Qwen is the large language model and large multimodal model series of the Qwen Team, Alibaba Group. Both language models and multimodal models are pretrained on large-scale multilingual and multimodal data and post-trained on quality data for aligning to human preferences. Qwen is capable of natural language understanding, text generation, vision understanding, audio understanding, tool use, role play, playing as AI agent, etc.
Qwen3-Coder model maintains impressive performance and efficiency, featuring the following key enhancements:
Significant Performance among open models on Agentic Coding, Agentic Browser-Use, and other foundational coding tasks.
Long-context Capabilities with native support for 256K tokens, extendable up to 1M tokens using Yarn, optimized for repository-scale understanding.
Expertise in agent capabilities, enabling precise integration with external tools in both thinking and unthinking modes and achieving leading performance among open-source models in complex agent-based tasks.
pip install -U -q keras-hub
pip install -U -q keras
Jax, TensorFlow, and Torch come preinstalled in Kaggle Notebooks. For instructions on installing them in another environment see the Keras Getting Started page.
Available Qwen 3 Coder Presets
The following model checkpoints are provided by the Keras team. Full code examples for each are available below.
Preset
Parameters
Description
qwen3_coder_instruct_30b_a3b_en
30B
Code-Specific Model, Mixture-of-Experts (MoE) model has 30.5B billion total parameters, with 3.3B billion activated, built on 48 layers, and utilizes 32 query and 4 key/value attention heads with 128 experts (8 active).
Example Usage
Python
12import keras
3import keras_hub
4import numpy as np
56# Use generate() for code generation.
7qwen_lm = keras_hub.models.QwenMoeCausalLM.from_preset("qwen3_coder_instruct_30b_a3b_en")
8qwen_lm.generate(" write a quick sort algorithm in python.", max_length=512)
910
Example Usage with Hugging Face URI
Python
12import keras
3import keras_hub
4import numpy as np
56# Use generate() for code generation.
7qwen_lm = keras_hub.models.QwenMoeCausalLM.from_preset("hf://keras/qwen3_coder_instruct_30b_a3b_en")
8qwen_lm.generate(" write a quick sort algorithm in python.", max_length=512)
910
