Qwen3

Qwen3 is Alibaba Cloud’s third-generation dense language model series, featuring improved reasoning, instruction following, and multilingual capabilities over Qwen2.


Task	Text Generation
Architecture	`Qwen3ForCausalLM`
Parameters	0.6B – 32B
HF Org	Qwen

Available Models

Qwen3: 0.6B, 1.7B, 4B, 8B, 14B, 32B

Architecture

Qwen3ForCausalLM

Example HF Models

Model	HF ID
Qwen3 0.6B	`Qwen/Qwen3-0.6B`
Qwen3 8B	`Qwen/Qwen3-8B`
Qwen3 32B	`Qwen/Qwen3-32B`

Example Recipes

Recipe	Description
qwen3_0p6b_hellaswag.yaml	SFT — Qwen3 0.6B on HellaSwag
qwen3_8b_squad_spark.yaml	SFT — Qwen3 8B on SQuAD (Spark)

Try with NeMo AutoModel

1. Install (full instructions):

$ pip install nemo-automodel

2. Clone the repo to get the example recipes:

$ git clone https://github.com/NVIDIA-NeMo/Automodel.git
$ cd Automodel

3. Run the recipe from inside the repo:

$ automodel --nproc-per-node=8 examples/llm_finetune/qwen/qwen3_0p6b_hellaswag.yaml

Run with Docker

1. Pull the container and mount a checkpoint directory:

$ docker run --gpus all -it --rm \
>   --shm-size=8g \
>   -v $(pwd)/checkpoints:/opt/Automodel/checkpoints \
>   nvcr.io/nvidia/nemo-automodel:26.06.00

2. Navigate to the AutoModel directory (where the recipes are):

$ cd /opt/Automodel

3. Run the recipe:

$ automodel --nproc-per-node=8 examples/llm_finetune/qwen/qwen3_0p6b_hellaswag.yaml

See the Installation Guide and LLM Fine-Tuning Guide.

Fine-Tuning

See the LLM Fine-Tuning Guide for full SFT and LoRA instructions.