Qwen2

Qwen2 is Alibaba Cloud’s second-generation large language model series. It features grouped query attention, YARN-based long-context extension, and dual chunk attention for long sequences. QwQ-32B-Preview, a reasoning-focused model, also uses this architecture.


Task	Text Generation
Architecture	`Qwen2ForCausalLM`
Parameters	0.5B – 72B
HF Org	Qwen

Available Models

Qwen2.5: 0.5B, 1.5B, 3B, 7B, 14B, 32B, 72B
Qwen2: 0.5B, 1.5B, 7B, 57B-A14B (MoE), 72B
QwQ-32B-Preview — reasoning model

Architecture

Qwen2ForCausalLM

Example HF Models

Model	HF ID
Qwen2.5 7B Instruct	`Qwen/Qwen2.5-7B-Instruct`
Qwen2.5 72B Instruct	`Qwen/Qwen2.5-72B-Instruct`
Qwen2 7B Instruct	`Qwen/Qwen2-7B-Instruct`
QwQ 32B Preview	`Qwen/QwQ-32B-Preview`

Example Recipes

Recipe	Description
qwen2_5_7b_squad.yaml	SFT — Qwen2.5 7B on SQuAD
qwq_32b_squad_peft.yaml	LoRA — QwQ 32B on SQuAD

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/qwen2_5_7b_squad.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/qwen2_5_7b_squad.yaml

See the Installation Guide and LLM Fine-Tuning Guide.

Fine-Tuning

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