2507-AWQ-4bit
Availability: InStock

Qwen/Qwen3-4B-Instruct-2507

quantized

apache-2.0

autotrain_compatible

text-generation-inference

endpoints_compatible

compressed-tensors

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Qwen3-4B-Instruct-2507-AWQ

Method

Quantised using vllm-project/llm-compressor, nvidia/Llama-Nemotron-Post-Training-Dataset and the following configs:

recipe = [
    AWQModifier(ignore=["lm_head"], scheme="W4A16_ASYM", targets=["Linear"]),
]

Qwen3-4B-Instruct-2507

Highlights

We introduce the updated version of the Qwen3-4B non-thinking mode, named Qwen3-4B-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

Qwen3-4B-Instruct-2507 has the following features:

Type: Causal Language Models
Training Stage: Pretraining & Post-training
Number of Parameters: 4.0B
Number of Paramaters (Non-Embedding): 3.6B
Number of Layers: 36
Number of Attention Heads (GQA): 32 for Q and 8 for KV
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

	GPT-4.1-nano-2025-04-14	Qwen3-30B-A3B Non-Thinking	Qwen3-4B Non-Thinking	Qwen3-4B-Instruct-2507
Knowledge
MMLU-Pro	62.8	69.1	58.0	69.6
MMLU-Redux	80.2	84.1	77.3	84.2
GPQA	50.3	54.8	41.7	62.0
SuperGPQA	32.2	42.2	32.0	42.8
Reasoning
AIME25	22.7	21.6	19.1	47.4
HMMT25	9.7	12.0	12.1	31.0
ZebraLogic	14.8	33.2	35.2	80.2
LiveBench 20241125	41.5	59.4	48.4	63.0
Coding
LiveCodeBench v6 (25.02-25.05)	31.5	29.0	26.4	35.1
MultiPL-E	76.3	74.6	66.6	76.8
Aider-Polyglot	9.8	24.4	13.8	12.9
Alignment
IFEval	74.5	83.7	81.2	83.4
Arena-Hard v2*	15.9	24.8	9.5	43.4
Creative Writing v3	72.7	68.1	53.6	83.5
WritingBench	66.9	72.2	68.5	83.4
Agent
BFCL-v3	53.0	58.6	57.6	61.9
TAU1-Retail	23.5	38.3	24.3	48.7
TAU1-Airline	14.0	18.0	16.0	32.0
TAU2-Retail	-	31.6	28.1	40.4
TAU2-Airline	-	18.0	12.0	24.0
TAU2-Telecom	-	18.4	17.5	13.2
Multilingualism
MultiIF	60.7	70.8	61.3	69.0
MMLU-ProX	56.2	65.1	49.6	61.6
INCLUDE	58.6	67.8	53.8	60.1
PolyMATH	15.6	23.3	16.6	31.1

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

Quickstart

The code of Qwen3 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'

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/Qwen3-4B-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/Qwen3-4B-Instruct-2507 --context-length 262144
vLLM:
vllm serve Qwen/Qwen3-4B-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.